{"cop_marine": {"collections_config": {"ANTARCTIC_OMI_SI_extent": {"description": "**DEFINITION**\n\nEstimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:\ni) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values.\nThe Antarctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019).  \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n \n**CMEMS KEY FINDINGS**\n\nWith quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28*106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00186\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}, "keywords": ["antarctic-omi-si-extent", "coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-ice-extent", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00186", "title": "Antarctic Sea Ice Extent from Reanalysis"}, "ANTARCTIC_OMI_SI_extent_obs": {"description": "**DEFINITION**\n\nSea Ice Extent (SIE) is the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since the 1970s. SIE is often reported with units of millions square kilometers. SIE includes all sea ice, and does not include lake and river ice. The change in sea ice extent (trend) is expressed in millions of km squared per decade. In addition, trends are expressed relative to the 1979-2024 period in % per decade. These trends are calculated (i) for the annual mean values; (ii) for the winter maximum cover (September in the Southern Hemisphere); (iii) for the summer minimum extent (February in the Southern Hemisphere). The annual mean trend is reported on the key figure. The September and February trends are reported in the text below.  See also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019; Fox-Kemper et al. 2021).\n\n\n**CMEMS KEY FINDINGS**\n\nSince 1979, there has been an overall slight increase of sea ice extent in the Southern Hemisphere but a sharp decrease was observed after 2016. Over the period 1979-2024, the annual rate amounts to -0.06 +/- +0.06 millions km squared by decade (-0.48% per decade). Winter (September) sea ice extent trend amounts to -0.03 +/- +0.06 millions km squared by decade (-0.14% per decade). Summer (February) sea ice extent trend amounts to -0.06 +/- +0.05 millions km squared by decade (-1.96% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that \u2018Antarctic sea ice extent overall has had no statistically significant trend (1979\u20132018) due to contrasting regional signals and large interannual variability (high confidence).\u2019 (Meredith et al. 2019). Southern Hemisphere sea-ice extent for June through October 2024 were second lowest sea ice extent values (for these months) since 1979. November 2024 was the lowest sea ice extent recorded for all November months since 1979.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00187\n\n**References:**\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. MassonDelmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 203-320. https://doi.org/10.1017/9781009157964.005.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, doi:10.1017/9781009157896.011.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1978-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["antarctic-omi-si-extent-obs", "coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-extent", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00187", "title": "Antarctic Monthly Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"description": "The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00003\n\n**References:**\n\n* Ardyna, M., Babin, M., Gosselin, M., Devred, E., B\u00e9langer, S., Matsuoka, A., and Tremblay, J.-\u00c9.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383\u20134404, https://doi.org/10.5194/bg-10-4383-2013, 2013.\n* Yumruktepe, V. \u00c7., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901\u20133921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.\n", "extent": {"spatial": {"bbox": [[-180, 41.559295654296875, 180, 90]]}, "temporal": {"interval": [["2019-01-01T00:00:00Z", "2026-05-19T00:00:00Z"]]}}, "keywords": ["arctic-analysisforecast-bgc-002-004", "arctic-ocean", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-floor-depth-below-sea-level", "sea-water-ph-reported-on-total-scale", "sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00003", "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"description": "The operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00001\n\n**References:**\n\n* Sakov, P., Counillon, F., Bertino, L., Lis\u00e6ter, K. A., Oke, P. R. and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8(4), 633\u2013656, doi:10.5194/os-8-633-2012, 2012.\n* Melsom, A., Counillon, F., LaCasce, J. H. and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dyn., 62(8), 1245\u20131257, doi:10.1007/s10236-012-0561-5, 2012.\n", "extent": {"spatial": {"bbox": [[-180, 41.559295654296875, 180, 90]]}, "temporal": {"interval": [["2021-07-05T00:00:00Z", "2026-05-19T23:00:00Z"]]}}, "keywords": ["arctic-analysisforecast-phy-002-001", "arctic-ocean", "coastal-marine-environment", "forecast", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-ice-concentration-and/or-thickness", "sea-level", "sst", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00001", "title": "Arctic Ocean Physics Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"description": "The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n**DOI (product):** \n\nhttps://doi.org/10.48670/moi-00004\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "extent": {"spatial": {"bbox": [[-180, 41.56918716430664, 179.98499999999996, 89.99999999999787]]}, "temporal": {"interval": [["2019-08-01T00:00:00Z", "2026-05-19T23:00:00Z"]]}}, "keywords": ["arctic-analysisforecast-phy-ice-002-011", "arctic-ocean", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-ice-age", "sea-ice-albedo", "sea-ice-area-fraction", "sea-ice-classification", "sea-ice-concentration-and/or-thickness", "sea-ice-thickness", "sea-ice-volume-fraction-of-ridged-ice", "sea-ice-x-velocity", "sea-ice-y-velocity", "surface-snow-thickness", "target-application#seaiceservices", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00004", "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"description": "The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts) and wave terms (Stokes-Coriolis drift, stress and parameterisation of mixing by Langmuir cells) from the Arctic wave forecast. HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00005", "extent": {"spatial": {"bbox": [[-179.98976135253906, 41.13, 179.97952270507812, 89.99102020263672]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2026-05-19T23:45:00Z"]]}}, "keywords": ["arctic-analysisforecast-phy-tide-002-015", "arctic-ocean", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-surface-elevation", "weather-climate-and-seasonal-forecasting", "x-sea-water-velocity", "y-sea-water-velocity"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00005", "title": "Arctic Ocean Tidal Analysis and Forecast"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"description": "The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00002", "extent": {"spatial": {"bbox": [[-180, 40.98, 179.98499999999996, 90.03000000000185]]}, "temporal": {"interval": [["2022-08-01T00:00:00Z", "2026-05-12T11:00:00Z"]]}}, "keywords": ["arctic-analysis-forecast-wav-002-014", "arctic-ocean", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-sea-level", "sea-ice-area-fraction", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-spectral-peak", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00002", "title": "Arctic Ocean Wave Analysis and Forecast"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"description": "The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles.  The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00006\n\n**References:**\n\n* Simon, E., Samuelsen, A., Bertino, L. and Mouysset, S.: Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Mar. Syst., 152, 1\u201317, doi:10.1016/j.jmarsys.2015.07.004, 2015.\n", "extent": {"spatial": {"bbox": [[-180, 41.559295654296875, 180, 90]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "keywords": ["arctic-multiyear-bgc-002-005", "arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "nutrients-(o2-n-p)", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-floor-depth-below-sea-level", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00006", "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"description": "The current version of the TOPAZ system - TOPAZ4b -  is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans,  has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly. Sea ice thickness prior to 2010 has been post-processed by an AI-based procedure. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00007", "extent": {"spatial": {"bbox": [[-180, 34.68649673461914, 180, 90]]}, "temporal": {"interval": [["1991-01-01T00:00:00Z", "2026-01-31T00:00:00Z"]]}}, "keywords": ["arctic-multiyear-phy-002-003", "arctic-ocean", "coastal-marine-environment", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "sea-ice-age", "sea-ice-albedo", "sea-ice-area-fraction", "sea-ice-classification", "sea-ice-concentration-and/or-thickness", "sea-ice-thickness", "sea-ice-volume-fraction-of-ridged-ice", "sea-ice-x-velocity", "sea-ice-y-velocity", "sea-level", "sst", "surface-snow-thickness", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00007", "title": "Arctic Ocean Physics Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"description": "The Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00336\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "extent": {"spatial": {"bbox": [[-180, 41.56918716430664, 179.97952270507812, 90.03000000000151]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["arctic-multiyear-phy-ice-002-016", "arctic-ocean", "coastal-marine-environment", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-ice-concentration-and/or-thickness", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00336", "title": "Arctic Ocean Sea Ice Reanalysis"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"description": "The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00008", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-multiyear-wav-002-013", "arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-sea-level", "sea-ice-area-fraction", "sea-ice-thickness", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00008", "title": "Arctic Ocean Wave Hindcast"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"description": "**DEFINITION**\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\n**CONTEXT**\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00192\n\n**References:**\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2021-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "arctic-omi-si-transport-nordicseas", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-ice-concentration-and/or-thickness", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00192", "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "ARCTIC_OMI_SI_extent": {"description": "**DEFINITION**\n\nEstimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:\ni) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).\nThe Arctic sea ice extent used here is based on the \u201cmulti-product\u201d  (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019).  \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time.  \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00190\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "arctic-omi-si-extent", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-ice-extent", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00190", "title": "Arctic Sea Ice Extent from Reanalysis"}, "ARCTIC_OMI_SI_extent_obs": {"description": "**DEFINITION**\n\nSea Ice Extent (SIE) is the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since the 1970s. SIE is often reported with units of millions square kilometers. SIE includes all sea ice, and does not include lake and river ice. The change in sea ice extent (trend) is expressed in millions of km squared per decade. In addition, trends are expressed relative to the 1979-2024 period in % per decade. These trends are calculated (i) for the annual mean values; (ii) for the winter maximum cover (March in the Northern Hemisphere); (iii) for the summer minimum extent (September in the Northern Hemisphere). The annual mean trend is reported on the key figure. The March and September trends are reported in the text below.  See also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019; Fox-Kemper et al. 2021).\n\n\n**CMEMS KEY FINDINGS**\n\nSince 1979, the Northern Hemisphere sea ice extent has decreased in all months. Loss of sea ice extent during summer exceeds the loss observed during winter periods. Over the period 1979-2024, the annual rate amounts to -0.49 +/- +0.02 millions km squared by decade (-4.27% per decade). Winter (March) sea ice extent trend amounts to -0.37 +/- +0.03 millions km squared by decade (-2.45% per decade). Summer (September) sea ice extent trend amounts to -0.79 +/- +0.06 millions km squared by decade (-12.57% per decade). These values are in agreement with those assessed in the IPCC SROCC (Meredith et al. 2019, with estimates up until year 2018). While 2024 started with the 20th lowest mean January sea ice extent, it ended with the lowest mean December sea ice extent, since 1979. September 2024 was the 5th lowest mean September sea ice extent since 1979. The record low mean September was in 2020.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00191\n\n**References:**\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. MassonDelmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 203-320. https://doi.org/10.1017/9781009157964.005.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1978-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "arctic-omi-si-extent-obs", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-extent", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00191", "title": "Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_OMI_TEMPSAL_FWC": {"description": "**DEFINITION**\n\nEstimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to:\n* obtain the mean FWC as expressed in cubic km (km3) \n* monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time).\n\n**CONTEXT**\n\nThe Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport.  \n\n**CMEMS KEY FINDINGS**\n\nSince 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 \u00b1 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00193\n\n**References:**\n\n* G. Garric, O. Hernandez, C. Bricaud, A. Storto, K. A. Peterson, H. Zuo, 2018: Arctic Ocean freshwater content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s70\u2013s72, DOI: 10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2017-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "arctic-omi-tempsal-fwc", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00193", "title": "Arctic Freshwater Content from Reanalysis"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"description": "This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate,  dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available.  The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of  the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00009", "extent": {"spatial": {"bbox": [[9.041487693786621, 53.00829315185547, 30.208656311035156, 65.89141845703125]]}, "temporal": {"interval": [["2020-10-01T00:00:00Z", "2026-05-19T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-analysisforecast-bgc-003-007", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "none", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00009", "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"description": "This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00010", "extent": {"spatial": {"bbox": [[9.041487693786621, 53.00829315185547, 30.208656311035156, 65.89141845703125]]}, "temporal": {"interval": [["2020-10-01T00:00:00Z", "2026-05-20T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-analysisforecast-phy-003-006", "coastal-marine-environment", "eastward-sea-water-velocity", "eastward-sea-water-velocity-assuming-no-tide", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "northward-sea-water-velocity", "northward-sea-water-velocity-assuming-no-tide", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "s", "sea-ice-area-fraction", "sea-ice-thickness", "sea-surface-height-above-geoid-assuming-no-tide", "sea-surface-height-above-sea-level", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "t", "target-application#seaiceservices", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00010", "title": "Baltic Sea Physics Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"description": "This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00011", "extent": {"spatial": {"bbox": [[9.013887405395508, 53.0082893371582, 30.207738876342773, 65.90777587890625]]}, "temporal": {"interval": [["2018-12-01T01:00:00Z", "2026-05-20T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-analysisforecast-wav-003-010", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "none", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-spectral-peak", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00011", "title": "Baltic Sea Wave Analysis and Forecast"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"description": "This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):**\n\nhttps://doi.org/10.48670/moi-00012", "extent": {"spatial": {"bbox": [[9.041532516479492, 53.00829315185547, 30.20798683166504, 65.89141845703125]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-multiyear-bgc-003-012", "cell-thickness", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "model-level-number-at-sea-floor", "mole-concentration-of-ammonium-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom)", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated)", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-ph-reported-on-total-scale", "secchi-depth-of-sea-water", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00012", "title": "Baltic Sea Biogeochemistry Reanalysis"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"description": "This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the  ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00013", "extent": {"spatial": {"bbox": [[9.041532516479492, 53.00829315185547, 30.20798683166504, 65.89141845703125]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-multiyear-phy-003-011", "cell-thickness", "coastal-marine-environment", "eastward-sea-water-velocity", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "multi-year", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-ice-area-fraction", "sea-ice-thickness", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sea-water-salinity(at-bottom)", "sst", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00013", "title": "Baltic Sea Physics Reanalysis"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"description": "This Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products.  The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014", "extent": {"spatial": {"bbox": [[9.013799667358398, 53.0082893371582, 30.20800018310547, 65.90809631347656]]}, "temporal": {"interval": [["1980-01-01T01:00:00Z", "2026-02-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "balticsea-multiyear-wav-003-015", "charnock-coefficient-for-surface-roughness-length-for-momentum-in-air", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-spectral-peak", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "surface-downward-eastward-stress-due-to-ocean-viscous-dissipation", "surface-downward-northward-stress-due-to-ocean-viscous-dissipation", "surface-roughness-length", "wave-momentum-flux-into-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00014", "title": "Baltic Sea Wave Hindcast"}, "BATHYMETRY_GLO_PHY_COASTAL_L4_MY_016_001": {"description": "**DEFINITION**\n\nImportant note to users:  These data are not to be used for navigation. The data is 100 m resolution and as high quality as possible. It has been produced with state-of-the-art technology and validated to the best of the producer\u2019s ability and where sufficient high-quality data were available. These data could be useful for planning and modelling purposes. The user should independently assess the adequacy of any material, data and/or information of the product before relying upon it. Neither Mercator Ocean International/Copernicus Marine Service nor the data originators are liable for any negative consequences following direct or indirect use of the product information, services, data products and/or data.\n\nProduct overview: This is a satellite derived bathymetry product covering the global coastal area (where data retrieval is possible), with 100 m resolution, based on Sentinel-2. This global coastal product has been developed based on 3 methodologies: Intertidal Satellite-Derived Bathymetry; Physics-based optical Satellite-Derived Bathymetry from RTE inversion; and Wave Kinematics Satellite-Derived Bathymetry from wave dispersion.\n\nThere is one dataset for each of the methods (including a quality index based on uncertainty) and an additional one where the three datasets were merged (also includes a quality index). Using their expertise and special techniques the consortium tried to achieve an optimal balance between coverage and data quality.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00364", "extent": {"spatial": {"bbox": [[-179.99947916666667, -89.99947916666666, 179.99947916779914, 89.99947916654406]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["bathymetry-glo-phy-coastal-l4-my-016-001", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "water-depth-phy-comp", "water-depth-phy-irte", "water-depth-phy-it", "water-depth-phy-wk", "water-depth-quality-indicator-phy-comp", "water-depth-quality-indicator-phy-irte", "water-depth-quality-indicator-phy-it", "water-depth-quality-indicator-phy-wk", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "EOMAP (Germany) - Deltares (The Netherlands) - GGS (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00364", "title": "Global coastal satellite derived bathymetry static"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"description": "BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~2.5km x 59-levels resolution, and are provided as daily and monthly means.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00354\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L. and Capet, A. (2020) \u201cBlack Sea Biogeochemical Analysis and Forecast (CMEMS Near-Real Time BLACKSEA Biogeochemistry).\u201d Copernicus Monitoring Environment Marine Service (CMEMS).\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["2020-11-01T00:00:00Z", "2026-05-19T23:00:00Z"]]}}, "keywords": ["black-sea", "blksea-analysisforecast-bgc-007-010", "cell-thickness", "coastal-marine-environment", "downwelling-photosynthetic-photon-flux-in-sea-water", "forecast", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "model-level-number-at-sea-floor", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BS-MARINES-LIEGE-BE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00354", "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"description": "The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 (ca. 2.5 km) and it has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents; the 2D variables sea surface height, bottom potential temperature, mixed layer thickness, sea-ice extent and sea-ice thickness.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/mds-00355\n\n**References:**\n\n* Jansen, E., Martins, D., Causio, S., Maslo, A., Ilicak, M., Cret\u00ec, S., Lecci, R., Lima, L., S\u00f6zer, A., Trotta, F., Ciliberti, S. A. (2024). Black Sea Physical Analysis and Forecast (Copernicus Marine Service BLK-PHY, EAS7 system) (Version 1) [Data set]. Copernicus Marine Service.\n", "extent": {"spatial": {"bbox": [[25, 39.5, 42, 47.32500076293945]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", "2026-05-20T00:00:00Z"]]}}, "keywords": ["black-sea", "blksea-analysisforecast-phy-007-001", "cell-thickness", "coastal-marine-environment", "eastward-sea-water-velocity", "eastward-sea-water-velocity-detided", "forecast", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "near-real-time", "northward-sea-water-velocity", "northward-sea-water-velocity-detided", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-surface-height-above-geoid-detided", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00355", "title": "Black Sea Physics Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"description": "The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS).\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/mds-00360\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n* Ricker, M., Behrens, A., & Staneva, J. (2024). The operational CMEMS wind wave forecasting system of the Black Sea. Journal of Operational Oceanography, 1\u201322. https://doi.org/10.1080/1755876X.2024.2364974\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47.32500076293945]]}, "temporal": {"interval": [["2021-04-16T12:00:00Z", "2026-05-20T23:00:00Z"]]}}, "keywords": ["black-sea", "blksea-analysisforecast-wav-007-003", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-spectral-peak", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "weather-climate-and-seasonal-forecasting", "wind-speed"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "HEREON (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00360", "title": "Black Sea Waves Analysis and Forecast"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"description": "The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n**DOI (product)**:\nhttps://doi.org/10.48670/mds-00372\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L., & Capet, A. (2020). Black Sea Biogeochemical Reanalysis (CMEMS BS-Biogeochemistry) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["1992-01-01T00:00:00Z", "2026-03-01T00:00:00Z"]]}}, "keywords": ["black-sea", "blksea-multiyear-bgc-007-005", "cell-thickness", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "model-level-number-at-sea-floor", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-height-above-geoid", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "ULG (Belgium)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00372", "title": "Black Sea Biogeochemistry Reanalysis"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"description": "The BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/mds-00356", "extent": {"spatial": {"bbox": [[-60, 40.5, 42, 9.969209968386869e+36]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-03-01T00:00:00Z"]]}}, "keywords": ["black-sea", "blksea-multiyear-phy-007-004", "cell-thickness", "coastal-marine-environment", "eastward-sea-water-velocity", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "multi-year", "net-downward-shortwave-flux-at-sea-water-surface", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "precipitation-flux", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "surface-downward-heat-flux-in-sea-water", "surface-downward-latent-heat-flux", "surface-downward-sensible-heat-flux", "surface-downward-x-stress", "surface-downward-y-stress", "surface-net-downward-longwave-flux", "surface-water-evaporation-flux", "water-flux-into-sea-water-from-rivers", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00356", "title": "Black Sea Physics Reanalysis"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"description": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/mds-00357", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2026-04-30T23:00:00Z"]]}}, "keywords": ["black-sea", "blksea-multiyear-wav-007-006", "charnock-coefficient-for-surface-roughness-length-for-momentum-in-air", "coastal-marine-environment", "eastward-friction-velocity-at-sea-water-surface", "eastward-wave-mixing-momentum-flux-into-sea-water", "level-4", "marine-resources", "marine-safety", "multi-year", "northward-friction-velocity-at-sea-water-surface", "northward-wave-mixing-momentum-flux-into-sea-water", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "surface-roughness-length", "wave-mixing-energy-flux-into-sea-water", "weather-climate-and-seasonal-forecasting", "wind-from-direction", "wind-speed"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "HEREON (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00357", "title": "Black Sea Waves Reanalysis"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"description": "**DEFINITION**\n\nThe oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which  [O2] < 20\u00b5M, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m\u00b3]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m\u00b2]. The 20\u00b5M threshold was chosen to minimize the indicator sensitivity to sensor\u2019s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.\n\n**CONTEXT**\n\nThe Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sak\u0131nan and G\u00fcc\u00fc 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.\n\n**CMEMS KEY FINDINGS**\n\nDuring the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00213\n\n**References:**\n\n* Capet, A., Vandenbulcke, L., & Gr\u00e9goire, M. (2020). A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status. Biogeosciences , 17(24), 6507\u20136525.\n* Capet A, Stanev E, Beckers JM, Murray J, Gr\u00e9goire M. (2016). Decline of the Black Sea oxygen inventory. Biogeosciences. 13:1287-1297.\n* Capet Arthur, Vandenbulcke Luc, Veselka Marinova, Gr\u00e9goire Marilaure. (2018). Decline of the Black Sea oxygen inventory. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Konovalov S, Murray JW. (2001). Variations in the chemistry of the Black Sea on a time scale of decades (1960\u20131995). J Marine Syst. 31: 217\u2013243.\n* Murray J, Jannasch H, Honjo S, Anderson R, Reeburgh W, Top Z, Friederich G, Codispoti L, Izdar E. (1989). Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 338: 411\u2013413.\n* Ostrovskii A and Zatsepin A. (2011). Short-term hydrophysical and biological variability over the northeastern Black Sea continental slope as inferred from multiparametric tethered profiler surveys, Ocean Dynam., 61, 797\u2013806, 2011.\n* \u00d6zsoy E and \u00dcnl\u00fcata \u00dc. (1997). Oceanography of the Black Sea: a review of some recent results. Earth-Science Reviews. 42(4):231-72.\n* Sak\u0131nan S, G\u00fcc\u00fc AC. (2017). Spatial distribution of the Black Sea copepod, Calanus euxinus, estimated using multi-frequency acoustic backscatter. ICES J Mar Sci. 74(3):832-846. doi:10.1093/icesjms/fsw183\n* Stanev E, He Y, Grayek S, Boetius A. (2013). Oxygen dynamics in the Black Sea as seen by Argo profiling floats. Geophys Res Lett. 40(12), 3085-3090.\n* Tugrul S, Basturk O, Saydam C, Yilmaz A. (1992). Changes in the hydrochemistry of the Black Sea inferred from water density profiles. Nature. 359: 137-139.\n* von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography 11, S1\u2013S142 (2018).\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1955-01-01T00:00:00Z", "2021-06-01T00:00:00Z"]]}}, "keywords": ["black-sea", "blksea-omi-health-oxygen-trend", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "ocean-mole-content-of-dissolved-molecular-oxygen", "oceanographic-geographical-features", "sea-water-sigma-theta-defined-by-mole-concentration-of-dissolved-molecular-oxygen-in-sea-water-above-threshold", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BS-MARINES-LIEGE-BE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00213", "title": "Black Sea Oxygen Trend from Observations Reprocessing"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021).\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern  dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).\n\n**CMEMS KEY FINDINGS**\n\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern  dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m.  The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akp\u0131nar and K\u00f6m\u00fcrc\u00fc, 2012; and Akpinar et al., 2016).\nThe anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00214\n\n**References:**\n\n* Akp\u0131nar, A.; K\u00f6m\u00fcrc\u00fc, M.\u02d9I. Wave energy potential along the south-east coasts of the Black Sea. Energy 2012, 42, 289\u2013302.\n* Akp\u0131nar, A., Bing\u00f6lbali, B., Van Vledder, G., 2016. Wind and wave characteristics in the Black Sea based on the SWAN wave model forced with the CFSR winds. Ocean Eng. 126, 276\u2014298, http://dx. doi.org/10.1016/j.oceaneng.2016.09.026.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Staneva, J. Behrens, A., Gayer G, Ricker M. (2019a) Black sea CMEMS MYP QUID Report\n* Staneva J, Behrens A., Gayer G, Aouf A., (2019b). Synergy between CMEMS products and newly available data from SENTINEL, Section 3.3, In: Schuckmann K,et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, doi: 10.1080/1755876X.2019.1633075.\n", "extent": {"spatial": {"bbox": [[27.37006950378418, 40.86015319824219, 41.9622917175293, 46.80458068847656]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["black-sea", "blksea-omi-seastate-extreme-var-swh-mean-and-anomaly", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00214", "title": "Black Sea Significant Wave Height extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea.\nWhile the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Ba\u015fusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Ba\u015fusta (2018) presented an increase in sea surface temperature that varied along both east\u2013west and south\u2013north directions in the Black Sea. \n\n**CMEMS KEY FINDINGS**\n\nThe mean annual 99th percentile in the period 1993\u20132019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00216\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Capet, A., Barth, A., Beckers, J. M., & Marilaure, G. (2012). Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 128-142. https://doi.org/10.1016/j.dsr2.2012.04.010\n* Ginzburg, A. I.; Kostianoy, A. G.; Sheremet, N. A. (2004). Seasonal and interannual variability of the Black Sea surface temperature as revealed from satellite data (1982\u20132000), Journal of Marine Systems, 52, 33-50. https://doi.org/10.1016/j.jmarsys.2004.05.002.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sakalli A, Ba\u015fusta N. 2018. Sea surface temperature change in the Black Sea under climate change: A simulation of the sea surface temperature up to 2100. International Journal of Climatology, 38(13), 4687-4698. https://doi.org/10.1002/joc.5688\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["black-sea", "blksea-omi-tempsal-extreme-var-temp-mean-and-anomaly", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00216", "title": "Black Sea Surface Temperature extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"description": "\"_DEFINITION_'\n\nThe blksea_omi_tempsal_sst_area_averaged_anomalies product for 2024 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018). \n\n**KEY FINDINGS**\n\nIn 2024, the annual mean Sea Surface Temperature (SST) in the Black Sea was 17.2\u202f\u00b0C, which is 1.8\u202f\u00b0C above the 1991\u20132020 climatological average of 15.4\u202f\u00b0C, and nearly 1\u202f\u00b0C higher than the previous year's value of 16.5\u202f\u00b0C. This marks the highest annual mean SST recorded in the region since 1982.\nUnlike previous years, all monthly mean SST anomalies in 2024 were positive, ranging from 1 to 3\u202f\u00b0C. The largest anomaly, 3.2\u202f\u00b0C, occurred in April, corresponding to a mean SST of 13.1\u202f\u00b0C, while the smallest anomaly, 0.83\u202f\u00b0C, was recorded in December, with a mean SST of 11.6\u202f\u00b0C.\nOver the period 1982\u20132024, the Black Sea SST has increased at an average rate of 0.056\u202f\u00b1\u202f0.003\u202f\u00b0C per year, resulting in a total warming of approximately 2.4\u202f\u00b0C over the past 43 years.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00217\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["black-sea", "blksea-omi-tempsal-sst-area-averaged-anomalies", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00217", "title": "Black Sea Surface Temperature time series and trend from Observations Reprocessing"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"description": "**DEFINITION**\n\nThe blksea_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2024 (\u00b0C/year). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n\n**KEY FINDINGS**\n\nOver the past four decades (1982-2024), Sea Surface Temperature (SST) in the Black Sea warmed at a rate of 0.056 \u00b1 0.003 \u00b0C per year, corresponding to a mean surface temperature warming of about 2.4 \u00b0C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.05 \u00b0C/year to 0.06 \u00b0C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00218\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "extent": {"spatial": {"bbox": [[26.375, 38.724998474121094, 42.375, 48.775001525878906]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["black-sea", "blksea-omi-tempsal-sst-trend", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00218", "title": "Black Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"description": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system  at 1/4 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSISFORECAST_PHYS_001_024 at daily frequency.                                                                           \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00015", "extent": {"spatial": {"bbox": [[-180, -80, 179.75, 90]]}, "temporal": {"interval": [["2021-10-01T00:00:00Z", "2026-05-20T00:00:00Z"]]}}, "keywords": ["cell-thickness", "coastal-marine-environment", "forecast", "global-analysisforecast-bgc-001-028", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "model-level-number-at-sea-floor", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-iron-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-floor-depth-below-geoid", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00015", "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"description": "The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00016", "extent": {"spatial": {"bbox": [[-180, -80, 179.9169921875, 90]]}, "temporal": {"interval": [["2019-01-01T00:00:00Z", "2026-05-21T00:00:00Z"]]}}, "keywords": ["age-of-sea-ice", "cell-thickness", "coastal-marine-environment", "eastward-sea-ice-velocity", "eastward-sea-water-velocity", "forecast", "global-analysisforecast-phy-001-024", "global-ocean", "in-situ-ts-profiles", "invariant", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "near-real-time", "northward-sea-ice-velocity", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-ice-albedo", "sea-ice-area-fraction", "sea-ice-concentration-and/or-thickness", "sea-ice-speed", "sea-ice-surface-temperature", "sea-ice-thickness", "sea-level", "sea-surface-height-above-geoid", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-pressure-at-sea-floor", "sea-water-salinity", "sst", "surface-snow-thickness", "target-application#seaiceforecastingapplication", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00016", "title": "Global Ocean Physics Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"description": "The operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00017\n\n**References:**\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lef\u00e8vre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n", "extent": {"spatial": {"bbox": [[-180, -80, 179.91683959960938, 90]]}, "temporal": {"interval": [["2021-01-01T03:00:00Z", "2026-05-21T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "forecast", "global-analysisforecast-wav-001-027", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00017", "title": "Global Ocean Waves Analysis and Forecast"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"description": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency                                                                           \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00019", "extent": {"spatial": {"bbox": [[-180, -80, 179.75, 90]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-02-28T00:00:00Z"]]}}, "keywords": ["-drivers-and-tipping-points", "/cross-discipline/rate-measurements", "atlantic-ocean", "cell-thickness", "coastal-marine-environment", "data", "global-multiyear-bgc-001-029", "global-ocean", "invariant", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "model-level-number-at-sea-floor", "modelling-data", "mole-concentration-of-dissolved-iron-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "none", "north-mid-atlantic-ridge", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-ph-reported-on-total-scale", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "weather-climate-and-seasonal-forecasting", "wp5-assessing-state"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00019", "title": "Global Ocean Biogeochemistry Hindcast"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"description": "The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n**Forcings sources:**\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n**Vertical coverage:**\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00020\n\n**References:**\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Mod\u00e9lisation du zooplancton et du micronecton marins. Th\u00e8se de Doctorat, Universit\u00e9 de La Rochelle, 136 p.\n", "extent": {"spatial": {"bbox": [[-180, -80, 179.9166717529297, 89.91666412353516]]}, "temporal": {"interval": [["1998-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "eastward-sea-water-velocity-vertical-mean-over-pelagic-layer", "euphotic-zone-depth", "global-multiyear-bgc-001-033", "global-ocean", "invariant", "level-4", "marine-resources", "marine-safety", "mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water", "mass-content-of-zooplankton-expressed-as-carbon-in-sea-water", "multi-year", "net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water", "northward-sea-water-velocity-vertical-mean-over-pelagic-layer", "numerical-model", "oceanographic-geographical-features", "sea-water-pelagic-layer-bottom-depth", "sea-water-potential-temperature-vertical-mean-over-pelagic-layer", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00020", "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"description": "The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for  temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00021\n\n**References:**\n\n* Lellouche Jean-Michel, Greiner Eric, Bourdall\u00e9-Badie Romain, Garric Gilles, Melet Ang\u00e9lique, Dr\u00e9villon Marie, Bricaud Cl\u00e9ment, Hamon Mathieu, Le Galloudec Olivier, Regnier Charly, Candela Tony, Testut Charles-Emmanuel, Gasparin Florent, Ruggiero Giovanni, Benkiran Mounir, Drillet Yann, Le Traon Pierre-Yves https://doi.org/10.3389/feart.2021.698876\n", "extent": {"spatial": {"bbox": [[-180, -80, 179.9166717529297, 90]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-04-28T00:00:00Z"]]}}, "keywords": ["-drivers-and-tipping-points", "/physical-oceanography/water-column-temperature-and-salinity", "atlantic-ocean", "cell-thickness", "coastal-marine-environment", "data", "eastward-sea-ice-velocity", "eastward-sea-water-velocity", "global-multiyear-phy-001-030", "global-ocean", "in-situ-ts-profiles", "invariant", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "modelling-data", "multi-year", "north-mid-atlantic-ridge", "northward-sea-ice-velocity", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "salinity", "sea-floor-depth-below-geoid", "sea-ice-area-fraction", "sea-ice-concentration-and/or-thickness", "sea-ice-thickness", "sea-level", "sea-surface-height-above-geoid", "sea-temperature", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "simulation-data", "sst", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting", "wp5-assessing-state"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00021", "title": "Global Ocean Physics Reanalysis"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"description": "You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution: monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe three reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * and C-GLORSv7 from CMCC (It);\n \nThese three products provided three different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00024", "extent": {"spatial": {"bbox": [[-180, -80, 179.75, 90]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "eastward-sea-water-velocity", "global-multiyear-phy-ens-001-031", "global-ocean", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "multi-year", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-ice-concentration-and/or-thickness", "sea-ice-fraction", "sea-ice-thickness", "sea-level", "sea-surface-height", "sea-water-potential-temperature", "sea-water-salinity", "sst", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "GLO-CMCC-LECCE-IT", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00024", "title": "Global Ocean Ensemble Physics Reanalysis"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"description": "GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards.   \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00022", "extent": {"spatial": {"bbox": [[-180, -89.80000305175781, 179.8000030517578, 89.80000305175781]]}, "temporal": {"interval": [["1980-01-01T00:00:00Z", "2026-02-28T21:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-multiyear-wav-001-032", "global-ocean", "invariant", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00022", "title": "Global Ocean Waves Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"description": "**DEFINITION**\n\nNINO34 sub surface temperature anomaly (\u00b0C) is defined as the difference between the subsurface temperature  averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area  and the climatological reference value over same area  (GLOBAL_MULTIYEAR_PHY_ENS_001_031). Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014.  \n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS**\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter.  \nThe NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00220\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-climvar-enso-tzt-anomaly", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-water-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00220", "title": "Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"description": "**DEFINITION**\n\nNINO34 sea surface temperature anomaly (\u00b0C) is defined as the difference between the sea surface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area  and the climatological reference value over same area  (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.4\u00b0C during a period of six month.\n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most  important source of climatic  variability resulting  from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations.  Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS**\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008.  \nThe NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00219\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-climvar-enso-sst-area-averaged-anomalies", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-surface-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00219", "title": "Nino 3.4 Sea Surface Temperature time series from Reanalysis"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"description": "**DEFINITION**\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\n**CONTEXT**\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\n**CMEMS KEY FINDINGS**\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.01e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In  2021 (resp. 2020), the global ocean CO2 sink was    2.41\u00b10.13 (resp.  2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr.  In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of  0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00223\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-health-carbon-co2-flux-integrated", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00223", "title": "Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"description": "**DEFINITION**\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3  ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time.  Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements.  \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\n**CONTEXT**\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14).  \n\n**CMEMS KEY FINDINGS**\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of  -0.0017\u00b10.019 decade-1\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00224\n\n**References:**\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-health-carbon-ph-area-averaged", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "sea-water-ph-reported-on-total-scale", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00224", "title": "Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"description": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e.,  \u03c3-to-\u00b5 ratio over a threshold of 1  0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\n**CONTEXT**\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021).      As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\n**CMEMS KEY FINDINGS**\n\nSince 1985, pH has been decreasing at a rate between -0.001 yr-1 and -0.0023 yr-1 over most of the global ocean basins. Tropical and subtropical regions show pH trends falling within the interquartile range of all the trend estimates (between -0.0016 yr-1 and -0.0018 yr-1). In sectors south of the Indian Ocean and Pacific Ocean, pH decreases much faster, reaching a growth rate of up to -0.0022 yr-1. An even more significant rate of change in pH is observed in the Greenland area and on the northeast coastal side of Canada, where values reach up to -0.0028 yr-1. Some polar or coastal areas show no significant trends.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00277\n\n**References:**\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n", "extent": {"spatial": {"bbox": [[-179.875, -88.125, 179.875, 89.875]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-health-carbon-ph-trend", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "trend-of-surface-ocean-ph-reported-on-total-scale", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00277", "title": "Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"description": "**DEFINITION**\n\nThe Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 over the whole time period (1993-2023) and over the period for which there are comparable observations (Apr 2004-Mar 2023). The observations come from the RAPID array (Smeed et al, 2017).  \n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018).\n\n**CMEMS KEY FINDINGS** \n\nThe AMOC strength at 1000m is found to be 17.0 \u00b1 3.2 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00231\n\n**References:**\n\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* Roberts, C. D., J. Waters, K. A. Peterson, M. D. Palmer, G. D. McCarthy, E. Frajka\u2010Williams, K. Haines, D. J. Lea, M. J. Martin, D. Storkey, E. W. Blockley and H. Zuo (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N, Geophys. Res. Lett., 40, 5164\u20135170 doi: 10.1002/grl.50930.\n* Sinha, B., Smeed, D.A., McCarthy, G., Moat, B.I., Josey, S.A., Hirschi, J.J.-M., Frajka-Williams, E., Blaker, A.T., Rayner, D. and Madec, G. (2018), The accuracy of estimates of the overturning circulation from basin-wide mooring arrays. Progress in Oceanography, 160. 101-123\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["amoc-cglo", "amoc-glor", "amoc-glos", "amoc-mean", "amoc-oras", "amoc-std", "coastal-marine-environment", "global-ocean", "global-omi-natlantic-amoc-26n-profile", "marine-resources", "marine-safety", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00231", "title": "Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"description": "**DEFINITION**\n\nThe Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 . The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017).\n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017).\n\n**CMEMS KEY FINDINGS **\n\nThe AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00232\n\n**References:**\n\n* Jackson, L. C., Peterson, K. A., Roberts, C. D. & Wood, R. A. (2016). Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geosci, 9, 518\u2014522\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* McCarthy, G., Frajka-Williams, E., Johns, W. E., Baringer, M. O., Meinen, C. S., Bryden, H. L., Rayner, D., Duchez, A., Roberts, C. & Cunningham, S. A. (2012). Observed interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N. Geophys. Res. Lett., 39, L19609+\n* Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A. & Bryden, H. L. (2014). Observed decline of the Atlantic meridional overturning circulation 2004&2012. Ocean Science, 10, 29--38.\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I., Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H. L. & McCarthy, G. D. (2018). The North Atlantic Ocean Is in a State of Reduced Overturning. Geophys. Res. Lett., 45, 2017GL076350+. doi: 10.1002/2017gl076350\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}, "keywords": ["amoc-cglo", "amoc-glor", "amoc-glos", "amoc-mean", "amoc-oras", "amoc-std", "coastal-marine-environment", "global-ocean", "global-omi-natlantic-amoc-max26n-timeseries", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00232", "title": "Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"description": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the Copernicus Climate Change Service (C3S) (product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024).  \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means.  \n2.\tA climatology was calculated by averaging the monthly means over the period 1991 - 2020.  \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology.  \n4.\tAn area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area.  \n5.\tThe time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n6.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope.  \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nOver the period 1982 to 2024, the global average linear trend was 0.012 \u00b1 0.001\u00b0C / year (95% confidence interval). 2024 is nominally the warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Ni\u00f1o and La Ni\u00f1a conditions. For example, peaks in the time series coincide with the strong El Ni\u00f1o events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018).\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00242\n\n**References:**\n\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Gasparin, F., von Schuckmann, K., Desportes, C., Sathyendranath, S. and Pardo, S. 2018. El Ni\u00f1o southern oscillation. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-tempsal-sst-area-averaged-anomalies", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00242", "title": "Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"description": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the Copernicus Climate Change Service (C3S) (product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024).  \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means.  \n2.\tA climatology was calculated by averaging the monthly means over the period 1991 - 2020.  \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology.  \n4.\tThe time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n5.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope.  \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nWarming trends occurred over most of the globe between 1982 and 2024, with the strongest warming in the Northern Pacific and Atlantic Oceans. However, there were cooling trends in parts of the Southern Ocean and the South-East Pacific Ocean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00243\n\n**References:**\n\n* Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. and Saba, V., 2018. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), p.191. DOI: 10.1038/s41586-018-0006-5.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n* S\u00e9vellec, F., Fedorov, A.V. and Liu, W., 2017. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nature Climate Change, 7(8), p.604, doi: 10.1038/nclimate3353.\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, -89.9749984741211, 179.97500610351562, 89.97500610351562]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-tempsal-sst-trend", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00243", "title": "Global Ocean Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_OMI_WMHE_heattrp": {"description": "**DEFINITION**\n\nHeat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00245\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n* Bricaud C, Drillet Y, Garric G. 2016. Ocean mass and heat transport. In CMEMS Ocean State Report, Journal of Operational Oceanography, 9, http://dx.doi.org/10.1080/1755876X.2016.1273446\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-wmhe-heattrp", "marine-resources", "marine-safety", "multi-year", "numerical-model", "ocean-volume-transport-across-line", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00245", "title": "Mean Heat Transport across sections from Reanalysis"}, "GLOBAL_OMI_WMHE_northward_mht": {"description": "**DEFINITION**\n\nMeridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. \nThey are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nAfter an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 \u00b0 N in the Pacific Ocean during the El Ni\u00f1o event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00246\n\n**References:**\n\n* Crosnier L, Barnier B, Treguier AM, 2001. Aliasing inertial oscillations in a 1/6\u00b0 Atlantic circulation model: impact on the mean meridional heat transport. Ocean Modelling, vol 3, issues 1-2, pp21-31. https://doi.org/10.1016/S1463-5003(00)00015-9\n* Ganachaud, A. , Wunsch C. 2003. Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment. J. Climate, 16, 696\u2013705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-wmhe-northward-mht", "marine-resources", "marine-safety", "multi-year", "numerical-model", "ocean-volume-transport-across-line", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00246", "title": "Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis"}, "GLOBAL_OMI_WMHE_voltrp": {"description": "**DEFINITION**\n\nVolume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdi\u00e8re and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00247\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "global-omi-wmhe-voltrp", "marine-resources", "marine-safety", "multi-year", "numerical-model", "ocean-volume-transport-across-line", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00247", "title": "Mean Volume Transport across sections from Reanalysis"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"description": "The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables:  chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00026\n\n**References:**\n\n* Gutknecht, E. and Reffray, G. and Mignot, A. and Dabrowski, T. and Sotillo, M. G. Modelling the marine ecosystem of Iberia-Biscay-Ireland (IBI) European waters for CMEMS operational applications. Ocean Sci., 15, 1489\u20131516, 2019. https://doi.org/10.5194/os-15-1489-2019\n", "extent": {"spatial": {"bbox": [[-19.0828411, 26, 5.084566999999999, 56.08294177]]}, "temporal": {"interval": [["2020-12-01T00:00:00Z", "2026-05-16T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "euphotic-zone-depth", "forecast", "iberian-biscay-irish-seas", "ibi-analysisforecast-bgc-005-004", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mole-concentration-of-ammonium-in-sea-water", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-iron-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "none", "numerical-model", "oceanographic-geographical-features", "sea-water-ph-reported-on-total-scale", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00026", "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"description": "The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run  by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00027\n\n**References:**\n\n* Sotillo, M.G.; Campuzano, F.; Guihou, K.; Lorente, P.; Olmedo, E.; Matulka, A.; Santos, F.; Amo-Baladr\u00f3n, M.A.; Novellino, A. River Freshwater Contribution in Operational Ocean Models along the European Atlantic Fa\u00e7ade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution. J. Mar. Sci. Eng. 2021, 9, 401. https://doi.org/10.3390/jmse9040401\n* Mason, E. and Ruiz, S. and Bourdalle-Badie, R. and Reffray, G. and Garc\u00eda-Sotillo, M. and Pascual, A. New insight into 3-D mesoscale eddy properties from CMEMS operational models in the western Mediterranean. Ocean Sci., 15, 1111\u20131131, 2019. https://doi.org/10.5194/os-15-1111-2019\n* Lorente, P. and Garc\u00eda-Sotillo, M. and Amo-Baladr\u00f3n, A. and Aznar, R. and Levier, B. and S\u00e1nchez-Garrido, J. C. and Sammartino, S. and de Pascual-Collar, \u00c1. and Reffray, G. and Toledano, C. and \u00c1lvarez-Fanjul, E. Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia-Biscay-Ireland) surface waters. Ocean Sci., 15, 967\u2013996, 2019. https://doi.org/10.5194/os-15-967-2019\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n* Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladr\u00f3n, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63\u201379, https://doi.org/10.1080/1755876X.2015.1014663, 2015.\n", "extent": {"spatial": {"bbox": [[-19.082841873168945, 26, 5.084567070007324, 56.082942962646484]]}, "temporal": {"interval": [["2020-12-01T00:00:00Z", "2026-05-21T00:00:00Z"]]}}, "keywords": ["barotropic-eastward-sea-water-velocity", "barotropic-northward-sea-water-velocity", "coastal-marine-environment", "eastward-sea-water-velocity", "forecast", "iberian-biscay-irish-seas", "ibi-analysisforecast-phy-005-001", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "near-real-time", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00027", "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"description": "The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00025\n\n**References:**\n\n* Toledano, C.; Ghantous, M.; Lorente, P.; Dalphinet, A.; Aouf, L.; Sotillo, M.G. Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service. J. Mar. Sci. Eng. 2022, 10, 457. https://doi.org/10.3390/jmse10040457\n", "extent": {"spatial": {"bbox": [[-19, 26, 5.000736236572266, 56.000919342041016]]}, "temporal": {"interval": [["2021-11-27T00:00:00Z", "2026-05-19T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "e1t", "e2t", "forecast", "iberian-biscay-irish-seas", "ibi-analysisforecast-wav-005-005", "level-4", "marine-resources", "marine-safety", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-spectral-peak", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "wave-spectra", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00025", "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "IBI_MULTIYEAR_BGC_005_003": {"description": "The IBI-MFC provides the biogeochemical multi-year (non assimilative) product for the Iberia-Biscay-Ireland region starting in 01/01/1993, extended every year to use available reprocessed upstream data and regularly updated on a monthly basis to cover the period up to month M-4 using an interim processing system. The model system is designed, developed and run by Mercator Ocean International, while the operational product post-processing and interim processing system are run by NOW Systems with the support of CESGA supercomputing centre.\nThe biogeochemical model PISCES is run simultaneously with the ocean physical NEMO model, generating products at 1/36\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00028\n\n**References:**\n\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n", "extent": {"spatial": {"bbox": [[-19.082841873168945, 26, 5.084567070007324, 56.082942962646484]]}, "temporal": {"interval": [["1992-08-28T00:00:00Z", "2026-02-03T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-multiyear-bgc-005-003", "level-4", "marine-resources", "marine-safety", "multi-year", "none", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00028", "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "IBI_MULTIYEAR_PHY_005_002": {"description": "The IBI-MFC provides the ocean physical reanalysis multi year product for the Iberia-Biscay-Ireland (IBI) region starting in 01/01/1993, extended on yearly basis by using available reprocessed upstream data and regularly updated on monthly basis to cover the period up to month M-4 from present time using an interim processing system. The model system is designed, implemented and run by Mercator Ocean International, while the operational product post-processing and interim system are run by NOW Systems with the support of CESGA supercomputing centre.\nThe IBI numerical core  is based on the NEMO v3.6 ocean general circulation model, run at 1/36\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D and 2D daily, monthly and yearly physical ocean fields, as well as hourly mean fields for surface variables. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00029", "extent": {"spatial": {"bbox": [[-19.082841873168945, 26, 5.084567070007324, 56.082942962646484]]}, "temporal": {"interval": [["1992-08-28T00:00:00Z", "2026-02-03T23:00:00Z"]]}}, "keywords": ["barotropic-eastward-sea-water-velocity", "barotropic-northward-sea-water-velocity", "coastal-marine-environment", "eastward-sea-water-velocity", "iberian-biscay-irish-seas", "ibi-multiyear-phy-005-002", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "multi-year", "net-downward-shortwave-flux-at-sea-water-surface", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "precipitation-flux", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-water-potential-temperature", "sea-water-salinity", "sst", "surface-downward-heat-flux-in-sea-water", "surface-downward-latent-heat-flux", "surface-downward-sensible-heat-flux", "surface-net-downward-longwave-flux", "upward-sea-water-velocity", "water-flux-out-of-sea-ice-and-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00029", "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "IBI_MULTIYEAR_WAV_005_006": {"description": "The IBI-MFC provides a high-resolution wave reanalysis multi-year product for the Iberia-Biscay-Ireland (IBI) region starting in 01/01/1980, extended on yearly basis by using available reprocessed upstream data and regularly updated on monthly basis to cover the period up to month M-4 from present time using an interim processing system. The model system is designed and implemented by M\u00e9t\u00e9o-France and NOW Systems - the latter is in charge for the operational product post-processing and interim system run, with the support of CESGA supercomputing centre.  \nThe multi-year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France, covering the same region as the IBI near real time (NRT) analysis and forecasting product, at the same horizontal resolution of 1/36\u00ba. The system assimilates significant wave height altimeter data and wave spectral data (Envisat and CFOSAT). The MY system is forced by the ECMWF ERA5 reanalysis wind data and nested into the Global Ocean Wave Reanalysis product.\nThe catalogue includes hourly instantaneous fields of different wave parameters, including air-sea fluxes. Additionally, climatological parameters of significant wave height and zero -crossing wave period are delivered for the reference time interval 1993-2016.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00030", "extent": {"spatial": {"bbox": [[-19, 26, 5.000736236572266, 56.000919342041016]]}, "temporal": {"interval": [["1980-01-01T00:00:00Z", "2025-12-23T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "e1t", "e2t", "iberian-biscay-irish-seas", "ibi-multiyear-wav-005-006", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "surface-downward-eastward-stress-due-to-ocean-viscous-dissipation", "surface-downward-northward-stress-due-to-ocean-viscous-dissipation", "wave-mixing-energy-flux-into-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00030", "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "IBI_OMI_CURRENTS_cui": {"description": "**DEFINITION**\n\nThe Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27\u00b0N to 42\u00b0N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5\u00ba westwards.\n\u3016CUI\u3017_lat=max\u2061(T_lat )-min\u2061(T_lat)\nA high Coastal Upwelling Index indicates intense upwelling conditions.\nThe index is computed from the following Copernicus Marine products:\n\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019)\n\tIBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards)\n\n**CONTEXT**\n\nCoastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34\u00baN up to 37\u00baN.\nThe Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nThe NWA coast (latitudes below 34\u00baN) shows  a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004.\nIn the IP coast (latitudes higher than 37\u00baN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013).\nAccording to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. \nWhile in the NWA coast the upwelling activity was more intense than the average.\nThe analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 \u00b0C/Year.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00248\n\n**References:**\n\n* Fraga F. 1981. Upwelling off the Galician Coast, Northwest Spain. In: Richardson FA, editor. Coastal Upwelling. Washington (DC): Am Geoph Union; p. 176\u2013182.\n* Hempel G. 1982. The Canary current: studies of an upwelling system. Introduction. Rapp. Proc. Reun. Cons. Int. Expl. Mer., 180, 7\u20138.\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016 Iberian-Biscay-Irish Sea. In von Scuckmann et al. (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Sverdrup HV. 1938. On the process of upwelling. J Mar Res. 1:155\u2013164.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2021-08-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-currents-cui", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00248", "title": "Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe Copernicus Marine IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSISFORECAST_WAV_005_005).\nTwo parameters have been considered for this OMI:\n\n* Map of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1980-2023).\n* Anomaly of the 99th percentile in 2024: The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2024.\n\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). \n\nOver the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (Young and Ribal, 2019).\n\nThe Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world\u2019s greatest wave generating regions (M\u00f8rk et al., 2010; Folley, 2017), the southern half,  located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes.\nThe North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Gleeson et al., 2017; Mart\u00ednez-Asensio et al. 2016; Wolf et al., 2002; Bauer, 2001; Kushnir et al., 1997; Bouws et al., 1996; Bacon and Carter, 1991). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006).   \nAdditionally, a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016). \n\nIn this context, long\u2010term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences.\n\n**CMEMS KEY FINDINGS**\n\nThe climatic mean of 99th percentile (1980-2023) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described.\nThe 99th percentile anomalies in 2024 show that during this period, virtually the entire IBI region was affected by positive anomalies in maximum SWH, which exceeded the standard deviation of the historical record in the waters west of the Iberian Peninsula, the Spanish coast of the Bay of Biscay, and the African coast south of Cape Ghir. Anomalies reaching twice the standard deviation of the time series were also observed in coastal regions of the English Channel.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00249\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: .Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R., ... & Visbeck, M. (2001). North Atlantic climate variability: phenomena, impacts and mechanisms. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1863-1898.\n", "extent": {"spatial": {"bbox": [[-19, 26, 5.000736236572266, 56.000919342041016]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-seastate-extreme-var-swh-mean-and-anomaly", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00249", "title": "Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis"}, "IBI_OMI_SEASTATE_swi": {"description": "**DEFINITION**\n\nThe Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art.\nTwo Copernicus Marine products are used to compute the Strong Wave Incidence index:\n* IBI-WAV-MYP: **IBI_MULTIYEAR_WAV_005_006**\n* IBI-WAV-NRT: **IBI_ANALYSISFORECAST_WAV_005_005**\n\nThe Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product:\n\nSWI = Fobs(SWH > ThP90) \u2013 Fclim(SWH > ThP90)\n\nSince the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively.\n\n**CONTEXT**\n\nOcean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. \nThe Strong Wave Incidence index based on the Copernicus Marine regional analysis and reanalysis product provides information on the frequency of severe wave events.\n\nThe IBI-MFC covers the Europe\u2019s Atlantic coast in a region bounded by the 26\u00baN and 56\u00baN parallels, and the 19\u00baW and 5\u00baE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (M\u00f8rk et al., 2010), one of the world\u2019s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kushnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995;  Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016).\n\nThe Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands.\nMore details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020).\n\n**CMEMS KEY FINDINGS**\n\nThe trend analysis of the SWI index for the period 1980\u20132024 shows statistically significant trends (at the 99% confidence level) in wave incidence, with an increase of at least 0.05 percentage points per year in regions WAV1, WAV3, and WAV4.\nThe analysis of the historical period, based on reanalysis data, highlights the major wave events recorded in each monitoring region. In region WAV1 (panel B), the maximum wave event occurred in February 2014, resulting in a 28% increase in strong wave conditions. In region WAV2 (panel C), two notable wave events were identified in November 2009 and February 2014, with increases of 16\u201318% in strong wave conditions. Similarly, in region WAV3 (panel D), a major event occurred in February 2014, marking one of the most intense events in the region with a 20% increase in storm wave conditions. Additionally, a comparable storm affected the region two months earlier, in December 2013. In region WAV4 (panel E), the most extreme event took place in January 1996, producing a 25% increase in strong wave conditions.\nAlthough each monitoring region is generally affected by independent wave events, the analysis reveals several historical events with above-average wave activity that propagated across multiple regions: November\u2013December 2010 (WAV3 and WAV2), February 2014 (WAV1, WAV2, and WAV3), and February\u2013March 2018 (WAV1 and WAV4).\nThe analysis of the near-real-time (NRT) period (from January 2024 onward) identifies a significant event in February 2024 that impacted regions WAV1 and WAV4, resulting in increases of 20% and 15% in strong wave conditions, respectively. For region WAV4, this event represents the second most intense event recorded in the region.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00251\n\n**References:**\n\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Dodet G, Bertin X, Taborda R. 2010. Wave climate variability in the North-East Atlantic Ocean over the last six decades, Ocean Modelling, 31, 120\u2013131.\n* Dupuis H, Michel D, Sottolichio A. 2006. Wave climate evolution in the Bay of Biscay over two decades. Journal of Marine Systems, 63, 105\u2013114.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269:676\u2013679.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* Pascual A., Levier B., Aznar R., Toledano C., Garc\u00eda-Valdecasas JM., Garc\u00eda M., Sotillo M., Aouf L., \u00c1lvarez E. (2020) Monitoring of wave sea state in the Iberia-Biscay-Ireland regional seas. In von Scuckmann et al. (2020) Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097\n* Tsimplis M N, Woolf D K, Osborn T J, Wakelin S, Wolf J, Flather R, Shaw A G P, Woodworth P, Challenor P, Blackman D, Pert F, Yan Z, Jevrejeva S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Phil. Trans. R. Soc. A, Vol. 363, 1329\u20131358 doi:10.1098/rsta.2005.1571.\n* Wang X, Swail V. 2004. Historical and possible future changes of wave heights in northern hemisphere oceans. In: Perrie W (ed), Atmosphere ocean interactions, vol 2. Wessex Institute of Technology Press, Ashurst.\n* WASA-Group. 1998. Changing waves and storms in the Northeast Atlantic?, Bull. Am. Meteorol. Soc., 79:741\u2013760.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Young I R, Zieger S, Babanin A V. 2011. Global Trends in Wind Speed and Wave Height. Science, Vol. 332, Issue 6028, 451-455, doi: 10.1126/science.1197219.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n", "extent": {"spatial": {"bbox": [[-19, 26, 5.000736236572266, 56.000919342041016]]}, "temporal": {"interval": [["1980-01-01T00:00:00Z", "2025-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-seastate-swi", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00251", "title": "Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018).\n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003).\nAlso relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980).\n\n**CMEMS KEY FINDINGS**\n\nIn the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016.\nThe analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43\u00baN parallel.\nExtreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer.\nIt is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00254\n\n**References:**\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM. 2011. Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP. 2003. The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016. Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "extent": {"spatial": {"bbox": [[-19, 26, 5, 56]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-tempsal-extreme-var-temp-mean-and-anomaly", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00254", "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "IBI_OMI_WMHE_mow": {"description": "**DEFINITION**\n\nVariations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: \n* IBI-MYP: IBI_MULTIYEAR_PHY_005_002\n* IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001\n* GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030\n* CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b\n* ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n\nThe anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year.\n\n**CONTEXT**\n\nThe Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019).\nThe importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999).\nSeveral studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019).\nThe CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019.\n\n**CMEMS KEY FINDINGS**\n\nThe absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water.\nResults obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. \nResults for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021.\nResults in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00258\n\n**References:**\n\n* Bozec A, Lozier MS, Chassignet EP, Halliwell GR. 2011. On the variability of the Mediterranean outflow water in the North Atlantic from 1948 to 2006. J Geophys Res 116:C09033. doi:10.1029/2011JC007191.\n* Daniault N, Maze JP, Arhan M. 1994. Circulation and mixing of MediterraneanWater west of the Iberian Peninsula. Deep Sea Res. Part I. 41:1685\u20131714.\n* de Pascual-Collar A, Sotillo MG, Levier B, Aznar R, Lorente P, Amo-Baladr\u00f3n A, \u00c1lvarez-Fanjul E. 2019. Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019.\n* Holliday NP. 2003. Air-sea interaction and circulation changes in the northeast Atlantic. J Geophys Res. 108(C8):3259. doi:10.1029/2002JC001344.\n* Iorga MC, Lozier MS. 1999. Signatures of the Mediterranean outflow from a North Atlantic climatology: 1. Salinity and density fields. Journal of Geophysical Research: Oceans, 104(C11), 25985-26009.\n* Lozier MS, Stewart NM. 2008. On the temporally varying northward penetration of Mediterranean overflow water and eastward penetration of Labrador Sea water. J Phys Oceanogr. 38(9):2097\u20132103. doi:10.1175/2008JPO3908.1.\n* Paillet J, Arhan M, McCartney M. 1998. Spreading of labrador Sea water in the eastern North Atlantic. J Geophys Res. 103 (C5):10223\u201310239.\n* Pascual A, Levier B, Sotillo M, Verbrugge N, Aznar R, Le Cann B. 2018. Characterisation of Mediterranean outflow w\u00e1ter in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Braseur, P., Fennel, K., Djavidnia, S.: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11, sup1, s1-s142, doi:10.1080/1755876X.2018.1489208, 2018.\n* Reid JL. 1979. On the contribution of the Mediterranean Sea outflow to the Norwegian\u2010Greenland Sea, Deep Sea Res., Part A, 26, 1199\u20131223, doi:10.1016/0198-0149(79)90064-5.\n* Talley LD, McCartney MS. 1982. Distribution and circulation of Labrador Sea water. Journal of Physical Oceanography, 12(11), 1189-1205.\n* van Aken HM. 2000. The hydrography of the mid-latitude northeast Atlantic Ocean I: the deep water masses. Deep Sea Res. Part I. 47:757\u2013788.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2021-08-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-wmhe-mow", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00258", "title": "Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"description": "Arctic Oceans  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00031", "extent": {"spatial": {"bbox": [[-180, -81.02686309814453, 179.99998474121094, 90]]}, "temporal": {"interval": [["1841-03-21T00:00:00Z", "2026-05-11T11:10:00Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "in-situ-observation", "insitu-arc-phybgcwav-discrete-mynrt-013-031", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "IMR (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00031", "title": "Arctic Ocean- In Situ Near Real Time Observations"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"description": "Baltic Sea  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00032", "extent": {"spatial": {"bbox": [[-180, -78.73729705810547, 179.9998779296875, 90]]}, "temporal": {"interval": [["1841-03-21T00:00:00Z", "2026-05-11T10:28:00Z"]]}}, "keywords": ["/observational-data/in-situ", "baltic-sea", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "in-situ-observation", "insitu-bal-phybgcwav-discrete-mynrt-013-032", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00032", "title": "Baltic Sea- In Situ Near Real Time Observations"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"description": "Black Sea  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00033", "extent": {"spatial": {"bbox": [[-180, -78.63800048828125, 179.99986267089844, 89.55000305175781]]}, "temporal": {"interval": [["1841-03-21T00:00:00Z", "2026-05-11T11:43:59Z"]]}}, "keywords": ["/observational-data/in-situ", "black-sea", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "in-situ-observation", "insitu-blk-phybgcwav-discrete-mynrt-013-034", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00033", "title": "Black Sea- In-Situ Near Real Time Observations"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"description": "Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2025 and GLobal Ocean Data Analysis Project GLODAPv2.2023. The SOCATv2025-OBS dataset contains nearly 50 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2025. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2025-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Lauvset et al. (2024). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles and datasets (see References below) in order to support future sustainability of the data products.\n\n**DOI (product):**   \nhttps://doi.org/10.17882/99089\n\n**References:**\n\n* Bakker et al., 2025. Surface Ocean CO2 Atlas Database Version 2025 (SOCATv2025) (NCEI Accession 0304549). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/648f-fv35.\n* Lauvset et al., 2023. Global Ocean Data Analysis Project version 2.2023 (GLODAPv2.2023) (NCEI Accession 0283442. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/zyrq-ht66.\n* Bakker et al., 2016. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data, 8, 383\u2013413, https://doi.org/10.5194/essd-8-383-2016.\n* Lauvset et al. 2024. The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product. Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2023-468.\n* Lauvset et al., 2016. A new global interior ocean mapped climatology: the \u202f\u202f1\u00b0\u00d7\u202f1\u00b0 GLODAP version 2. Earth Syst. Sci. Data, 8, 325\u2013340, https://doi.org/10.5194/essd-8-325-2016.\n", "extent": {"spatial": {"bbox": [[-180, -89.875, 179.99998474121094, 90]]}, "temporal": {"interval": [["1957-10-22T22:00:00Z", "2025-01-31T13:38:27Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "co2-carbonate-system", "coastal-marine-environment", "fugacity-of-carbon-dioxide-in-sea-water", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-bgc-carbon-discrete-my-013-050", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IMR (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/99089", "title": "Global Ocean - In Situ reprocessed carbon observations - SOCATv2025 / GLODAPv2.2023"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"description": "For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen,  chlorophyll / fluorescence and nutrients data.\n\n**DOI (product):**   \nhttps://doi.org/10.17882/86207", "extent": {"spatial": {"bbox": [[-180, -82.35169982910156, 179.99989318847656, 90]]}, "temporal": {"interval": [["1841-03-21T00:00:00Z", "2025-03-31T23:55:00Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "dissolved-oxygen", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-bgc-discrete-my-013-046", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mediterranean-sea", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "multi-year", "north-west-shelf-seas", "nutrients", "oceanographic-geographical-features", "phytoplankton", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "IMR (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/86207", "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"description": "Global Ocean   - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00036", "extent": {"spatial": {"bbox": [[-180, -90, 179.99998474121094, 90]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2046-01-01T23:30:00Z"]]}}, "keywords": ["-drivers-and-tipping-points", "/observational-data/in-situ", "/physical-oceanography/water-column-temperature-and-salinity", "cds-coriolis", "coastal-marine-environment", "currents", "data", "direction-of-sea-water-velocity", "dissolved-oxygen", "environmental-data", "global-ocean", "in-situ-observation", "insitu-glo-phybgcwav-discrete-mynrt-013-030", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "south-brazilian-shelf", "south-mid-atlantic-ridge", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting", "wp5-assessing-state", "zooplankton"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00036", "title": "Global Ocean- In-Situ Near-Real-Time Observations"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"description": "This product integrates in situ sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS, and from the Global Sea Level Observing System (GLOSS) data portals (University of Hawaii Sea Level Center and IOC/UNESCO Sea Level Station Monitoring Facility). These data are obtained from national tide gauge networks  operated by National Oceanographic Data Centres, Hydrographic and Meteorological offices, ports and Geographic Institutes, among others.\n\n**DOI (product):**   \nhttps://doi.org/10.17882/93670", "extent": {"spatial": {"bbox": [[-178.1602325439453, -66.66166687011719, 176.23199462890625, 78.92854309082031]]}, "temporal": {"interval": [["1821-05-25T05:00:00Z", "2024-12-31T23:59:59Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-ssh-discrete-my-013-053", "level-2", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "non-tidal-elevation-of-sea-surface-height", "north-west-shelf-seas", "oceanographic-geographical-features", "sea-surface-height", "tidal-sea-surface-height-above-reference-datum", "water-surface-height-above-reference-datum", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/93670", "title": "Global Ocean - Delayed Mode Sea level product"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"description": "For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office  for the period 1950-1990.\n\n**DOI (product):**  \nhttps://doi.org/10.17882/46219", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-ts-discrete-my-013-001", "level-2", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "salinity", "sea-temperature", "sea-water-salinity", "sea-water-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "OCEANSCOPE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/46219", "title": "Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"description": "Global Ocean in situ - Delayed Mode temperature and salinity CORA -objective analysis \nThe global ocean objective analysis (gridded) fields of temperature and salinity are produced from the in situ profiles available in the reprocessed (or multiyear) in-situ CORA product INSITU_GLO_PHY_TS_DISCRETE_MY_013_001, with the exception of moorings, thermosalinographs and surface drifters. The objective analysis is based on the ISAS method (Gaillard et al. 2009), a statistical estimation approach that enables the mapping of ocean in situ profiles onto three-dimensional gridded fields.\nThe resulting gridded product has a spatial resolution of 0.5\u00b0 in latitude and 0.5\u00b0 in longitude at the equator and includes 187 vertical levels. It provides monthly temperature and salinity fields centered the 15th of the month.  It is updated twice a year by a full reprocessing covering  the whole period from 1960 to December of the last year before present time (produced generally in November) and a temporal extension of the first 6 months of the ongoing year (done generally in July). \nA monthly file (data file) gathering the observed profiles used to calculate the analysis, which are interpolated on the vertical grid, is also provided.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00383\n\n**References:**\n\n* F. Gaillard, E. Autret, V. Thierry, P. Galaup, C. Coatanoan, and T. Loubrieu. Quality control of large argo datasets. Journal of Atmospheric and Oceanic Technology, 26:337\u2013351, 2009. https://doi.org/10.1175/2008JTECHO552.1.270\n", "extent": {"spatial": {"bbox": [[-180, -77.0104751586914, 179.5, 89.99999999999051]]}, "temporal": {"interval": [["1960-01-01T00:00:00Z", "2025-06-01T00:00:00Z"]]}}, "keywords": ["/observational-data/in-situ", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-ts-oa-my-013-052", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "salinity", "sea-temperature", "sea-water-salinity", "sea-water-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "OCEANSCOPE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00383", "title": "Global Ocean in situ - Delayed Mode temperature and salinity CORA -objective analysis"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"description": "The global ocean objective analysis (gridded) fields of temperature and salinity are produced from the in situ profiles available in the multiparameter near\u2013real-time in situ product INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030, with the exception of moorings, thermosalinographs and surface drifters. The objective analysis is based on the ISAS method (Gaillard et al. 2009), a statistical estimation approach that enables the mapping of ocean in situ profiles onto three-dimensional gridded fields.\nThe resulting gridded product has a spatial resolution of 0.5\u00b0 in latitude and 0.5\u00b0 in longitude at the equator and includes 187 vertical levels. It provides monthly temperature and salinity fields centered the 15th of the month, with the analysis of the previous month delivered on the 8th of each month.\nA monthly file (data file) gathering the observed profiles used to calculate the analysis, which are interpolated on the vertical grid, is also provided. \nThe product covers a two-year sliding time window. Older data are available in the corresponding delayed-mode product, INSITU_GLO_PHY_TS_OA_MY_013_052.\n\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00037", "extent": {"spatial": {"bbox": [[-180, -77, 179.5, 89.99999999999051]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["/observational-data/in-situ", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-ts-oa-nrt-013-002", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "salinity", "sea-temperature", "sea-water-salinity", "sea-water-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00037", "title": "Global Ocean in situ - Near Real Time temperature and salinity - objective analysis"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"description": "Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:\n* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.\n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.\n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories\n\n**DOI (product):**\nhttps://doi.org/10.17882/86236", "extent": {"spatial": {"bbox": [[-180, -78.30718994140625, 179.99996948242188, 89.99793243408203]]}, "temporal": {"interval": [["1979-12-11T04:00:00Z", "2026-01-30T23:00:00Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "currents", "eastward-sea-water-velocity", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-uv-discrete-my-013-044", "level-2", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "northward-sea-water-velocity", "oceanographic-geographical-features", "sea-temperature", "sea-water-temperature", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "CLS (France) - IFREMER (France) - AZTI (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/86236", "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"description": "This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:\n* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP\u2019s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00041", "extent": {"spatial": {"bbox": [[-180, -78.30599975585938, 179.99989318847656, 89.97200012207031]]}, "temporal": {"interval": [["1986-06-02T09:00:00Z", "2026-05-10T19:24:22Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "currents", "eastward-sea-water-velocity", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-phy-uv-discrete-nrt-013-048", "level-2", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "northward-sea-water-velocity", "oceanographic-geographical-features", "sea-temperature", "sea-water-temperature", "surface-eastward-sea-water-velocity", "surface-northward-sea-water-velocity", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "IFREMER (France) - AZTI (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00041", "title": "Global Ocean- in-situ Near real time observations of ocean currents"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"description": "These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\n**DOI (product):**  \nhttps://doi.org/10.17882/70345", "extent": {"spatial": {"bbox": [[-180, -90, 179.99899291992188, 74.61699676513672]]}, "temporal": {"interval": [["1970-04-27T18:00:00Z", "2024-12-31T23:57:00Z"]]}}, "keywords": ["/observational-data/in-situ", "arctic-ocean", "baltic-sea", "black-sea", "cds-coriolis", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-glo-wav-discrete-my-013-045", "level-2", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.17882/70345", "title": "Global Ocean - Delayed Mode Wave product"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"description": "IBI Seas  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00043", "extent": {"spatial": {"bbox": [[-179.9967041015625, -76.4000015258789, 179.99722290039062, 89.99939727783203]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-11T11:13:00Z"]]}}, "keywords": ["/observational-data/in-situ", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "iberian-biscay-irish-seas", "in-situ-observation", "insitu-ibi-phybgcwav-discrete-mynrt-013-033", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00043", "title": "Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"description": "Mediterranean Sea  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00044", "extent": {"spatial": {"bbox": [[-165.84800720214844, -64.9000015258789, 179.1741943359375, 78.4000015258789]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-11T09:13:00Z"]]}}, "keywords": ["/observational-data/in-situ", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "in-situ-observation", "insitu-med-phybgcwav-discrete-mynrt-013-035", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mediterranean-sea", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "HCMR (Greece)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00044", "title": "Mediterranean Sea- In-Situ Near Real Time Observations"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"description": "NorthWest Shelf area  - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00045", "extent": {"spatial": {"bbox": [[-179.9967041015625, -76.4000015258789, 179.99722290039062, 90]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-11T11:10:00Z"]]}}, "keywords": ["/observational-data/in-situ", "cds-coriolis", "coastal-marine-environment", "currents", "direction-of-sea-water-velocity", "dissolved-oxygen", "in-situ-observation", "insitu-nws-phybgcwav-discrete-mynrt-013-036", "level-2", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "phytoplankton", "salinity", "sea-surface-height", "sea-surface-wave-from-direction", "sea-surface-wave-mean-period", "sea-surface-wave-significant-height", "sea-temperature", "sea-water-practical-salinity", "sea-water-speed", "sea-water-temperature", "water-surface-height-above-reference-datum", "waves", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "BSH (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00045", "title": "Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"description": "The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\n\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n_DOI (Product)_: \nhttps://doi.org/10.48670/mds-00358\n\n**References:**\n\n* Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez, E., Amadio, C., & Cossarini, G. (2022). Mediterranean Sea Biogeochemical Analysis and Forecast (CMEMS MED-Biogeochemistry, MedBFM4 system) (Version 1) [Data set]. Copernicus Marine Service. https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_BGC_006_014_MEDBFM4\n", "extent": {"spatial": {"bbox": [[-5.541666507720947, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-19T00:00:00Z"]]}}, "keywords": ["cell-thickness", "coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mediterranean-sea", "medsea-analysisforecast-bgc-006-014", "model-level-number-at-sea-floor", "mole-concentration-of-ammonium-in-sea-water", "mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "nutrients-(o2-n-p)", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "OGS (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00358", "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"description": "The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar2.0) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations.\n\n_DOI (Product)_:\nhttps://doi.org/10.48670/mds-00359", "extent": {"spatial": {"bbox": [[-17.29166603088379, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["2021-03-01T00:00:00Z", "2026-05-20T23:00:00Z"]]}}, "keywords": ["cell-thickness", "coastal-marine-environment", "eastward-sea-water-velocity", "forecast", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-analysisforecast-phy-006-013", "model-level-number-at-sea-floor", "near-real-time", "northward-sea-water-velocity", "northward-sea-water-velocity-assuming-no-tided", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-surface-height-above-geoid-assuming-no-tided", "sea-surface-height-above-geoid-detided", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00359", "title": "Mediterranean Sea Physics Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"description": "MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height and 10m wind speed observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n**DOI (product)**: \nhttps://doi.org/10.48670/mds-00373\n\n**References:**\n\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWA\u039c4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4\n", "extent": {"spatial": {"bbox": [[-18.125, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["2021-04-19T00:00:00Z", "2026-05-20T11:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "forecast", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-analysisforecast-wav-006-017", "near-real-time", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "swh", "weather-climate-and-seasonal-forecasting", "wind-speed"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "HCMR (Greece)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00373", "title": "Mediterranean Sea Waves Analysis and Forecast"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"description": "The Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n_DOI (Product)_: https://doi.org/10.48670/mds-00374\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n\n**References:**\n\n* Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3\n* Teruzzi, A., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Di Biagio, V., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis INTERIM (CMEMS MED-Biogeochemistry, MedBFM3i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS) https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n", "extent": {"spatial": {"bbox": [[-5.541666507720947, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["1999-01-01T00:00:00Z", "2026-03-01T00:00:00Z"]]}}, "keywords": ["cell-thickness", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mediterranean-sea", "medsea-multiyear-bgc-006-008", "model-level-number-at-sea-floor", "mole-concentration-of-ammonium-in-sea-water", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "numerical-model", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-alkalinity-expressed-as-mole-equivalent", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "OGS (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00374", "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"description": "The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. The datasets are extended every year as well as on a monthly basis through one-month extensions in interim mode, reaching one month before present.\n\n_DOI (Product)_: https://doi.org/10.48670/mds-00375\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.48670/mds-00375\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini G., Pinardi, N. (2021). A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 1060, https://www.frontiersin.org/article/10.3389/feart.2021.702285, DOI=10.3389/feart.2021.702285\n", "extent": {"spatial": {"bbox": [[-6, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["1987-01-01T00:00:00Z", "2026-03-31T23:00:00Z"]]}}, "keywords": ["cell-thickness", "coastal-marine-environment", "eastward-sea-water-velocity", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-multiyear-phy-006-004", "model-level-number-at-sea-floor", "multi-year", "net-downward-shortwave-flux-at-sea-water-surface", "northward-sea-water-velocity", "numerical-model", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "precipitation-flux", "salinity", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-temperature", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "simulation-data", "sst", "surface-downward-heat-flux-in-sea-water", "surface-downward-latent-heat-flux", "surface-downward-sensible-heat-flux", "surface-downward-x-stress", "surface-downward-y-stress", "surface-net-downward-longwave-flux", "surface-water-evaporation-flux", "water-flux-into-sea-water-from-rivers", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00375", "title": "Mediterranean Sea Physics Reanalysis"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"description": "MEDSEA_MULTIYEAR_WAV_006_012  is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The dataset is extended every year as well as on a monthly basis through one-month extensions in interim mode, reaching one month before present. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF.\n\n_DOI (Product)_: https://doi.org/10.48670/mds-00376\n\n_DOI (Interim dataset)_:    \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I  \n                                                                                                                                                      \n_DOI (climatological dataset)_:    \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM\n\n**References:**\n\n* Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis (CMEMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS).\n* Korres, G., Ravdas, M., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis INTERIM (CMEMS Med-Waves, MedWAM3I system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS).\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Monthly Climatology (CMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Marine Service (CMS).\n", "extent": {"spatial": {"bbox": [[-18.125, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2026-03-31T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-multiyear-wav-006-012", "multi-year", "numerical-model", "oceanographic-geographical-features", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "significant-wave-height-(swh)", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "HCMR (Greece)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00376", "title": "Mediterranean Sea Waves Reanalysis"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 \u2013 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). \n\n**CMEMS KEY FINDINGS**\n\nIn 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia).  Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm \u201cGloria\u201d (19/1 \u2013 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 \u2013 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 \u2013 18/11) which also affected the Balearic islands and the Algerian coast and; storm Herv\u00e9 (2/2 \u2013 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 \u2013 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean  They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00262\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Amores, A., Marcos, M., Carri\u00f3, Di.S., Gomez-Pujol, L., 2020. Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean. Nat. Hazards Earth Syst. Sci. 20, 1955\u20131968. doi:10.5194/nhess-20-1955-2020\n* Chronis T, Papadopoulos V, Nikolopoulos EI. 2011. QuickSCAT observations of extreme wind events over the Mediterranean and Black Seas during 2000-2008. Int J Climatol. 31: 2068\u20132077.\n* Copernicus: Climate Change Service. 2020a (Last accessed July 2021): URL: https://surfobs.climate.copernicus.eu/stateoftheclimate/march2020.php\n* Copernicus, Copernicus Marine Service. 2020b (Last accessed July 2021): URL: https://marine.copernicus.eu/news/following-cyclone-ianos-across-mediterranean-sea\n* de Alfonso, M., Lin-Ye, J., Garc\u00eda-Valdecasas, J.M., P\u00e9rez-Rubio, S., Luna, M.Y., Santos-Mu\u00f1oz, D., Ruiz, M.I., P\u00e9rez-G\u00f3mez, B., \u00c1lvarez-Fanjul, E., 2021. Storm Gloria: Sea State Evolution Based on in situ Measurements and Modeled Data and Its Impact on Extreme Values. Front. Mar. Sci. 8, 1\u201317. doi:10.3389/fmars.2021.646873\n* Drobinski P, Alpert P, Cavicchia L, Flaoumas E, Hochman A, Kotroni V. 2018. Strong winds Observed trends, future projections, Sub-chapter 1.3.2, p. 115-122. In: Moatti JP, Thi\u00e9bault S (dir.). The Mediterranean region under climate change: A scientific update. Marseille: IRD \u00c9ditions.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2014. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2014 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Kotroni V, Lagouvardos K, Lalas D. 2001. The effect of the island of Crete on the Etesian winds over the Aegean Sea. Q J R Meteorol Soc. 127: 1917\u20131937. doi:10.1002/qj.49712757604\n* Lionello P, Rizzoli PM, Boscolo R. 2006. Mediterranean climate variability, developments in earth and environmental sciences. Elsevier.\n* Menendez M, Garc\u00eda-D\u00edez M, Fita L, Fern\u00e1ndez J, M\u00e9ndez FJ, Guti\u00e9rrez JM. 2014. High-resolution sea wind hindcasts over the Mediterranean area. Clim Dyn. 42:1857\u20131872.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sartini L, Besio G, Cassola F. 2017. Spatio-temporal modelling of extreme wave heights in the Mediterranean Sea. Ocean Modelling, 117, 52-69.\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Wikiwand: 2019 - 20 European windstorm season. URL: https://www.wikiwand.com/en/2019%E2%80%9320_European_windstorm_season\n* Zacharioudaki A, Korres G, Perivoliotis L, 2015. Wave climate of the Hellenic Seas obtained from a wave hindcast for the period 1960\u20132001. Ocean Dynamics. 65: 795\u2013816. https://doi.org/10.1007/s10236-015-0840-z\n", "extent": {"spatial": {"bbox": [[-18.125, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-omi-seastate-extreme-var-swh-mean-and-anomaly", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00262", "title": "Mediterranean Sea Significant Wave Height extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid \u201880s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend.\nThe Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14\u00b0C in the Northwestern part of the basin to ~23\u00b0C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80\u2019s as shown in Marb\u00e0 et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 \u00b0C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23\u201325 \u00b0C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016.\nThe 2020      Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3\u00b0C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion      and North Aegean Sea     . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020      is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected      by the different dataset used to evaluate this anomaly map: the 2020      Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the      Reanalysis product which, among the others, is characterized by different atmospheric forcing).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00266\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Darmaraki S, Somot S, Sevault F, Nabat P, Cabos W, Cavicchia L, et al. 2019. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371\u20131392. doi: 10.1007/s00382-019-04661-z\n* Ducrocq V., Drobinski P., Gualdi S., Raimbault P. 2016. The water cycle in the Mediterranean. Chapter 1.2.1 in The Mediterranean region under climate change. IRD E\u0301ditions. DOI : 10.4000/books.irdeditions.22908.\n* Marb\u00e0 N, Jord\u00e0 G, Agust\u00ed S, Girard C, Duarte CM. 2015. Footprints of climate change on Mediterranean Sea biota. Front.Mar.Sci.2:56. doi: 10.3389/fmars.2015.00056\n* Mariotti A and Dell\u2019Aquila A. 2012. Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn. 38,1129\u20131145. doi:10.1007/s00382-011-1056-7\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-zP\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sens. 2020, 12, 132.\n", "extent": {"spatial": {"bbox": [[-17.29166603088379, 30.1875, 36.29166793823242, 45.97916793823242]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-omi-tempsal-extreme-var-temp-mean-and-anomaly", "multi-year", "numerical-model", "oceanographic-geographical-features", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00266", "title": "Mediterranean Sea Surface Temperature extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_area_averaged_anomalies product for 2024 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nOver the past four decades (1982\u20132024), Sea Surface Temperature (SST) in the Mediterranean Sea has increased at an average rate of 0.032\u202f\u00b1\u202f0.001\u202f\u00b0C per year, resulting in a total warming of approximately 1.4\u202f\u00b0C, in line with findings from previous studies (e.g., Pisano et al., 2020; Pastor et al., 2020).\nIn 2024, the Mediterranean Sea continued to experience the intense SST warming that began in May 2022 (e.g., Mart\u00ednez et al., 2023; Marullo et al., 2023). The annual mean SST reached 21.4\u202f\u00b0C, which is 1.2\u202f\u00b0C above the 1991\u20132020 climatological average of 20.2\u202f\u00b0C, marking the highest annual value in the observational record.\nThe year was characterized by a pronounced temperature rise, with a minimum basin-average SST of 16.5\u202f\u00b0C in February and a maximum of 28\u202f\u00b0C in August \u2014 the highest monthly mean SST recorded in the region since 1982.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00268\n\n**References:**\n\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Olita, A., Sorgente, R., Natale, S., Gaber\u0161ek, S., Ribotti, A., Bonanno, A., & Patti, B. (2007). Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Science, 3(2), 273-289.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-omi-tempsal-sst-area-averaged-anomalies", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00268", "title": "Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"description": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1982-2024 (\u00b0C/year). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nOver the past four decades (1982-2024), the Mediterranean Sea surface temperature (SST) warmed at a rate of 0.032 \u00b1 0.001 \u00b0C per year, corresponding to a mean surface temperature warming of about 1.7 \u00b0C. The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 \u00b0C/year to 0.05 \u00b0C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 \u00b0C/year to 0.05 \u00b0C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend ranges between 0.02 and 0.03 \u00b0C/year. The SST warming rate spatial change shows an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00269\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734 Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Kirtman, B., Power, S. B, Adedoyin, J. A., Boer, G. J., Bojariu, R. et al., 2013. Near-term climate change: Projections and Predictability. In: Stocker, T.F., et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "extent": {"spatial": {"bbox": [[-5.563465118408203, 30.125, 36.32500076293945, 46.025001525878906]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["change-over-time-in-sea-surface-temperature", "coastal-marine-environment", "marine-resources", "marine-safety", "mediterranean-sea", "medsea-omi-tempsal-sst-trend", "multi-year", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00269", "title": "Mediterranean Sea Surface Temperature trend map from Observations Reprocessing"}, "MULTIOBS_GLO_BGC_CARBON_SURFACE_MYNRT_015_008": {"description": "This product corresponds to a L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al.  2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["aragonite-saturation-state-in-sea-water", "calcite-saturation-state-in-sea-water", "coastal-marine-environment", "dissolved-inorganic-carbon-in-sea-water", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-bgc-carbon-surface-mynrt-015-008", "none", "oceanographic-geographical-features", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "total-alkalinity-in-sea-water", "uncertainty-aragonite-saturation-state-in-sea-water", "uncertainty-calcite-saturation-state-in-sea-water", "uncertainty-dissolved-inorganic-carbon-in-sea-water", "uncertainty-sea-water-ph-reported-on-total-scale", "uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water", "uncertainty-total-alkalinity-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00047", "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"description": "This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON is based on a neural network trained using nutrient data (GLODAPv2 database)\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00048\n\n**References:**\n\n* Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault, J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. doi: 10.3389/fmars.2017.00128.\n* Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauz\u00e8de, A. K\u00f6rtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "dissolved-inorganic-carbon-in-sea-water", "global-ocean", "in-situ-observation", "level-3", "marine-resources", "marine-safety", "moles-of-nitrate-per-unit-mass-in-sea-water", "moles-of-oxygen-per-unit-mass-in-sea-water", "moles-of-phosphate-per-unit-mass-in-sea-water", "moles-of-silicate-per-unit-mass-in-sea-water", "multi-year", "multiobs-glo-bgc-nutrients-carbon-profiles-mynrt-015-009", "none", "oceanographic-geographical-features", "partial-pressure-of-carbon-dioxide-in-sea-water", "sea-water-ph-reported-on-total-scale", "sea-water-pressure", "sea-water-salinity", "sea-water-temperature", "total-alkalinity-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "LOV (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00048", "title": "Nutrient and carbon profiles vertical distribution"}, "MULTIOBS_GLO_BGC_SURFACE_NRT_015_016": {"description": "You can find here the Multi Observation Sargassum floating algae detection index product.\nIt contains 8 NRT datasets of information of the presence of sargassum algae, over 3 domains (Caribbean Seas, North Brazilian Shelve and Northern Tropical Atlantic) on regular grids from 20 meters to 1.1 kilometer resolution, of  10-minutes instantaneous, daily aggregated and weekly mean data on a daily basis.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00363\n\n**References:**\n\n* Stum, J., Tebri, H., Sutton, M., Granier, N., 2019. NRT satellite detection and drift forecast of Sargassum algae in the equatorial Atlantic. [Poster Presentation]. 4th International Ocean Color Science meeting, Busan, South Korea, 9-12 April 2019\n", "extent": {"spatial": {"bbox": [[-100, -5, 13, 40]]}, "temporal": {"interval": [["2023-11-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "floating-algae-index-anomaly", "global-ocean", "level-4", "marine-resources", "marine-safety", "maximum-chlorophyll-index", "multiobs-glo-bgc-surface-nrt-015-016", "near-real-time", "none", "normalized-floating-algae-index", "normalized-floating-algae-index-weekly-mean", "oceanographic-geographical-features", "satellite-observation", "status-flag", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00363", "title": "Floating Sargassum algae detection Index from satellite observations"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"description": "This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp), Chlorophyll-a concentration (Chla), Downwelling Photosynthetic Available Radiation (PAR) and downwelling irradiance, at 0.25\u00b0x0.25\u00b0 resolution from the surface to 1000 m. \nA neural network estimates the vertical distribution of Chla and bbp from surface ocean color measurements with hydrological properties and additional drivers. The SOCA-light models is used to integrate light.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00046\n\n**References:**\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall\u2019Olmo, F. D\u2019Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408.\n* Renosh, P. R., Zhang, J., Sauz\u00e8de, R., & Claustre, H., 2023: Vertically Resolved Global Ocean Light Models Using Machine Learning. Remote Sensing, 15(24), 5663. https://doi.org/10.3390/RS15245663/S1\n", "extent": {"spatial": {"bbox": [[-179.875, -82.125, 179.875, 89.875]]}, "temporal": {"interval": [["1998-01-01T00:00:00Z", "2023-12-27T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "downwelling-photosynthetic-photon-flux-in-sea-water", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water", "multi-year", "multiobs-glo-bio-bgc-3d-rep-015-010", "none", "oceanographic-geographical-features", "satellite-observation", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "LOV (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00046", "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient, Particulate Organic Carbon, Downwelling Photosynthetic Available Radiation and downwelling irradiance at three different wavelengths (ED380, ED412 and ED490)"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"description": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al.  2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2026-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "dissolved-inorganic-carbon-in-sea-water", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-bio-carbon-surface-mynrt-015-008", "none", "oceanographic-geographical-features", "sea-water-ph-reported-on-total-scale", "surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "total-alkalinity-in-sea-water", "uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon", "uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00047", "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"description": "This product is a  L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS  satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00327\n\n**References:**\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-05-09T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "eastward-sea-water-velocity", "eastward-sea-water-velocity-due-to-ekman-drift", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-mynrt-015-003", "near-real-time", "none", "northward-sea-water-velocity", "northward-sea-water-velocity-due-to-ekman-drift", "numerical-model", "oceanographic-geographical-features", "satellite-observation", "sea-water-x-velocity-due-to-tide", "sea-water-y-velocity-due-to-tide", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00327", "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"description": "The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00368\n\n**References:**\n\n* Boutin, J., J. L. Vergely, S. Marchand, F. D'Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin, and J. Vialard (2018), New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sensing of Environment, 214, 115-134. doi:https://doi.org/10.1016/j.rse.2018.05.022\n* Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin (2016), Mitigation of systematic errors in SMOS sea surface salinity, Remote Sensing of Environment, 180, 164-177. doi:https://doi.org/10.1016/j.rse.2016.02.061\n", "extent": {"spatial": {"bbox": [[-180, -83.6, 179.79999999997955, 83.60000000000238]]}, "temporal": {"interval": [["2010-01-12T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "level-3", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-sss-l3-mynrt-015-014", "near-real-time", "none", "oceanographic-geographical-features", "satellite-observation", "sea-surface-salinity", "sea-surface-salinity-error", "sea-surface-salinity-qc", "sea-surface-salinity-rain-corrected-error", "sea-surface-salinity-sain-corrected", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CATDS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00368", "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"description": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00369\n\n**References:**\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n", "extent": {"spatial": {"bbox": [[-179.870316, -83.6, 179.87434342382812, 83.60000000000238]]}, "temporal": {"interval": [["2010-06-03T00:00:00Z", "2025-06-26T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-sss-l4-my-015-015", "near-real-time", "none", "oceanographic-geographical-features", "satellite-observation", "sea-surface-density", "sea-surface-salinity", "sea-surface-temperature", "sea-water-conservative-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CATDS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00369", "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"description": "This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00051\n\n**References:**\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993\u20132016), Front. Mar. Sci., 5(March), 1\u201313, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data, Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-05-05T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-s-surface-mynrt-015-013", "near-real-time", "none", "oceanographic-geographical-features", "satellite-observation", "sea-surface-density", "sea-surface-salinity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00051", "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"description": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year processing. This is 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. These are  NRT and MY  datasets of monthly and daily mean together with climatological uncertainties. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n**References:**\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n", "extent": {"spatial": {"bbox": [[-179.9375, -82.1875, 179.9375, 89.9375]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-05-05T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "geopotential-height", "geostrophic-eastward-sea-water-velocity", "geostrophic-northward-sea-water-velocity", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-tsuv-3d-mynrt-015-012", "numerical-model", "ocean-mixed-layer-thickness", "oceanographic-geographical-features", "satellite-observation", "sea-water-salinity", "sea-water-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00052", "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007": {"description": "The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00053\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n", "extent": {"spatial": {"bbox": [[-179.875, -82.125, 179.875, 89.875]]}, "temporal": {"interval": [["1993-01-06T00:00:00Z", "2026-04-29T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "eastward-sea-water-velocity", "global-ocean", "in-situ-observation", "level-4", "marine-resources", "marine-safety", "multi-year", "multiobs-glo-phy-uvw-3d-mynrt-015-007", "northward-sea-water-velocity", "numerical-model", "oceanographic-geographical-features", "satellite-observation", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00053", "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013).\nTwo parameters are included on this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThis domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself.\n\n**CMEMS KEY FINDINGS**\n\nThe 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product)**\nhttps://doi.org/10.48670/moi-00273\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Elliott, A.J., Clarke, T., Li, ., 1990: Monthly distributions of surface and bottom temperatures in the northwest European shelf seas. Continental Shelf Research, Vol 11, no 5, pp 453-466, http://doi.org/10.1016/0278-4343(91)90053-9\n* Holt, J., Hughes, S., Hopkins, J., Wakelin, S., Holliday, P.N., Dye, S., Gonz\u00e1lez-Pola, C., Hj\u00f8llo, S., Mork, K., Nolan, G., Proctor, R., Read, J., Shammon, T., Sherwin, T., Smyth, T., Tattersall, G., Ward, B., Wiltshire, K., 2012: Multi-decadal variability and trends in the temperature of the northwest European continental shelf: A model-data synthesis. Progress in Oceanography, 96-117, 106, http://doi.org/10.1016/j.pocean.2012.08.001\n* Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Sparks, T. and Garforth, J. (2021), State of the UK Climate 2020. Int J Climatol, 41 (Suppl 2): 1-76. https://doi.org/10.1002/joc.7285\n* Liu, M., Tanhua, T., 2021: Water masses in the Atlantic Ocean: characteristics and distributions. Ocean Sci, 17, 463-486, http://doi.org/10.5194/os-17-463-2021\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[-16, 46, 13, 62.74324035644531]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "northwestshelf-omi-tempsal-extreme-var-temp-mean-and-anomaly", "numerical-model", "oceanographic-geographical-features", "temp-percentile99-anom", "temp-percentile99-mean", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Puertos Del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00273", "title": "North West Shelf Sea Surface Temperature extreme from Reanalysis"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"description": "The NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled hydrodynamic-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at about 7 km of horizontal resolution and 24 vertical levels.\nThe product is daily, providing 7-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00056", "extent": {"spatial": {"bbox": [[-19.88888931274414, 40.06666564941406, 12.99967098236084, 65.00125122070312]]}, "temporal": {"interval": [["2019-05-01T00:00:00Z", "2026-05-17T00:00:00Z"]]}}, "keywords": ["chl", "coastal-marine-environment", "e1t", "e2t", "e3t", "euphotic-zone-depth", "forecast", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mole-concentration-of-ammonium-in-sea-water", "mole-concentration-of-dissolved-inorganic-carbon-in-sea-water", "mole-concentration-of-dissolved-iron-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "mole-concentration-of-silicate-in-sea-water", "mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water", "near-real-time", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "north-west-shelf-seas", "numerical-model", "nwshelf-analysisforecast-bgc-004-002", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-water-ph-reported-on-total-scale", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00056", "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"description": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a coupled hydrodynamic-wave model system with tides, implemented over the North East Atlantic and Shelf Seas at 1.5 km of horizontal resolution and 33 vertical levels.\nThe product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00054", "extent": {"spatial": {"bbox": [[-16, 46, 13, 62.74324035644531]]}, "temporal": {"interval": [["2021-09-01T00:00:00Z", "2026-05-18T00:00:00Z"]]}}, "keywords": ["-eastward-sea-water-velocity-assuming-no-tide", "barotropic-eastward-sea-water-velocity", "barotropic-northward-sea-water-velocity", "coastal-marine-environment", "e1t", "e2t", "e3t", "eastward-sea-water-velocity", "forecast", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "near-real-time", "north-west-shelf-seas", "northward-sea-water-velocity", "northward-sea-water-velocity-assuming-no-tide", "numerical-model", "nwshelf-analysisforecast-phy-004-013", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-level", "sea-surface-height-above-geoid", "sea-surface-height-above-geoid-assuming-no-tide", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "upward-sea-water-velocity", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00054", "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_LR_004_001": {"description": "The NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 is produced by a coupled hydrodynamic-biogeochemical model system with tides, implemented over the North East Atlantic and Shelf Seas at 7 km of horizontal resolution and 24 vertical levels.\nThe product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00367", "extent": {"spatial": {"bbox": [[-19.88888931274414, 40.06666564941406, 12.999671936035156, 65.00125122070312]]}, "temporal": {"interval": [["2023-09-29T00:00:00Z", "2026-05-17T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "depth", "eastward-sea-water-velocity", "forecast", "itsp", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "near-real-time", "north-west-shelf-seas", "northward-sea-water-velocity", "numerical-model", "nwshelf-analysisforecast-phy-lr-004-001", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-height-above-geoid", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sl", "sst", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00367", "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast (Low Resolution)"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"description": "The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a coupled hydrodynamic-wave model system, implemented over the North East Atlantic and Shelf Seas at 1.5 km of horizontal resolution. The product is updated daily, providing 7-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00055\n\n**References:**\n\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n", "extent": {"spatial": {"bbox": [[-16, 46, 13, 62.74324035644531]]}, "temporal": {"interval": [["2022-10-06T00:00:00Z", "2026-05-17T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "e1t", "e2t", "forecast", "level-4", "marine-resources", "marine-safety", "near-real-time", "none", "north-west-shelf-seas", "numerical-model", "nwshelf-analysisforecast-wav-004-014", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-maximum-crest-height", "sea-surface-wave-maximum-height", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00055", "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"description": "**Short  Description:**\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butensch&ouml;n et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually,  providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for details.\n\n**Associated products:**\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00058\n\n**References:**\n\n* Ciavatta, S., Brewin, R. J. W., Sk\u00e1kala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean\u2010color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834\u2013854. https://doi.org/10.1002/2017JC013490\n", "extent": {"spatial": {"bbox": [[-19.88888931274414, 40.06666946411133, 12.999670028686523, 65.00125122070312]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-02-28T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mole-concentration-of-dissolved-molecular-oxygen-in-sea-water", "mole-concentration-of-nitrate-in-sea-water", "mole-concentration-of-phosphate-in-sea-water", "mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water", "multi-year", "net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water", "north-west-shelf-seas", "numerical-model", "nwshelf-multiyear-bgc-004-011", "oceanographic-geographical-features", "satellite-chlorophyll", "sea-water-ph-reported-on-total-scale", "surface-partial-pressure-of-carbon-dioxide-in-sea-water", "volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00058", "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"description": "**Short  Description:**\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution.  \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for further details.\n\n**Associated products:**\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00059", "extent": {"spatial": {"bbox": [[-19.88888931274414, 40.06666564941406, 12.999671936035156, 65.00125122070312]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-03-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "depth", "eastward-sea-water-velocity", "in-situ-ts-profiles", "level-4", "marine-resources", "marine-safety", "model-level-number-at-sea-floor", "multi-year", "north-west-shelf-seas", "northward-sea-water-velocity", "numerical-model", "nwshelf-multiyear-phy-004-009", "ocean-mixed-layer-thickness-defined-by-sigma-theta", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-height-above-geoid", "sea-water-potential-temperature", "sea-water-potential-temperature-at-sea-floor", "sea-water-salinity", "sst", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00059", "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "NWSHELF_REANALYSIS_WAV_004_015": {"description": "**Short  description:**\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**Associated products:**\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00060", "extent": {"spatial": {"bbox": [[-16, 46, 13, 62.74384689331055]]}, "temporal": {"interval": [["1980-01-01T00:00:00Z", "2025-12-31T21:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "depth", "level-4", "marine-resources", "marine-safety", "multi-year", "none", "north-west-shelf-seas", "numerical-model", "nwshelf-reanalysis-wav-004-015", "oceanographic-geographical-features", "sea-binary-mask", "sea-floor-depth-below-geoid", "sea-surface-primary-swell-wave-from-direction", "sea-surface-primary-swell-wave-mean-period", "sea-surface-primary-swell-wave-significant-height", "sea-surface-secondary-swell-wave-from-direction", "sea-surface-secondary-swell-wave-mean-period", "sea-surface-secondary-swell-wave-significant-height", "sea-surface-wave-from-direction", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment", "sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "sea-surface-wave-stokes-drift-x-velocity", "sea-surface-wave-stokes-drift-y-velocity", "sea-surface-wind-wave-from-direction", "sea-surface-wind-wave-mean-period", "sea-surface-wind-wave-significant-height", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NWS-HEREON-GEESTHACHT-DE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00060", "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.    \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00061\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-arc-bgc-hr-l3-nrt-009-201", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00061", "title": "Arctic Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00062\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-arc-bgc-hr-l4-nrt-009-207", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00062", "title": "Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"description": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams:  OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **4 km** (multi) or **300 m** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00292", "extent": {"spatial": {"bbox": [[-180, 52.005, 179.9970703125, 90.03000000000091]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2035-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "chl", "coastal-marine-environment", "kd490", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "multi-year", "oceancolour-arc-bgc-l3-my-009-123", "oceanographic-geographical-features", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs560", "rrs620", "rrs665", "rrs674", "rrs681", "rrs709", "satellite-observation", "spm", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00292", "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"description": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Suspended Matter (**SPM**), Diffuse Attenuation (**KD490**), Detrital and Dissolved Material Absorption Coef. (**ADG443_), Phytoplankton Absorption Coef. (**APH443_), Total Absorption Coef. (**ATOT443_) and  Reflectance (**RRS_').\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions:  **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00290", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "near-real-time", "oceancolour-arc-bgc-l3-nrt-009-121", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00290", "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"description": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products , and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**)\n\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi) or **300 meters** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00293", "extent": {"spatial": {"bbox": [[-180, 66, 179.9100000000136, 90.00000000000091]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "chl", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "multi-year", "oceancolour-arc-bgc-l4-my-009-124", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00293", "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"description": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**) and Diffuse Attenuation (**KD490**).\n\n* Temporal resolutions:**monthly**.\n* Spatial resolutions:  **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00291", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "near-real-time", "oceancolour-arc-bgc-l4-nrt-009-122", "oceanographic-geographical-features", "satellite-observation", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00291", "title": "Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"description": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the '''olci\"' products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword '''GlobColour\"'. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00286\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-45.99721908569336, 20.002777099609375, 12.99722671508789, 65.99722290039062]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["bbp", "cdm", "chl", "coastal-marine-environment", "global-ocean", "kd490", "level-3", "magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "multi-year", "oceancolour-atl-bgc-l3-my-009-113", "oceanographic-geographical-features", "pft", "rr555", "rr560", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs670", "rrs674", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "spm", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting", "zsd"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00286", "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"description": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n\n\n* Temporal resolutions: **daily**.\n\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00284\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127 *Sieburth, J. M.,\n* Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-45.99721908569336, 20.002777099609375, 12.99722671508789, 65.99722290039062]]}, "temporal": {"interval": [["2023-04-21T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["bbp", "cdm", "chl", "coastal-marine-environment", "global-ocean", "kd490", "level-3", "magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "near-real-time", "oceancolour-atl-bgc-l3-nrt-009-111", "oceanographic-geographical-features", "pft", "rr555", "rr560", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs670", "rrs674", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "spm", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting", "zsd"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00284", "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"description": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n\n* Spatial resolutions: **1 km**.\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00289\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n", "extent": {"spatial": {"bbox": [[-45.99479293823242, 20.005207061767578, 12.994793891906738, 65.99478912353516]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["chl", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "multi-year", "oceancolour-atl-bgc-l4-my-009-118", "oceanographic-geographical-features", "pft", "pp", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00289", "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"description": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a **cloud free** product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00288\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n", "extent": {"spatial": {"bbox": [[-45.99479293823242, 20.005207061767578, 12.994793891906738, 65.99478912353516]]}, "temporal": {"interval": [["2023-04-27T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["chl", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "near-real-time", "oceancolour-atl-bgc-l4-nrt-009-116", "oceanographic-geographical-features", "pft", "pp", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00288", "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day. The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. \n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 32 NetCDF datasets for (1) optics and (2) transparency, suspended matter and chlorophyll concentration respectively per month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for optics (BBP443 only), and (2) transparency, suspended matter and chlorophyll concentration and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m\n*cmems_obs_oc_bal_bgc_optics_nrt_l3-hr-mosaic_P1D-v01\n\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00079\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[8.999825296995105, 53.00046296296296, 31.000174703004888, 65.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-bal-bgc-hr-l3-nrt-009-202", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00079", "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). he 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The geophysical product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for (1) optics and (2) turbidity, suspended matter and chlorophyll concentration, respectively for the month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for (1) optics (BBP443 only), (2) turbidity, suspended matter and chlorophyll concentration per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_bal_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_bal_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr-mosaic_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00080\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[8.999825296995105, 53.00046296296296, 31.000174703004888, 65.99953703703704]]}, "temporal": {"interval": [["2020-01-08T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-bal-bgc-hr-l4-nrt-009-208", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles-443", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00080", "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"description": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00296\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "extent": {"spatial": {"bbox": [[9.252659797668457, 53.251346588134766, 30.24734115600586, 65.8486557006836]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "multi-year", "oceancolour-bal-bgc-l3-my-009-133", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00296", "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"description": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00294\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n", "extent": {"spatial": {"bbox": [[9.252659797668457, 53.251346588134766, 30.24734115600586, 65.8486557006836]]}, "temporal": {"interval": [["2023-04-18T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "near-real-time", "oceancolour-bal-bgc-l3-nrt-009-131", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00294", "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"description": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00308\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "extent": {"spatial": {"bbox": [[9.252659797668457, 53.251346588134766, 30.24734115600586, 65.8486557006836]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "chl", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "multi-year", "oceancolour-bal-bgc-l4-my-009-134", "oceanographic-geographical-features", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00308", "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"description": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: monthly \n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00295\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "extent": {"spatial": {"bbox": [[9.252659797668457, 53.251346588134766, 30.24734115600586, 65.8486557006836]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "near-real-time", "oceancolour-bal-bgc-l4-nrt-009-132", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00295", "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day. The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. \n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 32 NetCDF datasets for (1) optics and (2) transparency, suspended matter and chlorophyll concentration respectively per month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 32 datasets for optics (BBP443 only), and (2) transparency, suspended matter and chlorophyll concentration and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m\n*cmems_obs_oc_blk_bgc_optics_nrt_l3-hr-mosaic_P1D-v01\n\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00086\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[26.000661375661405, 40.00046296296296, 41.999338624338655, 47.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-blk-bgc-hr-l3-nrt-009-206", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00086", "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). he 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for (1) optics and (2) turbidity, suspended matter and chlorophyll concentration, respectively for the month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for (1) optics (BBP443 only), and (2) turbidity, suspended mattr and chlorophyll concentration per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_blk_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_blk_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr-mosaic_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00087\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[26.000661375661405, 40.00046296296296, 41.999338624338655, 47.99953703703704]]}, "temporal": {"interval": [["2020-01-02T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-blk-bgc-hr-l4-nrt-009-212", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles-443", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00087", "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"description": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_BLK_BGC_L3_MY\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00303\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "extent": {"spatial": {"bbox": [[26.501876831054688, 40.00135040283203, 41.99812316894531, 47.99864959716797]]}, "temporal": {"interval": [["1997-09-16T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "multi-year", "oceancolour-blk-bgc-l3-my-009-153", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00303", "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"description": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00301\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "extent": {"spatial": {"bbox": [[26.501876831054688, 40.00135040283203, 41.99812316894531, 47.99864959716797]]}, "temporal": {"interval": [["2023-04-29T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "near-real-time", "oceancolour-blk-bgc-l3-nrt-009-151", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00301", "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"description": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"**, **\"\"pp\"\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00304\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "extent": {"spatial": {"bbox": [[26.501876831054688, 40.00135040283203, 41.99812316894531, 47.99864959716797]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2026-04-30T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "multi-year", "oceancolour-blk-bgc-l4-my-009-154", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00304", "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"description": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00302\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "extent": {"spatial": {"bbox": [[26.501876831054688, 40.00135040283203, 41.99812316894531, 47.99864959716797]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "near-real-time", "oceancolour-blk-bgc-l4-nrt-009-152", "oceanographic-geographical-features", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00302", "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"description": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n\n* Temporal resolutions: **daily**.\n\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00280\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-179.99722290039062, -89.99722290039062, 179.9972381591797, 89.99722290039062]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["chl", "coastal-marine-environment", "global-ocean", "level-3", "magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "multi-year", "oceancolour-glo-bgc-l3-my-009-103", "oceanographic-geographical-features", "rr560", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs674", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00280", "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"description": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00282", "extent": {"spatial": {"bbox": [[-179.97916666666666, -89.97916666666666, 179.97916666666663, 89.97916666666667]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "multi-year", "oceancolour-glo-bgc-l3-my-009-107", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00282", "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"description": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00278\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-179.99722290039062, -89.99722290039062, 179.9972381591797, 89.99722290039062]]}, "temporal": {"interval": [["2023-04-25T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-3", "magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "near-real-time", "oceancolour-glo-bgc-l3-nrt-009-101", "oceanographic-geographical-features", "rr560", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs674", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00278", "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"description": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n\n\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n\n\n\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n\n\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00281\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-179.99722290039062, -89.99722290039062, 179.9972381591797, 89.99722290039062]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["chl", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "multi-year", "oceancolour-glo-bgc-l4-my-009-104", "oceanographic-geographical-features", "primary-production-of-biomass-expressed-as-carbon", "rr560", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs674", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00281", "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"description": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP,  OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**).\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00283", "extent": {"spatial": {"bbox": [[-179.9791717529297, -89.97916412353516, 179.9791717529297, 89.97916412353516]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "multi-year", "oceancolour-glo-bgc-l4-my-009-108", "oceanographic-geographical-features", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00283", "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"description": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a **cloud free** product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00279\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n", "extent": {"spatial": {"bbox": [[-179.99722290039062, -89.99722290039062, 179.9972381591797, 89.99722290039062]]}, "temporal": {"interval": [["2023-04-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["674", "chl", "coastal-marine-environment", "global-ocean", "kd490", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-suspended-particulate-matter-in-sea-water", "near-real-time", "oceancolour-glo-bgc-l4-nrt-009-102", "oceanographic-geographical-features", "pft", "primary-production-of-biomass-expressed-as-carbon", "rr560", "rrs", "rrs400", "rrs412", "rrs443", "rrs490", "rrs510", "rrs620", "rrs665", "rrs681", "rrs709", "satellite-observation", "secchi-depth-of-sea-water", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting", "zsd"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "ACRI (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00279", "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day. The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. \n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 32 NetCDF datasets for (1) optics and (2) transparency, suspended matter and chlorophyll concentration respectively per month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 32 datasets for optics (BBP443 only), and (2) transparency, suspended matter and chlorophyll concentration and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n\n*cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m\n*cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr-mosaic_P1D-v01\n\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00107\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-19.999338624338595, 26.000462962962963, -0.0006613756613447208, 47.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-ibi-bgc-hr-l3-nrt-009-204", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00107", "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). he 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for (1) optics and (2) turbidity, suspended matter and chlorophyll concentration, respectively for the month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for (1) optics (BBP443 only) and (2) turbidity, suspended mattr and chlorophyll concentration per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n*cmems_obs_oc_ibi_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_ibi_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr-mosaic_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00108\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-19.999338624338595, 26.000462962962963, -0.0006613756613447208, 47.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-ibi-bgc-hr-l4-nrt-009-210", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles-443", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00108", "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'tur_tsm_chl' products include TUR, SPM and CHL). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day. The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. \n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month. The output is a set of 32 NetCDF datasets for (1) optics and (2) transparency, suspended matter and chlorophyll concentration respectively per month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 32 datasets for optics (BBP443 only), and (2) transparency, suspended matter and chlorophyll concentration and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m\n*cmems_obs_oc_med_bgc_optics_nrt_l3-hr-mosaic_P1D-v01\n\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00109\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-5.999338624338596, 30.000462962962963, 36.999338624338655, 45.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "mediterranean-sea", "near-real-time", "oceancolour-med-bgc-hr-l3-nrt-009-205", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00109", "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). he 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for (1) optics and (2) turbidity, suspended matter and chlorophyll concentration, respectively for the month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for (1) optics (BBP443 only) and (2) turbidity, suspended mattr and chlorophyll concentration per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n*cmems_obs_oc_med_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_med_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1D-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr-mosaic_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00110\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-5.999338624338596, 30.000462962962963, 36.999338624338655, 45.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "mediterranean-sea", "near-real-time", "oceancolour-med-bgc-hr-l4-nrt-009-211", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles-443", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00110", "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"description": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"multi**\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* **_pp**_ with the Integrated Primary Production (PP)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L3_MY\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00299\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[-5.99828577041626, 30.00135040283203, 36.498287200927734, 45.99864959716797]]}, "temporal": {"interval": [["1997-09-16T00:00:00Z", "2026-05-03T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mediterranean-sea", "multi-year", "oceancolour-med-bgc-l3-my-009-143", "oceanographic-geographical-features", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00299", "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"description": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00297\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[-5.99828577041626, 30.00135040283203, 36.498287200927734, 45.99864959716797]]}, "temporal": {"interval": [["2023-04-29T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water", "mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water", "mediterranean-sea", "near-real-time", "oceancolour-med-bgc-l3-nrt-009-141", "oceanographic-geographical-features", "satellite-observation", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles", "volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00297", "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"description": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"gap-free\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L4_MY\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00300\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[-5.99828577041626, 30.00135040283203, 36.498287200927734, 45.99864959716797]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2026-04-30T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mediterranean-sea", "multi-year", "oceancolour-med-bgc-l4-my-009-144", "oceanographic-geographical-features", "primary-production-of-biomass-expressed-as-carbon", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00300", "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"description": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00298\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[-5.99828577041626, 30.00135040283203, 36.498287200927734, 45.99864959716797]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mediterranean-sea", "near-real-time", "oceancolour-med-bgc-l4-nrt-009-142", "oceanographic-geographical-features", "satellite-observation", "volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00298", "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'tur_tsm_chl' products include TUR, SPM and CHL). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day. The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. \n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month. The output is a set of 32 NetCDF datasets for (1) optics and (2) transparency, suspended matter and chlorophyll concentration respectively per month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 2 datasets for optics (BBP443 only), and (2) transparency, suspended matter and chlorophyll concentration and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n\n*cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m\n*cmems_obs_oc_nws_bgc_optics_nrt_l3-hr-mosaic_P1D-v01\n\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00118\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-11.999475890985316, 48.00046296296296, 13.000524109014684, 61.99953703703704]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "oceancolour-nws-bgc-hr-l3-nrt-009-203", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00118", "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"description": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). BBP443, constitute the category of the 'optics' products. The  BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). he 'tur_tsm_chl' products include TUR, SPM and CHL. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of  a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account  corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for (1) optics and (2) turbidity, suspended matter and chlorophyll concentration, respectively for the month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), turbidity, suspended mattr and chlorophyll concentration per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_nws_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr-mosaic_P1M-v01\n*cmems_obs_oc_nws_bgc_tur_spm_chl_nrt_l4-hr-mosaic_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr-mosaic_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\"\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00119\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "extent": {"spatial": {"bbox": [[-11.999475890985316, 48.00046296296296, 13.000524109014684, 61.99953703703704]]}, "temporal": {"interval": [["2020-01-04T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-a-in-sea-water", "mass-concentration-of-suspended-matter-in-sea-water", "near-real-time", "north-west-shelf-seas", "oceancolour-nws-bgc-hr-l4-nrt-009-209", "oceanographic-geographical-features", "satellite-observation", "sea-water-turbidity", "surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles", "volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles-443", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00119", "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OMI_CIRCULATION_BOUNDARY_ATLANTIC_gulf_stream_destabilization_point": {"description": "**DEFINITION**\n\nThe destabilization point of the Gulf Stream marks the longitude where the current transitions  from a boundary-following current to an open-ocean jet. It is identified using the 25 cm SSH contour, from detrended Absolute Dynamic Topography (ADT), a standard method validated in multiple studies (Lillibridge & Mariano, 2013; Rossby et al., 2014; Andres, 2016; Chi et al., 2021; Guo et al., 2023). \n\nMonthly Gulf Stream paths from 1993 to  present are divided into 0.5\u00b0 longitude bins, and the northernmost latitude of the SSH contour is used to compute the latitudinal variance in each bin. The destabilization point is defined as the first longitude where this variance reaches 0.42\u00b0\u00b2, which represents half the maximum variance observed across the full time series ( S\u00e1nchez-Rom\u00e1n et al., 2024). \n\nA 95% confidence interval is computed from monthly estimates, and a 5-year running mean is applied to smooth high-frequency fluctuations. Longitudinal shifts of the destabilization point can exceed 1400 km, influenced by climate modes such as the North Atlantic Oscillation (NAO) (S\u00e1nchez-Rom\u00e1n et al., 2024). \n\n \n\n**CONTEXT**\n\nThe Gulf Stream is a major western boundary current which forms the northward flowing path of the Atlantic Meridional Overturning Circulation (AMOC). It carries near-surface warm waters from the Gulf of Mexico to the subpolar North Atlantic, playing an important role in its climate variability and change. Interannual lateral displacements in the Gulf Stream\u2019s position impact associated water transport to subpolar regions, altering the global climate system (Guo et al., 2023).  \n\nTracking the destabilization point provides an early indicator of structural changes in the North Atlantic circulation system. Its position directly reflects the length of the stable jet segment and the onset region of meanders. The observed shifts indicate modifications in the jet\u2019s structure, its stability, and its interaction with oceanic variability. Its evolution reveals a significant westward migration followed by an eastward shift over the past three decades. \n\n\n**KEY FINDINGS**\n\nThe results reveal  a low-frequency westward and southward displacement of the Gulf Stream destabilization point between 1995 and 2012, followed by a previously unreported reversed migration starting in 2013. These changes affect the intensity of mesoscale variability and the transport of waters toward the subpolar North Atlantic. They appear to be correlated with the dynamics of the North Atlantic Oscillation, whose successive phases modulate the position of the current. \n\n\n**DOI (product):**\nhttps://doi.org/10.48670/mds-00377\n\n**References:**\n\n* Andres, M. (2016), On the recent destabilization of the Gulf Stream path downstream of Cape Hatteras, Geophys. Res. Lett., 43, 9836\u20139842, doi:10.1002/2016GL069966.\n* Chi, L., Wolfe, C. L. P., and Hameed, S.: Has the Gulf Stream slowed or shifted in the altimetry era?, Geophys. Res. Lett., 48, e2021GL093113, https://doi.org/10.1029/2021GL093113, 2021.\u2002\n* Guo, Y., Bishop, S., Bryan, F., and Bachman, S.: Mesoscale variability linked to interannual displacement of Gulf Stream, Geophys. Res. Lett., 50, e2022GL102549, https://doi.org/10.1029/2022GL102549, 2023.\u2002\n* Lillibridge, J. L. and Mariano, A.J.: A statistical analysis of Gulf Stream variability from 18+ years of altimetry data, Deep-Sea Res. Pt. II, 85, 127\u2013146, https://doi.org/10.1016/j.dsr2.2012.07.034, 2013. \u2002\n* Rossby, H., Flagg, C., Donohue, K., Sanchez-Franks, A., Lillibridge, J.: On the long-term stability of Gulf Stream transport based on 20 years of direct measurements, Geophys. Res. Lett., 41, 114\u2013120, https://doi.org/10.1002/2013GL058636, 2014.\u2002\n* S\u00e1nchez-Rom\u00e1n, A., Gues, F., Bourdalle-Badie, R., Pujol, M.-I., Pascual, A., & Dr\u00e9villon, M. (2024, September 30). Changes in the Gulf Stream path over the last 3 decades. State of the Planet. Copernicus GmbH. http://doi.org/10.5194/sp-4-osr8-4-2024\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2025-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "latitude", "level-2", "longitude", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-circulation-boundary-atlantic-gulf-stream-destabilization-point", "satellite-observation", "time", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 2", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00377", "title": "Gulf Stream destabilization point"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"description": "**DEFINITION**\n\nThe Black Sea Rim Current Index (BSRCI) is an ocean monitoring indicator (OMI) that reflects the intensity of the Rim Current \u2013 one of the main features of Black Sea circulation and a basin-scale cyclonic current. The index is computed using sea surface current speed averaged over two areas of intense currents based on the Black Sea reanalysis data. These areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the both sections (BSRCI). The formulation used to estimate both indices is provided in the Quality Information Document (QUID) of the corresponding OMI.\nIn general, BSRCI is defined as the relative annual anomaly from the long-term mean speed.  An index close to zero means close to the average conditions; a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc.\nThe BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). \nThe Black Sea Physics Reanalysis Product (BLKSEA_MULTIYEAR_PHY_007_004) has been used as a database to build the index. Details on the products are delivered in the PUM and QUID of this OMI.\n\n**CONTEXT**\n\nThe Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. The Rim Current is quasi-geostrophic in nature, and its dynamics are approximately governed by the Sverdrup balance. Its position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). \nConsequently, this impacts the vertical stratification, Cold Intermediate Layer, water masses formation, the biological activity distribution and the coastal mesoscale eddies\u2019 propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, and intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally \u2013 by the mesoscale eddies. \nSeveral recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. \nTo summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus, our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. \n\n**KEY FINDINGS**\n\nThe Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value \u2013 weaker circulation than usual. The time-series of the BSRCI suggests that the Black Sea Rim current speed varies within ~30% in the period 1993-2024. During the first two decades, a peak is observed in 2005, while higher values become more frequent in recent years, particularly in 2014, 2022, and 2024, when the BSRCI reaches values around 0.15. In contrast, minimum values are recorded in 2004, 2013, 2016, and 2019. The general tendency of the BSRCI time series is positive, e.g. to intensification of the circumpolar current. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00326\n\n**References:**\n\n* Fach, B., (2015), Modeling the Influence of Hydrodynamic Processes on Anchovy Distribution and Connectivity in the Black Sea, Turkish Journal of Fisheries and Aquatic Sciences 14: 1-2, doi: 10.4194/1303-2712-v14_2_06\n* Korotenko KA. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts. PeerJ. 2018 Aug 29;6:e5448. doi: 10.7717/peerj.5448. PMID: 30186680; PMCID: PMC6119461.\n* Miladinova S, D. Macias, A. Stips, E. Garcia-Gorriz, (2020), Identifying distribution and accumulation patterns of floating marine debris in the Black Sea, Marine Pollution Bulletin, doi: 10.1016/j.marpolbul.2020.110964\n* Simonov, A. I. and E. N. Altman, (Eds.), Hydrometeorology and Hydrochemistry of the USSR Seas, vol. IV, The Black Sea, 430 pp, Gidrometeoizdat, St. Petersburg, Russia, 1991\n* Stanev, E. V., Le Traon, P.\u2010Y., and Peneva, E. L., Sea level variations and their dependency on meteorological and hydrological forcing: Analysis of altimeter and surface data for the Black Sea, J. Geophys. Res., 105(C7), 17203\u2013 17216, doi:10.1029/1999JC900318., 2000\n* Stanev, E. V., and E. L. Peneva, Regional sea level response to global climatic change: Black Sea examples. Global and Planetary Change, 32, 33-47, 2002.\n* Stanev E.V. and Ricker M. (2019), The Fate of Marine Litter in Semi-Enclosed Seas: A Case Study of the Black Sea, Frontiers in Marine Science, doi: 10.3389/fmars.2019.00660.\n* Von Schuckmann et al., 2021: Copernicus Marine Service Ocean State Report, Issue 5, Journal of Operational Oceanography, 14, sup1, doi: 10.1080/1755876X.2021.1946240.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-circulation-boundary-blksea-rim-current-index", "rim-current-intensity-index", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00326", "title": "Black Sea Rim Current Intensity Index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"description": "**DEFINITION**\n\nThe indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149\u00b0E and 32-37\u00b0N and computed from the DUACS delayed-time (CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. \n\n\"\"CONTEXT\"\" \n\nThe Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35\u00b0N, 140\u00b0E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).\n\n\"\"CMEMS KEY FINDINGS\"\" \n\nThe different states of the Kuroshio extension phase have been presented and validated by (Bessi\u00e8res et al., 2013) and further reported by Dr\u00e9villon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an \u2018elongated state\u2019 (also called \u2018strong state\u2019) corresponding to a narrow strong steady jet, and a \u2018contracted state\u2019 (also called \u2018weak state\u2019) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00222\n\n**References:**\n\n* Bessi\u00e8res, L., Rio, M.H., Dufau, C., Boone, C., Pujol, M.I., 2013. Ocean state indicators from MyOcean altimeter products. Ocean Sci. 9, 545\u2013560. https://doi.org/10.5194/os-9-545-2013\n* Dr\u00e9villon, M., Legeais, J.-F., Peterson, A., Zuo, H., Rio, M.-H., Drillet, Y., Greiner, E., 2018. Western boundary currents. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 2, s60\u2013s65. https://doi.org/10.1080/1755876X.2018.1489208\n* Jayne, S.R., Hogg, N.G., Waterman, S.N., Rainville, L., Donohue, K.A., Randolph Watts, D., Tracey, K.L., McClean, J.L., Maltrud, M.E., Qiu, B., Chen, S., Hacker, P., 2009. The Kuroshio Extension and its recirculation gyres. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 2088\u20132099. https://doi.org/10.1016/j.dsr.2009.08.006\n* Niiler, P.P., Maximenko, N.A., Panteleev, G.G., Yamagata, T., Olson, D.B., 2003. Near-surface dynamical structure of the Kuroshio Extension. J. Geophys. Res. Oceans 108. https://doi.org/10.1029/2002JC001461\n* Qiu, B., 2003. Kuroshio Extension Variability and Forcing of the Pacific Decadal Oscillations: Responses and Potential Feedback. J. Phys. Oceanogr. 33, 2465\u20132482. https://doi.org/10.1175/2459.1\n* Qiu, B., 2002. The Kuroshio Extension System: Its Large-Scale Variability and Role in the Midlatitude Ocean-Atmosphere Interaction. J. Oceanogr. 58, 57\u201375. https://doi.org/10.1023/A:1015824717293\n* Qiu, B., Chen, S., 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep Sea Res. Part II Top. Stud. Oceanogr., North Pacific Oceanography after WOCE: A Commemoration to Nobuo Suginohara 57, 1098\u20131110. https://doi.org/10.1016/j.dsr2.2008.11.036\n* Qiu, B., Chen, S., 2005. Variability of the Kuroshio Extension Jet, Recirculation Gyre, and Mesoscale Eddies on Decadal Time Scales. J. Phys. Oceanogr. 35, 2090\u20132103. https://doi.org/10.1175/JPO2807.1\n* Lin, P., Chai, F., Xue, H., Xiu, P., 2014. Modulation of decadal oscillation on surface chlorophyll in the Kuroshio Extension. J. Geophys. Res. Oceans 119, 187\u2013199. https://doi.org/10.1002/2013JC009359\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-circulation-boundary-pacific-kuroshio-phase-area-averaged", "satellite-observation", "specific-turbulent-kinetic-energy-of-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00222", "title": "Kuroshio Phase from Observations Reprocessing"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"description": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m\u00b3, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (\u03c3 \u0305) bins can be computed as follows:\n\u03c8^* (y,\u03c3 \u0305 )=-1/T \u222b_(t_0)^(t_1)\u2592\u222b_(x_B1)^(x_B2)\u2592\u3016\u222b_(-H)^0\u2592H[\u03c3 \u0305-\u03c3(x,y,z,t)] \u00d7\u03bd(x,y,z,t)dzdxdt,\u3017\nwhere H is the Heaviside function and \u03bd is the meridional velocity. We used 100 \u03c3_2  (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.\n\n**CONTEXT**\n\nThe BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change.  The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).\n\n**KEY FINDINGS**\n\nThe MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.\nFor the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00349\n\n**References:**\n\n* Ilicak, M., Causio, S., Ciliberti, S., Coppini, G., Lima, L., Aydogdu, A., Azevedo, D., Lecci, R., Cetin, D. U., Masina, S., Peneva, E., Gunduz, M., Pinardi, N. (2022). The Black Sea overturning circulation and its indicator of change. In: Copernicus Ocean State Report, issue 6, Journal of Operational Oceanography, 15:sup1, s64:s71; DOI: doi.org/10.1080/1755876X.2022.2095169\n* Lima, L., Ciliberti, S.A., Aydo\u011fdu, A., Masina, S., Escudier, R., Cipollone, A., Azevedo, D., Causio, S., Peneva, E., Lecci, R., Clementi, E., Jansen, E., Ilicak, M., Cret\u00ec, S., Stefanizzi, L., Palermo, F., Coppini, G. (2021). Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis. Front. Mar. Sci. 8:710973. doi: 10.3389/fmars.2021.710973\n* Stanev, E. V., Peneva, E., Chtirkova, B. (2019). Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans 124, 4803\u20134819. doi: 10.1029/2019JC015076\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "ocean-meridional-overturning-streamfunction", "oceanographic-geographical-features", "omi-circulation-moc-blksea-area-averaged-mean", "s", "sla", "t", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00349", "title": "Black Sea Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"description": "**DEFINITION**\n\nTime mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the period from 1987 to the year preceding the current one [-1Y], operationally extended yearly. The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period. \nThe Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5\u00b0N, and then considering the associated maxima. \nIn each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined.\nTime series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021).\nMore details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020) and in the QUID.\n\n**CONTEXT**\nThe western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (MEDSEA_MULTIYEAR_PHY_006_004,  Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. \nThe Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015).\nAs in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3.\n\n**CMEMS KEY FINDINGS**\n\nThe western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. \nOn average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992 (Roether et al. 1996, 2014, Gertman et al. 2006). In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00317\n\n**References:**\n\n* Cessi, P. 2019. The global overturning circulation. Ann Rev Mar Sci. 11:249\u2013270. DOI:10.1146/annurev-marine- 010318-095241. Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., Pinardi, N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Gertman, I., Pinardi, N., Popov, Y., Hecht, A. 2006. Aegean Sea water masses during the early stages of the eastern Mediterranean climatic Transient (1988\u20131990). J Phys Oceanogr. 36(9):1841\u20131859. DOI:10.1175/JPO2940.1.\n* Johnson, K.S., Berelson, W.M., Boss, E.S., Chase, Z., Claustre, H., Emerson, S.R., Gruber, N., Ko\u0308rtzinger, A., Perry, M.J., Riser, S.C. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography. 22:216\u2013225. DOI:10.5670/oceanog. 2009.81.\n* Lyubartsev, V., Borile, F., Clementi, E., Masina, S., Drudi, M/. Coppini, G., Cessi, P., Pinardi, N. 2020. Interannual variability in the Eastern and Western Mediterranean Overturning Index. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097.\n* Pinardi, N., Cessi, P., Borile, F., Wolfe, C.L.P. 2019. The Mediterranean Sea overturning circulation. J Phys Oceanogr. 49:1699\u20131721. DOI:10.1175/JPO-D-18-0254.1.\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Tonani, M., Lyubartsev, V., Dobricic, S., Bonaduce, A. 2015. Mediterranean Sea large-scale, low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Prog Oceanogr. 132:318\u2013332. DOI:10.1016/j.pocean.2013.11.003.\n* Roether, W., Klein, B., Hainbucher, D. 2014. Chap 6. The eastern Mediterranean transient. In: GL Eusebi Borzelli, M Gacic, P Lionello, P Malanotte-Rizzoli, editors. The Mediterranean Sea. American Geophysical Union (AGU); p. 75\u201383. DOI:10.1002/9781118847572.ch6.\n* Roether, W., Manca, B.B., Klein, B., Bregant, D., Georgopoulos, D., Beitzel, V., Kovac\u030cevic\u0301, V., Luchetta, A. 1996. Recent changes in the eastern Mediterranean deep waters. Science. 271:333\u2013335. DOI:10.1126/science.271.5247.333.\n* Schroeder, K., Chiggiato, J., Bryden, H., Borghini, M., Ismail, S.B. 2016. Abrupt climate shift in the western Mediterranean Sea. Sci Rep. 6:23009. DOI:10.1038/srep23009.\n* Smith, R.O., Bryden, H.L., Stansfield, K. 2008. Observations of new western Mediterranean deep water formation using Argo floats 2004-2006. Ocean Science, 4 (2), 133-149.\n* Von Schuckmann, K. et al. 2020. Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097.\n* Yoon, S., Chang, K., Nam, S., Rho, T.K., Kang, D.J., Lee, T., Park, K.A., Lobanov, V., Kaplunenko, D., Tishchenko, P., Kim, K.R. 2018. Re-initiation of bottom water formation in the East Sea (Japan Sea) in a warming world. Sci Rep. 8:1576. DOI:10. 1038/s41598-018-19952-4.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1987-01-01T00:00:00Z", "2239-05-05T01:16:18Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-ts-profiles", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "numerical-model", "ocean-meridional-overturning-streamfunction", "oceanographic-geographical-features", "omi-circulation-moc-medsea-area-averaged-mean", "sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00317", "title": "Mediterranean Meridional Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"description": "**DEFINITION**\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1):  Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\n**CONTEXT**\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.3 Sv for the full period 1991-2024. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 2 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00189\n\n**References:**\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1991-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "ocean-volume-transport-across-line", "oceanographic-geographical-features", "omi-circulation-voltrans-arctic-averaged", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00189", "title": "Nordic Seas Volume Transports from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"description": "**DEFINITION**\n\nThe indicator of Volume Transport Anomaly in Selected Vertical Sections in the Iberia\u2013Biscay\u2013Ireland (IBI) region (OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies) is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been selected to represent the temporal variability of key ocean currents within the IBI domain. \n\nThe monitored ocean currents include the transport towards the North Sea through the Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), the Canary Current (CC) (Knoll et al., 2002; Mason et al., 2011), the Azores Current (AC) (Mason et al., 2011), the Algerian Current (ALG) (Tintor\u00e9 et al., 1988; Benzohra and Millot, 1995; Font et al., 1998), and the net transport along the 48\u00b0 N latitude parallel (N48) (see OMI figure). \n\nTo produce ensemble-based results, six datasets provided by the Copernicus Marine Service have been used: \n\n* **IBI-REA** & **IBI-INT**: IBI_MULTIYEAR_PHY_005_002 (reanalysis and interim datasets) \n\n* **GLO-REA**: GLOBAL_MULTIYEAR_PHY_001_030 (reanalysis) \n\n* **ARMOR**: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (reprocessed observations) \n\n* **MED-REA**: MEDSEA_MULTIYEAR_PHY_006_004 (reanalysis) \n\n* **NWS-REA**: NWSHELF_MULTIYEAR_PHY_004_009 (reanalysis) \n\nThe time series displays the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with reversed sign (red dashed line), which indicates the threshold of anomaly values corresponding to a reversal in the direction of the current transport. In addition, the trend analysis at the 95% confidence level is shown in the bottom-right corner of each diagram. \n\nFurther details on the product are provided in the corresponding Product User Manual (de Pascual-Collar et al., 2026a) and Quality Information Document (de Pascual-Collar et al., 2026b), as well as in de Pascual-Collar et al., 2024. \n\n**CONTEXT** \n\nThe IBI area is a highly complex region characterized by a remarkable variety of ocean currents. Among them, we can highlight those that originate as a result of the closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; P\u00e9rez et al., 2001; Jia, 2000); the subsurface currents flowing northward along the continental slope (de Pascual-Collar et al., 2019; Pascual et al., 2018; Mach\u00edn et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Maz\u00e9 et al., 1997; White & Bowyer, 1997); and the exchange currents occurring in the Strait of Gibraltar and the Alboran Sea (Sotillo et al., 2016; Font et al., 1998; Benzohra & Millot, 1995; Tintor\u00e9 et al., 1988). \n\nThe variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental processes. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian Current serves as the only pathway for Atlantic Water to reach the Western Mediterranean. \n\n**CMEMS KEY FINDINGS** \n\nThe volume transport time series reveal periods during which the monitored currents exhibited notably high or low variability. Specifically, the RTE current shows pronounced variability in 2010 and during 2014\u20132015; the N48 section between 2012 and 2014; the ALG current in 2006 and 2017; the AC current between 2005\u20132007 and in 2021; and the CC current between 2005\u20132007. \n\nFurthermore, certain periods display anomalies of sufficient magnitude (in absolute value) to indicate a reversal in the net transport direction of the current. This is the case for the ALG current in 2017 and 2024 (with net transport towards the west), and for the CC current in 2010 (with net transport towards the north). \n\nTrend analysis over the period 1993\u20132023 does not reveal any statistically significant trends for the monitored currents. However, the confidence interval for the trend in the ALG section is close to rejecting the null hypothesis of no trend.\n\n**DOI (product):**\nhttps://doi.org/10.48670/mds-00351\n\n**References:**\n\n* Benzohra, M., Millot, C.: Characteristics and circulation of the surface and intermediate water masses off Algeria. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1803-1830, https://doi.org/10.1016/0967-0637(95)00043-6, 1995.\n* Bower, A. S., Le Cann, B., Rossby, T., Zenk, T., Gould, J., Speer, K., Richardson, P. L., Prater, M. D., Zhang, H.-M.: Directly measured mid-depth circulation in the northeastern North Atlantic Ocean: Nature, 419, 6907, 603\u2013607, https://doi.org/10.1038/nature01078, 2002.\n* Bozec, A., Lozier, M. S., Chasignet, E. P., Halliwel, G. R.: On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006, J. Geophys. Res.-Oceans, 116, C09033, https://doi.org/10.1029/2011JC007191, 2011.\n* Fasullo, J. T., Trenberth, K. E.: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. Journal of Climate, 21(10), 2313-2325, https://doi.org/10.1175/2007JCLI1936.1, 2008.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Friocourt Y, Levier B, Speich S, Blanke B, Drijfhout SS. A regional numerical ocean model of the circulation in the Bay of Biscay, J. Geophys. Res.,112:C09008, https://doi.org/10.1029/2006JC003935, 2007.\n* Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-M\u00f6ller, A., Hansen, B., Lav\u00edn, A., Loeng, H., Mork, K. A., \u00d8sterhus, S., Sherwin, T., Walczowski, W.: Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas, Geophys. Res. Lett., 35, L03614, https://doi.org/10.1029/2007GL032675, 2008.\n* Holliday, N. P.: Air\u2010sea interactionand circulation changes in the north- east Atlantic, J. Geophys. Res., 108(C8), 3259, https://doi.org/10.1029/2002JC001344, 2003.\n* Jia, Y.: Formation of an Azores Current Due to Mediterranean Overflow in a Modeling Study of the North Atlantic. J. Phys. Oceanogr., 30, 9, 2342\u20132358, https://doi.org/10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2, 2000.\n* Knoll, M., Hern\u00e1ndez-Guerra, A., Lenz, B., L\u00f3pez Laatzen, F., Mach\u0131\u0301n, F., M\u00fcller, T. J., Siedler, G.: The Eastern Boundary Current system between the Canary Islands and the African Coast, Deep-Sea Research. 49-17, 3427-3440, https://doi.org/10.1016/S0967-0645(02)00105-4, 2002.\n* Lozier, M. S., Stewart, N. M.: On the temporally varying penetration ofMediterranean overflowwaters and eastward penetration ofLabrador Sea Water, J. Phys. Oceanogr., 38,2097\u20132103, https://doi.org/10.1175/2008JPO3908.1, 2008.\n* Mach\u00edn, F., Pelegr\u00ed, J. L., Fraile-Nuez, E., V\u00e9lez-Belch\u00ed, P., L\u00f3pez-Laatzen, F., Hern\u00e1ndez-Guerra, A., Seasonal Flow Reversals of Intermediate Waters in the Canary Current System East of the Canary Islands. J. Phys. Oceanogr, 40, 1902\u20131909, https://doi.org/10.1175/2010JPO4320.1, 2010.\n* Mason, E., Colas, F., Molemaker, J., Shchepetkin, A. F., Troupin, C., McWilliams, J. C., Sangra, P.: Seasonal variability of the Canary Current: A numerical study. Journal of Geophysical Research: Oceans, 116(C6), https://doi.org/10.1029/2010JC006665, 2011.\n* Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997.\n* Pascual, A., Levier ,B., Sotillo, M., Verbrugge, N., Aznar, R., Le Cann, B.: Characterization of Mediterranean Outflow Water in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann et al. (2018) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product User Manual for OMI_CURRENTS_VOLTRANS_section_integrated_anomalies. Issue 1.1, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-OMI-PUM-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2026a.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M. G., Ciliberti, S.: EU Copernicus Marine Service Product Quality Information Document for Global Reanalysis Products, OMI_CURRENTS_VOLTRANS_section_integrated_anomalies, Issue 1.1, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-OMI-QUID-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2026b.\n* de Pascual-Collar, \u00c1., Aznar, R., Levier, B., and Sotillo, M. G.: Monitoring main ocean currents of the Iberia\u2013Biscay\u2013Ireland region, in: 8th edition of the Copernicus Ocean State Report (OSR8), edited by: von Schuckmann, K., Moreira, L., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., and Neukermans, G., Copernicus Publications, State Planet, 4-osr8, 5, https://doi.org/10.5194/sp-4-osr8-5-2024, 2024.\n* de Pascual-Collar, A., Sotillo, M. G., Levier, B., Aznar, R., Lorente, P., Amo-Baladr\u00f3n, A., \u00c1lvarez-Fanjul E.: Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019, 2019.\n* Peliz, A., Dubert, J., Marchesiello, P., Teles\u2010Machado, A.: Surface circulation in the Gulf of Cadiz: Model and mean flow structure. Journal of Geophysical Research: Oceans, 112, C11, https://doi.org/10.1029/2007JC004159, 2007.\n* Perez, F. F., Castro, C. G., \u00c1lvarez-Salgado, X. A., R\u00edos, A. F.: Coupling between the Iberian basin-scale circulation and the Portugal boundary current system: a chemical study, Deep-Sea Research. I 48,1519 -1533, https://doi.org/10.1016/S0967-0637(00)00101-1, 2001.\n* Sotillo, M. G., Amo-Baladr\u00f3n, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodr\u00edguez-Rubio, P., Conti, D., Jim\u00e9nez Madrid, J. A., de los Santos, F. J., Alvarez Fanjul E.: How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean, Deep-Sea Research. 133, 100-117, https://doi.org/10.1016/j.dsr2.2016.05.020, 2016.\n* Tintore, J., La Violette, P. E., Blade, I., Cruzado, A.: A study of an intense density front in the eastern Alboran Sea: the Almeria\u2013Oran front. Journal of Physical Oceanography, 18, 10, 1384-1397, https://doi.org/10.1175/1520-0485(1988)018%3C1384:ASOAID%3E2.0.CO;2, 1988.\n* White, M., Bowyer, P.: The shelf-edge current north-west of Ireland. Annales Geophysicae 15, 1076\u20131083. https://doi.org/10.1007/s00585-997-1076-0, 1997.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-circulation-voltrans-ibi-section-integrated-anomalies", "volume-transport-anomaly-across-monitoring-section", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00351", "title": "Volume Transport Anomaly in Selected Vertical Sections"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"description": "**DEFINITION**\n\nOcean Freshwater Content (OFC) ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 7 (Raudsepp et al, 2023) and is derived from regional reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The OFC is calculated according to Boyer et al. (2007)\nOFC =    \u03c1(Sref, Tref, p) / \u03c1(0, Tref, p ) \u00b7 ( Sref - S) / Sref\nwhere S(x, y, z, t) and Sref (x, y, z) are actual salinity and reference salinity, respectively, and x,y,z,t are zonal, meridional, vertical and temporal coordinates, respectively. The density, \u03c1, is calculated according to the TEOS10 (IOC et al., 2010). The key issue of OFC calculations lies in how the reference salinity is defined. The climatological range of salinity in the Baltic Sea varies from the freshwater conditions in the northern and eastern parts to the oceanic water conditions in the Kattegat. We follow the Boyer et al. (2007) formulation and calculate the climatological OFC from the three-dimensional temperature (Tref) and salinity (Sref) fields averaged over the period of 1993\u20132014.\nThe method for calculating the ocean freshwater content anomaly is based on the daily mean sea water salinity fields (S) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total freshwater content anomaly is determined using the following formula:\nOFC(t) = \u222dV OFC(x, y, z, t) dx dy dz\nThe vertical integral is computed using the static cell vertical thicknesses (dz) sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial integration is performed over the Baltic Sea spatial domain, defined as the region between 9\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N using product grid definition in cmems_mod_bal_phy_my_static. \n\nWe evaluate the uncertainty from the mean standard deviation of monthly mean OFC. The shaded area in the figure corresponds to the annual standard deviation of monthly mean OFC. \n\nLinear trend (km3y-1) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\n\nClimate warming has resulted in the intensification of the global hydrological cycle but not necessarily on the regional scale (Pratap and Markonis, 2022). The increase of net precipitation over land and sea areas, decrease of ice cover, and increase of river runoff are the main components of the global hydrological cycle that increase freshwater content in the ocean (Boyer et al., 2007) and decrease ocean salinity.\nThe Baltic Sea is one of the marginal seas where water salinity and OFC are strongly influenced by the water exchange with the North Sea. The Major Baltic Inflows (MBIs) are the most voluminous event-type sources of saline water to the Baltic Sea (Mohrholz, 2018). The frequency and intensity of the MBIs and other large volume inflows have no long-term trends but do have a multidecadal variability of about 30 years (Mohrholz, 2018; Lehmann and Post, 2015; Lehmann et al., 2017; Radtke et al., 2020). Smaller barotropic and baroclinically driven inflows transport saline water into the halocline or below it, depending on the density of the inflow water (Reissmann et al., 2009). \n\n**KEY FINDINGS**\n\nThe Baltic Sea's ocean freshwater content is exhibiting a declining trend 38.06\u00b18.69 km3/year, along with decadal fluctuations as also noted by Lehmann et al. (2022). Elevated freshwater levels were recorded prior to the Major Baltic Inflows of 1993, 2002, and 2013, which subsequently led to a swift decrease in freshwater content. The lowest ocean freshwater content was recorded in 2023. Over the past four years, the freshwater content anomaly has remained comparatively stable. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00347\n\n**References:**\n\n* Boyer, T., Levitus, S., Antonov, J., Locarnini, R., Mishonov, A., Garcia, H., Josey, S.A., 2007. Changes in freshwater content in the North Atlantic Ocean 1955\u20132006. Geophysical Research Letters, 34(16), L16603. Doi: 10.1029/2007GL030126\n* IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and use of thermodynamic properties. Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English), 196 pp. Available from http://www.TEOS-10.org (11.10.2021). Lehmann, A., Post, P., 2015. Variability of atmospheric circulation patterns associated with large volume changes of the Baltic Sea. Advances in Science and Research, 12, 219\u2013225, doi:10.5194/asr-12-219-2015\n* Lehmann, A., H\u00f6flich, K., Post, P., Myrberg, K., 2017. Pathways of deep cyclones associated with large volume changes (LVCs) and major Baltic inflows (MBIs). Journal of Marine Systems, 167, pp.11-18. doi:10.1016/j.jmarsys.2016.10.014\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz, V., 2018. Major Baltic inflow statistics\u2013revised. Frontiers in Marine Science, 5, p.384. doi:10.3389/fmars.2018.00384\n* Pratap, S., Markonis, Y., 2022. The response of the hydrological cycle to temperature changes in recent and distant climatic history, Progress in Earth and Planetary Science 9(1),30. doi:10.1186/s40645-022-00489-0\n* Radtke, H., Brunnabend, S.-E., Gr\u00e4we, U., Meier, H. E. M., 2020. Investigating interdecadal salinity changes in the Baltic Sea in a 1850\u20132008 hindcast simulation, Climate of the Past, 16, 1617\u20131642, doi:10.5194/cp-16-1617-2020\n* Raudsepp, U., Maljutenko, I., Barzandeh, A., Uiboupin, R., Lagemaa, P., 2023. Baltic Sea freshwater content. Copernicus Ocean State Report, Issue 7. State Planet, 1(osr7), pp 7. doi:10.5194/sp-1-osr7-7-2023\n* Reissmann, J. H., Burchard, H., Feistel,R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., Wiecczorek, G., 2009. Vertical mixing in the Baltic Sea and consequences for eutrophication a review, Progress in Oceanography, 82, 47\u201380. doi:10.1016/j.pocean.2007.10.004\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "ofc-balrean", "ofc-balrean-lower-rmsd", "ofc-balrean-upper-rmsd", "omi-climate-ofc-baltic-area-averaged-anomalies", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00347", "title": "Baltic Sea Ocean Freshwater Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BALTIC_area_averaged_anomalies": {"description": "**DEFINITION**\n\nOcean heat content ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 6 (Raudsepp et al, 2022) and is derived from regional reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula:\n\nHC =  \u03c1 * cp * ( Tp  +273.15).\n\nHere, \u03c1 and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content.\n\nWe evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (\u00b1 35.3 MJ/m\u00b2) evaluated from the observed temperatures and corresponding model values. \n\nLinear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\n\nOcean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 \u00b1 0.08 W/m2 in the period 1955\u20132019, while during the last decade of 2010\u20132019 the warming rate was 0.70 \u00b1 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean.\n\n**KEY FINDINGS**\n\nThe ocean heat content (OHC) of the Baltic Sea exhibits an increasing trend of 0.34\u00b10.09 W/m\u00b2, along with multi-year oscillations. This increase is less pronounced than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and that of some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). The relatively low trend values are attributed to the Baltic Sea's shallowness, which constrains heat accumulation in its waters. The most significant OHC anomaly was recorded in 2020, and following a decline from this peak, the anomaly has now stabilized at approximately 250 MJ/m\u00b2.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00322\n\n**References:**\n\n* Garcia-Soto C, Cheng L, Caesar L, Schmidtko S, Jewett EB, Cheripka A, Rigor I, Caballero A, Chiba S, B\u00e1ez JC, Zielinski T and Abraham JP (2021) An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 8:642372. doi: 10.3389/fmars.2021.642372\n* Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp., p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf\n* Giorgetti, A., Lipizer, M., Molina Jack, M.E., Holdsworth, N., Jensen, H.M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M. and Fyrberg, L., 2020. Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry. Frontiers in Marine Science, 7, p.583657.\n* Holland E, von Schuckmann K, Monier M, Legeais J-F, Prado S, Sathyendranath S, Dupouy C. 2019. The use of Copernicus Marine Service products to describe the state of the tropical western Pacific Ocean around the islands: a case study. In: Copernicus Marine Service Ocean State Report, Issue 3. J Oper Oceanogr. 12(suppl. 1):s43\u2013s48. doi:10.1080/1755876X.2019.1633075\n* Lima L, Peneva E, Ciliberti S, Masina S, Lemieux B, Storto A, Chtirkova B. 2020. Ocean heat content in the Black Sea. In: Copernicus Marine Service Ocean State Report, Issue 4. J Oper Oceanogr. 13(suppl. 1):s41\u2013s48. doi:10.1080/1755876X.2020.1785097.\n* von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G. 2019. Copernicus Marine Service Ocean State report. J Oper Oceanogr. 12(suppl. 1):s1\u2013s123. doi:10.1080/1755876X.2019.1633075.\n* von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, Collar A, Sotillo MG, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content. In: Copernicus Marine Service Ocean State Report, issue 2. J Oper Oceanogr. 11 (Suppl. 1):s1\u2013s142. doi:10.1080/1755876X.2018.1489208.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "ohc-balrean", "ohc-balrean-lower-rmsd", "ohc-balrean-upper-rmsd", "omi-climate-ohc-baltic-area-averaged-anomalies", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00322", "title": "Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"description": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth. Further details and the formulation of OHC can be found in the quality information document (QUID) of the corresponding ocean monitoring indicator (OMI). \nTime series of  monthly area-averaged OHC is provided for the Black Sea (40.86\u00b0N, 46.8\u00b0N; 27.32\u00b0E, 41.96\u00b0E) and is evaluated in areas where the topography is deeper than 300 m. The Azov and Marmara Seas are not considered.\nThe quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). An ensemble mean and its associated spread were derived using four products: three global and one regional (Black Sea) dataset: \nThe Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode (INSITU_GLO_PHY_TS_OA_MY_013_052, CORA5.2; Szekely et al., 2019).\nThe Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, AMOR3D; Guinehut et al., 2012). \nThe Global Ocean Physics Reanalysis (GLOBAL_MULTIYEAR_PHY_001_030; Lellouche et al., 2021).\nThe Black Sea Physics Reanalysis (BLKSEA_MULTIYEAR_PHY_007_004; Lima et al., 2021).\n\n**CONTEXT**\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nSeveral studies discuss warming in the Black Sea using either observations or model results (Akpinar et al., 2017; Stanev et al., 2019; Lima et al., 2020). Using satellite sea surface temperature observations (SST), Degtyarev (2000) detected a positive temperature trend of 0.016 \u00baC years-1 in the 50-100 m layer from 1985 to 1997. From Argo floats Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL; at approximately 25 \u2013 70 m) of about 0.05 oC year-1 in recent years. The warming signal was also present in OHC analyses using an earlier version of the Black Sea regional reanalysis which revealed a positive trend of 0.88\u00b10.18 W m-2 in the upper layers (0-200 m). This warming has resulted in the disappearance of the CIL in the Black Sea in recent years, as well as an increased frequency of marine heatwaves in the region (Mohamed et al., 2022).\n\n**KEY FINDINGS**\nTime series of OHC anomalies present a significant interannual variability, altering between cool and warm events. This important characteristic becomes evident over the years 2012 to 2015: a negative minimum in the OHC anomaly is registered close to \u2013 2.00 x 108 J m-2 in 2012, followed by positive values around 1.50 x 108 J m-2 in 2013 and above 2.0 x 108 J m-2 most of time in 2014 and 2015. Since 2005 the Black Sea has experienced an increase in OHC, and record OHC values were noticed in January 2021. \nThe increase in OHC weakens the CIL, whereas its decreasing favours the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021). Beginning in February 2021, OHC anomalies began to decline again. A very weak CIL formed in March 2022, as reported by \u00c7okacar et al. (2024). \n\n**DOI (product):** \n\u00a0https://doi.org/10.48670/moi-00306\n\n**References:**\n\n* Akpinar, A., Fach, B. A., Oguz, T., 2017: Observing the subsurface thermal signature of the Black Sea cold intermediate layer with Argo profiling floats. Deep Sea Res. I Oceanogr. Res. Papers 124, 140\u2013152. doi: 10.1016/j.dsr.2017.04.002.\n* Lima, L., Peneva, E., Ciliberti, S., Masina, S., Lemieux, B., Storto, A., Chtirkova, B., 2020: Ocean heat content in the Black Sea. In: Copernicus marine service Ocean State Report, issue 4, Journal of Operational Oceanography, 13:Sup1, s41\u2013s47, doi: 10.1080/1755876X.2020.1785097.\n* Lima L., Ciliberti S. A., Aydo\u011fdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., Ilicak M., Cret\u00ec S., Stefanizzi L., Palermo F., Coppini G., 2021: Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis, Frontier in Marine Science, 8:710973, doi: 10.3389/fmars.2021.710973.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841, DOI: 10.1007/s00382-015-2728-5.\n* Stanev, E. V., Peneva, E., and Chtirkova, B. 2019: Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans, 124, 4803\u20134819, doi: 10.1029/2019JC015076.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:Sup1, s1-s142, doi: 10.1080/1755876X.2018.1489208.\n* Degtyarev, A. K., 2000: Estimation of temperature increase of the Black Sea active layer during the period 1985\u2013 1997, Meteorilogiya i Gidrologiya, 6, 72\u2013 76 (in Russian).\n* \u00c7okacar, T. (2024). Cold Intermediate Water Formation in the Black Sea Triggered by March 2022 Cold Intrusions. Journal of Marine Science and Engineering, 12(11), 2027. https://doi.org/10.3390/jmse12112027\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "in-situ-observation", "integral-wrt-depth-of-sea-water-temperature-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-blksea-area-averaged-anomalies", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00306", "title": "Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_2000": {"description": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming\ni) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2).\nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.9 \u00b1 0.1 W/m2.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00235\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content", "integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-global-area-averaged-anomalies-0-2000", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00235", "title": "Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_300": {"description": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming\ni) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2).\nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the near-surface (0-300m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.4 \u00b1 0.1 W/m2.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00233\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content", "integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-global-area-averaged-anomalies-0-300", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00233", "title": "Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_700": {"description": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming\ni) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2).\nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-700m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.6 \u00b1 0.1 W/m2.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00234\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content", "integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-global-area-averaged-anomalies-0-700", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00234", "title": "Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_GLOBAL_trend": {"description": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming\ni) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2).\nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nRegional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00236\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "extent": {"spatial": {"bbox": [[-180, -80, 179.75, 90]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-global-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00236", "title": "Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"description": "**DEFINITION**\n \nThe Iberia-Biscay-Ireland (IBI) Ocean Heat Content (OHC) indicator, OMI_CLIMATE_OHC_IBI_area_averaged_anomalies, provides estimates of OHC anomalies computed over the reference period 1993\u20132024. The values are integrated from the surface down to 2000 m depth, using a reference density of \u03c1\u2080 = 1030 kg\u00b7m\u207b\u00b3 and a specific heat capacity of c\u209a = 3980 J\u00b7kg\u207b\u00b9\u00b7\u00b0C\u207b\u00b9 (e.g., von Schuckmann et al., 2009). This variable is directly proportional to the average temperature change in the ocean.\n\nAveraged time series of OHC anomalies and their associated uncertainties are computed for the IBI region (26\u00b0 N\u201356\u00b0 N; 19\u00b0 W\u20135\u00b0 E) using the following Copernicus Marine products:\n* **IBI-MYP** & **IBI-INT**: IBI_MULTIYEAR_PHY_005_002 (reanalysis and interim datasets)\n* **GLO-MYP**: GLOBAL_REANALYSIS_PHY_001_031 (reanalysis)\n* **CORA**: INSITU_GLO_PHY_TS_OA_MY_013_052 (in situ observations)\n* **ARMOR**: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (reprocessed observations)\n\nThe figure displays the ensemble mean (blue line) and the ensemble spread (grey shading). Further details on the indicator and data processing are provided in the corresponding Product User Manual (de Pascual-Collar et al., 2026) and in de Pascual-Collar et al. (2023), von Schuckmann et al. (2016), and von Schuckmann et al. (2018).\n \n**CONTEXT**\n \nChange in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meteorological Organization (WMO, 2017). \n\nIn the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022; Potter and Lozier, 2004), these variations affect the temporal evolution of regional ocean heat content in the IBI region.\n\nRecent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m).\n\nAmong the different periods of interannual variability identified by the indicator, a sustained increase in OHC from 2023 onwards is particularly noteworthy. This short-term trend results in 2024 exhibiting the highest OHC value recorded in the time series.\n \n**CMEMS KEY FINDINGS**\n \nThe ensemble mean OHC anomaly time series over the Iberia\u2013Biscay\u2013Ireland region is characterized by marked interannual variability and a a statistically significant ocean warming trend of 0.55 \u00b1 0.3 W m\u207b\u00b2 (99% confidence interval). In addition, the final year of the time series (2024) exhibits the highest OHC value recorded, following a period of sustained warming that began in 2023.\n \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00316\n\n**References:**\n\n* Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Fr\u00f6licher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig (2019): Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 589\u2013655. https://doi.org/10.1017/9781009157964.008.\n* de Pascual-Collar, A., Aznar, R., Amo, A., Sotillo, M. G., Ciliberti, S., (2026): EU Copernicus Marine Service Product User Manual for Iberia-Biscay-Ireland Ocean Heat Content (0-2000m). Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/PUM/CMEMS-OMI-PUM-OHC-IBI.pdf.\n* de Pascual-Collar, \u00c1., Aznar, R., Levier, B., and Garc\u00eda-Sotillo, M., (2023): Ocean heat content in the Iberian\u2013Biscay\u2013Ireland regional seas, in: 7th edition of the Copernicus Ocean State Report (OSR7), edited by: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., and Neukermans, G., Copernicus Publications, State Planet, 1-osr7, 9, https://doi.org/10.5194/sp-1-osr7-9-2023.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu (2021): Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, DOI: https://doi.org/10.1017/9781009157896.011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Masina, S., Pinardi, N., Cipollone, A., Banerjee, D. S., Lyubartsev, V., von Schuckmann, K., Jackson, L., Escudier, R., Clementi, E., Aydogdu, A. and Iovino D., (2022). The Atlantic Meridional Overturning Circulation forcing the mean se level in the Mediterranean Sea through the Gibraltar transport. In: Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography,15:sup1, s119\u2013s126; DOI: https://doi.org/10.1080/1755876X.2022.2095169\n* Potter, R. A., and Lozier, M. S. (2004): On the warming and salinification of the Mediterranean outflow waters in the North Atlantic, Geophys. Res. Lett., 31, 1\u20134, DOI: https://doi.org/10.1029/2003GL018161\n* Robson, J., Ortega, P., Sutton, R., (2016): A reversal of climatic trends in the North Atlantic since 2005. Nature Geosci 9, 513\u2013517. https://doi.org/10.1038/ngeo2727.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, (2009): Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007; DOI: https://doi.org/10.1029/2008JC005237\n* von Schuckmann, K., Le Traon, P. Y., Alvarez-Fanjul, E., Axell, L., Balmaseda, M., Breivik, L. A., \u2026 Verbrugge, N. (2016). The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WCRP (2018): Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, (2017): World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "in-situ-observation", "integral-wrt-depth-of-sea-water-potential-temperature-expressed-as-heat-content", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-ibi-area-averaged-anomalies", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00316", "title": "Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_MEDSEA_area_averaged_anomalies": {"description": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0  c_p (T_yr-T_clim )dz \t\t\t\t\t\t\t\t[1]\nwith a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009).\nTime series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and is evaluated for topography deeper than 300m.\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nThe quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). \nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\n\u2022\tThe Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\n\u2022\tFour global reanalyses at 1/4 degree horizontal resolution (GLOBAL_MULTIYEAR_PHY_ENS_001_031): \nGLORYS, C-GLORS, ORAS5, FOAM\n\n\u2022\tTwo observation based products: \nCORA (INSITU_GLO_PHY_TS_OA_MY_013_052) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2022 at rate of 1.38\u00b10.08 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00261\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Iona, A., A. Theodorou, S. Sofianos, S. Watelet, C. Troupin, J.-M. Beckers, 2018: Mediterranean Sea climatic indices: monitoring long term variability and climate changes, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-51, in review.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841. DOI: 10.1007/s00382-015-2728-5\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, s1-s142, DOI: 10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-ohc-medsea-area-averaged-anomalies", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00261", "title": "Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"description": "**DEFINITION**\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n\u00afS=1/V \u222bV S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\n**CONTEXT**\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the \u201cmulti-product\u201d approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\n\u2022\tThe Mediterranean Sea Reanalysis at 1/24\u00b0horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\n\u2022\tFour global reanalyses at 1/4\u00b0horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, GLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\n\u2022\tTwo observation-based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI:  https://doi.org/10.17882/46219, Szekely et al., 2022) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI.\n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2021 at rate of 7.0*10-3 \u00b13.1*10-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993\u20132021 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00325\n\n**References:**\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., R\u00e9gnier, C., Dr\u00e9villon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA \u2013 In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-ts-profiles", "integral-wrt-depth-of-sea-water-salinity-expressed-as-salt-content", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-osc-medsea-volume-mean", "sea-level", "water-mass-formation-rate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00325", "title": "Mediterranean Ocean Salt Content (0-300m)"}, "OMI_CLIMATE_SI_ARCTIC_transport": {"description": "**DEFINITION**\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\n**CONTEXT**\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00192\n\n**References:**\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1991-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-si-arctic-transport", "sea-ice-concentration-and/or-thickness", "sea-ice-transport-across-line", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00192", "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "OMI_CLIMATE_SI_BALTIC_extent": {"description": "**DEFINITION**\n\nSea ice extent ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Samuelsen et al, 2018) and is derived from satellite observations product SEAICE_BAL_PHY_L4_MY_011_019. Sea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 \u00b0C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.\n\nCMEMS KEY FINDINGS\n\nSea ice in the Baltic Sea exhibits a strong seasonal cycle, typically forming in October and persisting until early June. The 2023/24 ice season was notable for an intermediate maximum extent, reaching approximately 105 000 km\u00b2 on 12 February 2024. Initial ice formation was observed as early as 22 October 2023 in the northern Bay of Bothnia. Expansion accelerated through November and December, with rapid growth continuing into January. The extent remained above the climatological median for much of the freezing period.\n\nIce coverage peaked in mid-February and then gradually declined, with the Baltic Sea becoming ice-free by 4 June 2024, marking a longer-than-average season. The peak day for sea ice extent varies annually but typically falls between late February and early March (Singh et al., 2024). Over the past 30 years, the highest recorded extent was 260,000 km\u00b2 during the 2010/11 winter. While there has been a declining trend in sea ice extent from 1993 to 2024, the linear trend remains statistically insignificant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00200\n\n**References:**\n\n* Eilola K, M\u00e5rtensson S, Meier HEM, 2013. Modeling the impact of reduced sea ice cover in future climate on the Baltic Sea biogeochemistry. Geophysical Research Letters, 40, 149-154, doi:10.1029/2012GL054375\n* Granskog M, Kaartokallio H, Kuosa H, Thomas DN, Vainio J, 2006. Sea ice in the Baltic Sea \u2013 A review. Estuarine, Coastal and Shelf Science, 70, 145\u2013160. doi:10.1016/j.ecss.2006.06.001\n* Hordoir R., Meier HEM, 2012. Effect of climate change on the thermal stratification of the Baltic Sea: a sensitivity experiment. Climate Dynamics, 38, 1703-1713, doi:10.1007/s00382-011-1036-y\n* Singh, S., Maljutenko, I., and Uiboupin, R., 2024, Sea ice in the Baltic Sea during 1993/94\u20132020/21 ice seasons from satellite observations and model reanalysis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1701\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-si-baltic-extent", "satellite-observation", "siextentn", "siextentn-yday-2024", "siextentn-yday-mean", "siextentn-yday-std", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00200", "title": "Baltic Sea Ice Extent from Observations Reprocessing"}, "OMI_CLIMATE_SI_BALTIC_volume": {"description": "**DEFINITION**\n\nSea ice volume ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Samuelsen et al, 2018) and is derived from satellite observations product SEAICE_BAL_PHY_L4_MY_011_019. The sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Bostr\u00f6m and \u00d6sterman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).\n\nCMEMS KEY FINDINGS\n\nIn the Baltic Sea, the ice season typically begins in October and can last until June. The maximum sea ice volume usually occurs in March, but during the 2023/24 season, it was observed in February. That season recorded lower than average maximum ice volume of approximately 17 km\u00b3, well below the long-term climatological average. Between 1993 and 2024, the annual maximum ice volume ranged from a minimum of 3 km\u00b3 in 2020 to a peak of 51 km\u00b3 in 1996.\n\nA statistically significant declining trend of \u22120.63 km\u00b3 per year (p = 0.02) is evident in the time series of maximum sea ice volume, highlighting a persistent reduction over the past three decades. Recent observations also indicate a concurrent decrease in both sea ice fraction and ice thickness, particularly in the Bothnian Bay, as reported by Singh et al. (2024), further reinforcing the regional impacts of climate change on Baltic Sea ice dynamics.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00201\n\n**References:**\n\n* Bostr\u00f6m M, \u00d6sterman C, 2017, Improving operational safety during icebreaker operations, WMU Journal of Maritime Affairs, 16, 73-88, DOI: 10.1007/s13437-016-0105-9\n* Enberg S, Majaneva M, Autio R, Blomster J, Rintala J-M, 2018, Phases of microalgal succession in sea ice and the water column in the baltic sea from autumn to spring. Marine Ecology Progress Series, 559, 19-34. DOI: 10.3354/meps12645\n* Harkonen T, J\u00fcssi M, J\u00fcssi I, Verevkin M, Dmitrieva L, Helle E, Sagitov R, Harding KC, 2008, Seasonal Activity Budget of Adult Baltic Ringed Seals, PLoS ONE 3(4): e2006, DOI: 0.1371/journal.pone.0002006\n* Heinonen J, Rissanen S, 2017, Coupled-crushing analysis of a sea ice - wind turbine interaction \u2013 feasibility study of FAST simulation software, Ships and Offshore Structures, 12, 1056-1063. DOI: 10.1080/17445302.2017.1308782\n* Sundqvist L, Harkonen T, Svensson CJ, Harding KC, 2012, Linking Climate Trends to Population Dynamics in the Baltic Ringed Seal: Impacts of Historical and Future Winter Temperatures, AMBIO, 41: 865, DOI: 10.1007/s13280-012-0334-x\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010, Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, DOI: 10.1109/BALTIC.2010.5621649\n* Valdez Banda OA, Goerlandt F, Montewka J, Kujala P, 2015, A risk analysis of winter navigation in Finnish sea areas, Accident Analysis & Prevention, 79, 100\u2013116, DOI: 10.1016/j.aap.2015.03.024\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-si-baltic-volume", "satellite-observation", "sivoln", "sivoln-yday-2024", "sivoln-yday-mean", "sivoln-yday-std", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00201", "title": "Baltic Sea Ice Volume from Observations Reprocessing"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8.  The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensembles model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit. \n\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).  \n\nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gr\u00e4we et al., 2019). \n\n**KEY FINDINGS**\n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the Baltic Sea rises at a rate of 5.0 \u00b1 0.8 mm/year with an acceleration of 0.19 \u00b1 0.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00202\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gr\u00e4we, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089\u20133108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-baltic-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00202", "title": "Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).  \n\nIn the Black Sea, major drivers of change have been attributed to  anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005). \n\n**KEY FINDINGS**\n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the Black Sea rises at a rate of 0.8 \u00b1 0.8 mm/yr with an acceleration of 0.16 \u00b1 0.06 mm/yr\u00b2. Nevertheless, the area-averaged sea level anomaly is dominated by interannual variability and the trend estimate is not statistically significant at 95% confidence level. This trend estimation is based on the altimeter measurements corrected from the regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period).  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00215\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633\u20132646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-blksea-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00215", "title": "Black Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent  seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit. \n\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n\"\"CONTEXT \"\"\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).  \n\nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf, Black Sea and Baltic regions with different sea level dynamics detailed in the regional indicators. \n\n\"\"KEY FINDINGS\"\" \n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent seas area rises at a rate of 4.0 \u00b1 0.8 mm/yr with an acceleration of 0.23 \u00b1 0.06 mm/yr\u00b2. This trend estimation is based on the altimeter measurements corrected from regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00335\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "oceanographic-geographical-features", "omi-climate-sl-europe-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00335", "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit. \n\nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence level using altimeter error budget (Quet et al 2024 [in prep.]). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.  \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \n\n\"\"KEY FINDINGS \"\"\n\nOver the [1999/02/20 to 2025/10/18] period, global mean sea level  rises at an average rate of 3.8 \uf0b1 0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the global GIA correction (Spada, 2017) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period).  \n\nThe observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Ni\u00f1o Southern Oscillation (Cazenave and Cozannet, 2014). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00237\n\n**References:**\n\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15\u201334, https://doi.org/10.1002/2013EF000188, 2014.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities \u2014 Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Quet V, Prandi P, Meyssignac B, Octau F, Jussiau E, Mangilli A, Dibarboure G, Bignalet-Cazalet F, Ablain M, Long term assessment of the Global Mean Sea Level record and associated uncertainties based on new L2P DT 2024 products (in prep)\n* Spada, G. (2017). Glacial Isostatic Adjustment and Contemporary Sea Level Rise: An Overview. In: Cazenave, A., Champollion, N., Paul, F., Benveniste, J. (eds) Integrative Study of the Mean Sea Level and Its Components. Space Sciences Series of ISSI, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-319-56490-6_8\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\"\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-global-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00237", "title": "Global Ocean Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). At each grid point, the trends/accelerations are estimated on the time series corrected from regional GIA correction (GIA map of a 27 ensemble model following Spada et Melini, 2019) and adjusted from annual and semi-annual signals. Regional uncertainties on the trends estimates can be found in Prandi et al., 2021. \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).  \n\n**KEY FINDINGS**\n\nThe altimeter sea level trends over the [1999/02/20 to 2025/10/18] period exhibit large-scale variations with trends up to +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016). \n\nPrandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr.  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00238\n\n**References:**\n\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press., 2019.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., von Schuckmann, K., Melet, A., Storto, A., and Meyssignac, B.: Sea Level, Journal of Operational Oceanography, 11, s13\u2013s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* McGregor, S., Gupta, A. S., and England, M. H.: Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution, Journal of Climate, 25, 8164\u20138176, https://doi.org/10.1175/JCLI-D-12-00105.1, 2012.\n* Merrifield, M. A., Thompson, P. R., and Lander, M.: Multidecadal sea level anomalies and trends in the western tropical Pacific, Geophysical Research Letters, 39, https://doi.org/10.1029/2012GL052032, 2012.\n* Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy, H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011, in: Integrative Study of the Mean Sea Level and Its Components, edited by: Cazenave, A., Champollion, N., Paul, F., and Benveniste, J., Springer International Publishing, Cham, 191\u2013219, https://doi.org/10.1007/978-3-319-56490-6_9, 2017.\n* Palanisamy, H., Cazenave, A., Delcroix, T., and Meyssignac, B.: Spatial trend patterns in the Pacific Ocean sea level during the altimetry era: the contribution of thermocline depth change and internal climate variability, Ocean Dynamics, 65, 341\u2013356, https://doi.org/10.1007/s10236-014-0805-7, 2015.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schr\u00f6ter, J., and Dahle, C.: Revisiting the contemporary sea-level budget on global and regional scales, Proceedings of the National Academy of Sciences, 113, 1504\u20131509, https://doi.org/10.1073/pnas.1519132113, 2016.\n* Spada, Giorgio, and Daniele Melini. \u201cSELEN&lt;Sup&gt;4&lt;/Sup&gt; (SELEN Version 4.0): A Fortran Program for Solving the Gravitationally and Topographically Self-Consistent Sea-Level Equation in Glacial Isostatic Adjustment Modeling.\u201d Geoscientific Model Development 12, no. 12 (December 4, 2019): 5055\u201375. https://doi.org/10.5194/gmd-12-5055-2019.\n", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-global-regional-trends", "satellite-observation", "tendency-of-sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00238", "title": "Global Ocean Mean Sea Level trend map from Observations Reprocessing"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n\"\"CONTEXT \"\"\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).  \n\nIn IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO)  for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). \n\n\"\"KEY FINDINGS \"\"\n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the IBI area rises at a rate of 5.0 \u00b1 0.8 mm/yr with an acceleration of 0.29 \u00b1 0.06 mm/yr\u00b2. This trend estimation is based on the altimeter measurements corrected from global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00252\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-ibi-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00252", "title": "Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the Mediterranean Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n\"\"CONTEXT\"\" \n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).  \n\nBeside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed between 1993 and 1999 (nearly 8.4 mm/y) and relatively lower values afterward (nearly 2.4 mm/y between 2000 and 2022). This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in the 90\u2019s. Their influence contributed to change the temperature and salinity property of the intermediate and deep water masses. These changes in the water masses and distribution is also associated with sea surface circulation changes, as the one observed in the Ionian Sea in 1997-1998 (e.g. Ga\u010di\u0107 et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). These circulation changes may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013).The relatively high contribution of both sterodynamic (due to steric and circulation changes) and gravitational, rotational, and deformation (due to mass and water storage changes) after 2000 compared to the [1960, 1989] period is also underlined by (Calafat et al., 2022). \n\n\"\"KEY FINDINGS\"\" \n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the Mediterranean Sea rises at a rate of 3.7 \u00b1 0.8 mm/yr with an acceleration of 0.16 \u00b1 0.06 mm/yr\u00b2. This trend estimation is based on the altimeter measurements corrected from regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00264\n\n**References:**\n\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J. Geophys. Res. Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Ga\u010di\u0107, M., Civitarese, G., Eusebi Borzelli, G. L., Kova\u010devi\u0107, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci, A., and Zarokanellos, N.: On the relationship between the decadal oscillations of the northern Ionian Sea and the salinity distributions in the eastern Mediterranean, J. Geophys. Res. Oceans, 116, https://doi.org/10.1029/2011JC007280, 2011.\n* Incarbona, A., Martrat, B., Mortyn, P. G., Sprovieri, M., Ziveri, P., Gogou, A., Jord\u00e0, G., Xoplaki, E., Luterbacher, J., Langone, L., Marino, G., Rodr\u00edguez-Sanz, L., Triantaphyllou, M., Di Stefano, E., Grimalt, J. O., Tranchida, G., Sprovieri, R., and Mazzola, S.: Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events, Sci. Rep., 6, 29623, https://doi.org/10.1038/srep29623, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Landerer, F. W. and Volkov, D. L.: The anatomy of recent large sea level fluctuations in the Mediterranean Sea, Geophys. Res. Lett., 40, 553\u2013557, https://doi.org/10.1002/grl.50140, 2013.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-medsea-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00264", "title": "Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2024 version, \u201cmy\u201d (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). \n\nThe time series of area averaged anomalies correspond to the area average of the maps in the North-West Shelf Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit. The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n\"\"CONTEXT\"\" \n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b).  \n\nIn this region, the time series shows decadal variations. As observed over the global ocean, the main actors of the long-term sea level trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions. \n\n\"\"KEY FINDINGS\"\" \n\nOver the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the NWS area rises at a rate of 4.1 \u00b1 0.8 mm/yr with an acceleration of 0.26 \u00b1 0.06 mm/yr\u00b2. This trend estimation is based on the altimeter measurements corrected from regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00271\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Von Schuckmann et al., \u201cThe State of the Global Ocean, Issue 8.\u201d\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1999-02-20T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sl-northwestshelf-area-averaged-anomalies", "satellite-observation", "sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00271", "title": "North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"description": "**DEFINITION**\n\nOMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). \nSee the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product.\n\n**CONTEXT**\n\nSea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 \u00b0C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039\u00b10.003\u00b0C/year over the period 1982-2024 which corresponds to an average warming of 1.68\u00b0C. Adding the North Sea area, the average trend amounts to 0.026\u00b10.002\u00b0C/year over the same period, which corresponds to an average warming of 1.19\u00b0C for the entire region since 1982.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00205\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Mar-ta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda La-fuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Mari-nova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-bal-area-averaged-anomalies", "satellite-observation", "sea-surface-foundation-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00205", "title": "Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_trend": {"description": "**DEFINITION**\n\nThe  OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2024. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (43 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2024. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nSST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2024. The average trend for the Baltic Sea domain (east of 9\u00b0E longitude) is 0.039\u00b0C/year, which represents an average warming of 1.68\u00b0C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.026\u00b0C/year corresponding to an average warming of 1.19\u00b0C for the 1982-2024 period. Trends are highest for the Baltic Sea and the North Sea, compared to other regions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00206\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Marta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda Lafuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Marinova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "change-over-time-in-sea-surface-foundation-temperature", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-bal-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00206", "title": "Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe omi_climate_sst_ibi_area_averaged_anomalies product for 2024 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the IBI areas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- IBI_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the IBI areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish areas built from re-processed ESA SST CCI, C3S (Embury et al., 2019).  Anomalies are computed against the 1991-2020 reference period. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nThe overall trend in the SST anomalies in this region is 0.012 \u00b10.002 \u00b0C/year over the period 1982-2024.  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00256\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. https://doi.org/10.1080/1755876X.2016.1273446\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-ibi-area-averaged-anomalies", "satellite-observation", "sea-surface-foundation-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00256", "title": "Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_trend": {"description": "**DEFINITION**\n\nThe omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2024, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from re-processed ESA SST CCI, C3S (Embury et al., 2024). Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nThe overall trend in the SST anomalies in this region is 0.012 \u00b10.001 \u00b0C/year over the period 1982-2024.    \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00257\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. https://doi.org/10.1080/1755876X.2016.1273446\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[-18.975000381469727, 26.024999618530273, 4.974999904632568, 55.974998474121094]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "ibi-omi-tempsal-sst-trend", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-ibi-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00257", "title": "Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"description": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2024. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020 (30 years), defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**CMEMS KEY FINDINGS**\nThe area average anomaly of 2024 is 1.94\u00b11.09\u00b0C (\u00b1 means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2\u00b0C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 5.5\u00b0C. Near zero and slightly negative anomalies are observed in some areas of the Norwegian and Greenland Sea and around the Bering Strait.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00353\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "ice-surface-temperature", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-ist-arctic-anomaly", "satellite-observation", "sea-surface-temperature", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00353", "title": "Arctic Sea and Sea Ice Surface Temperature anomaly based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"description": "**DEFINITION **\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2024, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05  degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). Previously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\nThe basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104\u00b10.005 \u00b0C/year over the period 1982-2024 (43 years) which corresponds to an average warming of 4.47\u00b0C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2024 reveals regional peak warming exceeding 6\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00323\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "ice-surface-temperature", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-ist-arctic-area-averaged-anomalies", "satellite-observation", "sea-surface-temperature", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00323", "title": "Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"description": "**DEFINITION**\n\nThe OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2024. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (43 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2024. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages  from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**CMEMS KEY FINDINGS**\nSST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2024. The cumulative trends are upwards of 2\u00b0C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature  trend is 0.104+/-0.005\u00b0C/yr, i.e. an increase by around 4.47\u00b0C between 1982 and 2024. The 2d map of Arctic anomalies reveals regional peak warming exceeding 5.5\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00324\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "ice-surface-temperature", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-climate-sst-ist-arctic-trend", "satellite-observation", "sea-surface-temperature", "target-application#seaiceinformation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00324", "title": "Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"description": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2024 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from re-processed ESA SST CCI, C3S (Embury et al., 2019). Anomalies are computed against the 1991-2020 reference period. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nThe overall trend in the SST anomalies in this region is 0.016 \u00b10.001 \u00b0C/year over the period 1982-2024.  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00275\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. https://doi.org/10.1080/1755876X.2016.1273446\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "omi-climate-sst-northwestshelf-area-averaged-anomalies", "satellite-observation", "sea-surface-foundation-temperature", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00275", "title": "European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"description": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1982-2024, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from re-processed ESA SST CCI, C3S (Embury et al., 2019). Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT **\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nOver the period 1982-2024, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.016 \u00b1 0.001 \u00b0C/Year.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00276\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Embury, O., Merchant, C. J., Good, S. A., Rayner, N. A., H\u00f8yer, J. L., Atkinson, C., ... & Donlon, C. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data, 11(1), 326.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. https://doi.org/10.1080/1755876X.2016.1273446\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, https://doi.org/10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[-17.975000381469727, 48.025001525878906, 12.975000381469727, 61.974998474121094]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "omi-climate-sst-northwestshelf-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00276", "title": "European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_TEMPSAL_BALTIC_Stz_trend": {"description": "**DEFINITION**\n\nSubsurface salinity trend ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Mulet et al, 2018) and is derived from regional reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait) annual salinity and at each depth level. \n\n**CONTEXT**\n\nThe Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matth\u00e4us, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (V\u00e4li et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (Lehmann et al., 2022). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe subsurface salinity from 1993 to 2024 exhibits distinct variations at different depths. In the surface layer up to 25 meters, which aligns with the average upper mixed layer depth in the Baltic Sea, there is no discernible trend. The salinity trend increases steadily from zero at a 25-meter depth to 0.04 per year at 70 meters. The most pronounced trend, 0.045 per year, is found within the extended halocline layer ranging from 70 to 150 meters. It is noteworthy that there is a slight reduction in the salinity trend to 0.04 per year between depths of 150 and 220 meters. Although this decrease is minor, it suggests that salt transport into the extended halocline layer is more pronounced than into the deeper layers. The Major Baltic Inflows are responsible for the significant salinity trend of 0.05 per year observed in the deepest layer of the Baltic Sea.     \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00207\n\n**References:**\n\n* von Dewitz B, Tamm S, Ho\u00c8flich K, Voss R, Hinrichsen H-H., 2018. Use of existing hydrographic infrastructure to forecast the environmental spawning conditions for Eastern Baltic cod, PLoS ONE 13(5): e0196477, doi:10.1371/journal.pone.0196477\n* Kotta, J., Vanhatalo, J., J\u00e4nes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., Bobsien, I., Viitasalo, M., Virtanen, E., Nystr\u00f6m Sandman, A., Isaeus, M., Leidenberger, S., Jonsson, P.R., Johannesson, K., 2019. Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9: 1821, doi:10.1038/s41598-018-38416-3\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Sandrine Mulet, Bruno Buongiorno Nardelli, Simon Good, Andrea Pisano, Eric Greiner, Maeva Monier, Emmanuelle Autret, Lars Axell, Fredrik Boberg, Stefania Ciliberti, Marie Dr\u00e9villon, Riccardo Droghei, Owen Embury, J\u00e9rome Gourrion, Jacob H\u00f8yer, M\u00e9lanie Juza, John Kennedy, Benedicte Lemieux-Dudon, Elisaveta Peneva, Rebecca Reid, Simona Simoncelli, Andrea Storto, Jonathan Tinker, Karina von Schuckmann and Sarah L. Wakelin. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI:10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. 2.7 Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* V\u00e4li G, Meier HEM, Elken J, 2013, Simulated halocline variability in the baltic sea and its impact on hypoxia during 1961-2007, Journal of Geophysical Research: Oceans, 118(12), 6982\u20137000, DOI:10.1002/2013JC009192\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-tempsal-baltic-stz-trend", "so-trend", "so-trend-lower95p", "so-trend-upper95p", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00207", "title": "Baltic Sea Subsurface Salinity trend from Reanalysis"}, "OMI_CLIMATE_TEMPSAL_BALTIC_Ttz_trend": {"description": "**DEFINITION**\n\nSubsurface temperature trend ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Mulet et al, 2018) and is derived from regional reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. Horizontal averaging has been done over the Baltic Sea domain (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. \n\n**CONTEXT**\n\nThe Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Lepp\u00e4ranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matth\u00e4us, 1984). The 20\u201350\u202fm thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018).\n\n\n**KEY FINDINGS**\n\nAnalysis of subsurface temperatures from 1993 to 2024 indicates that the Baltic Sea is experiencing warming across all depth intervals. The temperature trend in the upper mixed layer (0-25 m) is approximately 0.055 \u00b0C/year, decreasing to 0.045 \u00b0C/year within the seasonal thermocline layer. A peak temperature trend of 0.065 \u00b0C/year is observed at a depth of 70 m, aligning with the base of the cold intermediate layer. Beyond this depth, the trend stabilizes, closely matching the 0.065 \u00b0C/year value. At a 95% confidence level, it can be stated that the Baltic Sea's warming is consistent with depth, averaging around 0.06 \u00b0C/year. Notably, recent trends show a significant increase; for instance, Savchuk's 2018 measurements indicate an average temperature trend of 0.04 \u00b0C/year in the Baltic Proper's deep layers (>60m) from 1979 to 2016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00208\n\n**References:**\n\n* Chubarenko, I., Stepanova, N. 2018. Cold intermediate layer of the Baltic Sea: Hypothesis of the formation of its core. Progress in Oceanography, 167, 1-10, doi: 10.1016/j.pocean.2018.06.012\n* Kahru, M., Elmgren, R., and Savchuk, O. P. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009\u20131018. doi: 10.5194/bg-13-1009-2016\n* Lepp\u00e4ranta, M., Myrberg, K. 2008. Physical Oceanography of the Baltic Sea. Springer, Praxis Publishing, Chichester, UK, pp. 370\n* Liblik, T., Lips, U. 2011. Characteristics and variability of the vertical thermohaline structure in the Gulf of Finland in summer. Boreal Environment Research, 16, 73-83.\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Savchuk, .P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-tempsal-baltic-ttz-trend", "thetao-trend", "thetao-trend-lower95p", "thetao-trend-upper95p", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00208", "title": "Baltic Sea Subsurface Temperature trend from Reanalysis"}, "OMI_CLIMATE_TEMPSAL_IBI_extreme_var_mean_and_anomaly": {"description": "**DEFINITION**\n\nThe Iberia Biscay Ireland (IBI) Sea Surface Temperature extreme from Reanalysis ocean monitoring indicator (OMI) (OMI_CLIMATE_TEMPSAL_IBI_extreme_var_temp_mean_and_anomaly)  is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different Copernicus Marine products are used to compute the indicator: The IBI Reanalysis (IBI_MULTIYEAR_PHY_005_002) and the IBI Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). \n\nTwo parameters have been considered for this OMI: \n\n* **Map of the 99th mean percentile**: It is obtained from the reanalysis product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2023). \n\n* **Anomaly of the 99th percentile in 2024**: The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2024 percentile. \n\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). \n\n**CONTEXT** \n\nThe Sea Surface Temperature (SST) is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). \n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The SST field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the IBI region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). \n\nUpwelling processes, taking place in the coastal margins, are also relevant in the IBI region. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). \n\n**CMEMS KEY FINDINGS** \n\nIn the IBI region, the 99th mean percentile for 1993-2023 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016. \n\nThe analysis of the 99th percentile SST anomaly for the year 2024 reveals that the northeastern Atlantic region, between latitudes 36\u00b0 N and 48\u00b0 N, experienced thermal anomalies exceeding twice the standard deviation. Similar anomalies are also observed near the northeastern Iberian Peninsula, suggesting that inshore and coastal areas may have been affected as well. In contrast, the upwelling region west of the Iberian Peninsula shows negative anomalies in maximum SST, indicating an intensification of upwelling processes in this area. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00254\n\n**References:**\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM., (2011): Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al., (2019): Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C., (2002): Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S., (2018): Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP., (2003): The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM., (2013): Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Locarnini, R.A., Mishonov, A.V., Antonov, J.I., Boyer, T.P., Garcia, H.E., Baranova, O.K., Zweng, M.M., Paver, C.R., Reagan, J.R., Johnson, D.R., et al., (2013). World ocean atlas 2013. In:Levitus S, Mishonov A, editors, technical editors. NOAAatlas NESDIS 73, 40 pp. (Volume 1: Temperature).\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M., (2018): Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F., (2016): Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S., (2018): Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A., (2016): Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "extent": {"spatial": {"bbox": [[-19, 26, 4.999999046325684, 56]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-tempsal-ibi-extreme-var-mean-and-anomaly", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "NOW Systems (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00254", "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_2000": {"description": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). \n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.3\u00b10.3 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00240\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Cazenave et al. (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-thsl-global-area-averaged-anomalies-0-2000", "satellite-observation", "thermosteric-change-in-mean-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00240", "title": "Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_700": {"description": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). \n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-700m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.0 \u00b1 0.2 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00239\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["2005-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-thsl-global-area-averaged-anomalies-0-700", "satellite-observation", "thermosteric-change-in-mean-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00239", "title": "Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_THSL_GLOBAL_trend": {"description": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m).\n\n**CONTEXT**\n\nMost of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60\u00b0 latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades.\n\n**CMEMS KEY FINDINGS**\n\nSignificant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -5 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00241\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Stammer D, Cazenave A, Ponte RM, Tamisiea ME (2013) Causes for contemporary regional sea level changes. Ann Rev Mar Sci 5:21\u201346. doi:10.1146/annurev-marine-121211-172406\n* Meyssignac, B., C. G. Piecuch, C. J. Merchant, M.-F. Racault, H. Palanisamy, C. MacIntosh, S. Sathyendranath, R. Brewin, 2016: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011\n", "extent": {"spatial": {"bbox": [[-180, -80, 179.75, 90]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-climate-thsl-global-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "Mercator Ocean International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00241", "title": "Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"description": "**DEFINITION**\n\nThe North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25\u00b0\u2013 62\u00b0N, 180\u00b0\u2013 250\u00b0E). The spatial and temporal pattern of the NPGO has been deduced over the [1950-2004] period using an empirical orthogonal function (EOF) decomposition on sea level and sea surface temperature fields produced by the Regional Ocean Modeling System (ROMS) (Di Lorenzo et al., 2008; Shchepetkin and McWilliams, 2005). Afterward, the sea level spatial pattern of the NPGO is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index.\nThe NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS and completed with SEALEVEL_GLO_PHY_L4_NRT _008_046 CMEMS products). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. They do not impact the general variability of the NPGO.  \n\n\"\"CONTEXT \"\"\n\nNPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40\u00b0N and the opposite south of 40\u00b0N. (Di Lorenzo et al., 2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of (Yi et al., 2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database.\n\n\"\"CMEMS KEY FINDINGS\"\" \n\nThe NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40\u00b0N and negative south of 40\u00b0N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00221\n\n**References:**\n\n* Ceballos, L. I., E. Di Lorenzo, C. D. Hoyos, N. Schneider and B. Taguchi, 2009: North Pacific Gyre Oscillation Synchronizes Climate Fluctuations in the Eastern and Western Boundary Systems. Journal of Climate, 22(19) 5163-5174, doi:10.1175/2009jcli2848.1\n* Chhak, K. C., E. Di Lorenzo, N. Schneider and P. F. Cummins, 2009: Forcing of Low-Frequency Ocean Variability in the Northeast Pacific. Journal of Climate, 22(5) 1255-1276, doi:10.1175/2008jcli2639.1.\n* Di Lorenzo, E., Fiechter, J., Schneider, N., Bracco, A., Miller, A.J., Franks, P.J.S., Bograd, S.J., Moore, A.M., Thomas, A.C., Crawford, W., Pe\u00f1a, A., Hermann, A.J., 2009. Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophys. Res. Lett. 36. https://doi.org/10.1029/2009GL038261\n* Di Lorenzo, E., Schneider, N., Cobb, K.M., Franks, P.J.S., Chhak, K., Miller, A.J., McWilliams, J.C., Bograd, S.J., Arango, H., Curchitser, E., Powell, T.M., Rivi\u00e8re, P., 2008. North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys. Res. Lett. 35. https://doi.org/10.1029/2007GL032838\n* Shchepetkin, A.F., McWilliams, J.C., 2005. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model. 9, 347\u2013404. https://doi.org/10.1016/j.ocemod.2004.08.002\n* Tranchant, B., Pujol, I., Di Lorenzo, E., Legeais, J.-F., 2019. The North Pacific Gyre Oscillation. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 3, s29\u2013s31. https://doi.org/10.1080/ 1755876X.2019.1633075\n* Yi, D.L., Gan, B., Wu, L., Miller, A.J., 2018. The North Pacific Gyre Oscillation and Mechanisms of Its Decadal Variability in CMIP5 Models. J. Clim. 31, 2487\u20132509. https://doi.org/10.1175/JCLI-D-17-0344.1\n* Walker, G.T., Bliss, E.W., 1932. World Weather V. Memoirs of the Royal Meteorological Society.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2026-04-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-climvar-pacific-npgo-sla-eof-mode-projection", "satellite-observation", "tendency-of-sea-surface-height-above-sea-level", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00221", "title": "North Pacific Gyre Oscillation from Observations Reprocessing"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"description": "**DEFINITION**\n\nTemperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68\u00b0N - 80\u00b0N, 18\u00b0E - 55\u00b0E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).\"\"\n\n**CONTEXT**\nAnomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020;  Husson et al., 2022).\n\n**KEY FINDINGS**\n\nThere is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00346\n\n**References:**\n\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS, Wernberg T. 2016. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr., 141, 227-238\n* Husson B, Lind S, Fossheim M, Kato-Solvang H, Skern-Mauritzen M, P\u00e9cuchet L, Ingvaldsen RB, Dolgov AV, Primicerio R. 2022. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic. Global Change Biol, 28, 3728-3744\n* Lien VS, Raj RP, Chatterjee S. 2024. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study. State of the Planet (in press)\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Clim Change, 8, 634-639\n* Oziel L, Baudena A, Ardyna M, Massicotte P, Randelhoff A, Sallee J-B, Ingvaldsen RB, Devred E, Babin M. 2020. Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean. Nat Commun., 11(1), 1705, doi:10.1038/s41467-020-15485-5\n* Skagseth \u00d8, Eldevik T, \u00c5rthun M, Asbj\u00f8rnsen H, Lien VS, Smedsrud LH. 2020. Reduced efficiency of the Barents Sea cooling machine. Nat Clim Change, doi.org/10.1038/s41558-020-0772-6\n* Smedsrud LH, Muilwijk M, Brakstad A, Madonna E, Lauvset SK, Spensberger C, Born A, Eldevik T, Drange H, Jeansson E, Li C, Olsen A, Skagseth \u00d8, Slater DA, Straneo F, V\u00e5ge K, \u00c5rthun M. 2022.\n* Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century. Rev Geophys., 60, e2020RG000725\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1991-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mhw-index-bottom", "mhw-index-surface", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-extreme-mhw-arctic-area-averaged-anomalies", "temperature-bottom", "temperature-surface", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "NERSC (Norway)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00346", "title": "Marine Heatwave Index in the Barents Sea from Reanalysis"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_baltic_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess.   \nThe Baltic Sea is affected by vertical land motion due to the Glacial Isostatic Adjustment (Ludwigsen et al., 2020) and consequently relative sea level trends (as measured by tide gauges) have been shown to be strongly negative, especially in the northern part of the basin. On the other hand, Baltic Sea absolute sea level trends (from altimetry-based observations) show statistically significant positive trends (Passaro et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nUp to 47 stations fulfill the completeness index criteria in 2023, two more than in 2022 (45). The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 and 0.77 m above mean sea level at the Finnish stations Kemi and Oulu, respectively) and the inner part of the Gulf of Finland (e.g.: 0.82 m above mean sea level in St. Petersburg, Russia). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby, Gotland Island-Sweden). Annual 99th percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 3 cm in Fredericia, Denmark) to 10-13 cm in the Gulf of Finland (e.g.: 12 cm in Hamina). Negative anomalies of 2023 99th percentile are observed in most of the domain (Gulf of Bothnia and Gulf of Finland) reaching maximum significant values of -20 cm in Kemi and KalixStoron. Positive anomalies of 2023 99th percentile are however found in the southwestern part of the basin, with maximum values reaching +12 cm in Fynshav (Denmark).\n\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00203\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Passaro M, M\u00fcller F L, Oelsmann J, Rautiainen L, Dettmering D, Hart-Davis MG, Abulaitijiang A, Andersen, OB, H\u00f8yer JL, Madsen, KS, Ringgaard IM, S\u00e4rkk\u00e4 J, Scarrott R, Schwatke C, Seitz F, Tuomi L, Restano M, and Benveniste J. 2021. Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front Mar Sci, 8, 647607, https://doi.org/10.3389/FMARS.2021.647607/BIBTEX.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-sl-baltic-slev-mean-and-anomaly-obs", "slev-percentile1-anomaly", "slev-percentile1-mean", "slev-percentile1-std", "slev-percentile1-targetyear", "slev-percentile99-anomaly", "slev-percentile99-mean", "slev-percentile99-std", "slev-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00203", "title": "Baltic Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess.   \nThe Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00253\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Chafik L, Nilsen JE\u00d8, Dangendorf S et al. 2019. North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era. Sci Rep 9, 1041. https://doi.org/10.1038/s41598-018-37603-6\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-sl-ibi-slev-mean-and-anomaly-obs", "slev-percentile1-anomaly", "slev-percentile1-mean", "slev-percentile1-std", "slev-percentile1-targetyear", "slev-percentile99-anomaly", "slev-percentile99-mean", "slev-percentile99-std", "slev-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00253", "title": "Iberia Biscay Ireland sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_medsea_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Mediterranean Sea shows statistically significant positive sea level trends over the whole basin. However, at sub-basin scale sea level trends show spatial variability arising from local circulation (Calafat et al., 2022; Meli et al., 2023).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled by 41 stations in 2023, 3 more than in 2022, including the first station in the African coast, in the Alboran Sea (Melilla). The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish, French and Italian coasts, ranging from around 0.20 m above mean sea level in Sicily and the Balearic Islands (e.g.: 0.22 m in Porto Empedocle; 0.23 m in Ibiza) to around 0.60 m above mean sea level in the Northern Adriatic Sea (e.g.: 0.63 m in Trieste, 0.61 m in Venice). The annual 99th percentiles standard deviation ranges between 2 cm in the Alboran Sea and Sicily to 8 cm in Marseille. The 2023 99th percentile anomalies present positive values in the central and northern part of the Mediterranean Sea, with the exception of Ibiza, in the Balearic Islands, and zero or slightly negative anomalies in the Spanish coast and South of Italy. However, these anomalies are only significant, when compared with the standard deviation of the annual percentiles in the record, at a few stations: Marseille (+12 cm), Ibiza (+8 cm), Trieste (+8 cm) and Venice (+7 cm).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00265\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J Geophys Res Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Legeais J-F, Llovel W, Melet A, and Meyssignac B. 2020. Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, s77\u2013s82, https://doi.org/10.1080/1755876X.2020.1785097.\n* Meli M, Camargo CML, Olivieri M, Slangen ABA, and Romagnoli C. 2023. Sea-level trend variability in the Mediterranean during the 1993\u20132019 period, Front Mar Sci, 10, 1150488, https://doi.org/10.3389/FMARS.2023.1150488/BIBTEX.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-extreme-sl-medsea-slev-mean-and-anomaly-obs", "slev-percentile1-anomaly", "slev-percentile1-mean", "slev-percentile1-std", "slev-percentile1-targetyear", "slev-percentile99-anomaly", "slev-percentile99-mean", "slev-percentile99-std", "slev-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00265", "title": "Mediterranean Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled by 33 stations in 2023, one less than in 2022 (32). The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.45 m above mean sea level in Tregde (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden). The 99th percentile anomalies for 2023 show overall slightly negative values except in the Kattegat (Eastern part), with maximum significant values of +11 cm in Hornbaek (Denmark), and +10 cm in Ringhals (Sweden).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00272\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Dettmering D, M\u00fcller FL, Oelsmann J, Passaro M, Schwatke C, Restano M, Benveniste J, and Seitz F. 2021. North SEAL: A new dataset of sea level changes in the North Sea from satellite altimetry, Earth Syst Sci Data, 13, 3733\u20133753, https://doi.org/10.5194/ESSD-13-3733-2021.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "omi-extreme-sl-northwestshelf-slev-mean-and-anomaly-obs", "slev-percentile1-anomaly", "slev-percentile1-mean", "slev-percentile1-std", "slev-percentile1-targetyear", "slev-percentile99-anomaly", "slev-percentile99-mean", "slev-percentile99-std", "slev-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00272", "title": "North West Shelf sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present values around 20.5\u00baC in the SouthEast of the Coast of Sweeden with 1.4-1.7\u00baC of standard deviation (std), 19.6-21.1\u00baC in the North Coast of Germany with 1.5-3.6\u00baC of std and 19.6-21.8\u00baC  in the Estonian Coast with 1.2-5.3\u00baC of std.\nResults for this year show negative anomalies in the Coast of Sweeden (-1.1/-0.4\u00baC) and either positive or negative anomalies in the North Coast of Germany (-1.2/+1.0) and in the Estonian Coast (-0.7/+0.6\u00baC) within the standard deviation margin in the respective areas.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00204\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. V., Masson-Delmotte, P., Zhai, A., Pirani, S.L., Connors, C., P\u00e9an, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M.I., Gomis, M., Huang, K., Leitzell, E., Lonnoy, J.B.R., Matthews, T.K., Maycock, T., Waterfield, O., Yelek\u00e7i, R., Yu, B. Zhou (eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. www.ipcc.ch/report/ar6/wg1/\n* IPCC. 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. H.-O., P\u00f6rtner, D.C., Roberts, M., Tignor, E.S., Poloczanska, K., Mintenbeck, A., Alegr\u00eda, M., Craig, S., Langsdorf, S., L\u00f6schke, V., M\u00f6ller, A., Okem, B., Rama (eds). Cambridge University Press, Cambridge, UK and New York, NY, USA. www.ipcc.ch/report/ar6/wg2/\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-sst-baltic-sst-mean-and-anomaly-obs", "sst-percentile1-anomaly", "sst-percentile1-mean", "sst-percentile1-std", "sst-percentile1-targetyear", "sst-percentile99-anomaly", "sst-percentile99-mean", "sst-percentile99-std", "sst-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00204", "title": "Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).  \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a range from 16-21\u00baC in the Southwest of the British Isles and the English Channel, 21-22\u00baC in the Galician Coast, 22-24\u00baC in the South of Bay of Biscay and the Gulf of Cadiz to 24.5\u00baC in the Canary Island. The standard deviations are between 0.6\u00baC and 1.8\u00baC in the Southwest of the British Isles and the English Channel and between 0.6\u00baC and 1.3\u00baC  in the south of Bay of Biscay with a narrow margin in the rest of the areas (0.5-0.8\u00baC).\nResults for this year show a general positive anomaly in all the areas reaching +0.5\u00baC in the Gulf of Cadiz, and +1.3\u00baC in the Southwest of the British Isles and the English Channel inside the std margin in these areas. Only one coastal stations in the Irish Sea present a low negative anomaly (-0.4\u00baC).  More significant are the positve anomalies found in the other areas: +1.2\u00baC in the South of Bay of Biscay  +1.5\u00ba in the Canary Island,  2.2\u00baC in the Galician Coast, all of them out of the std margin of the respective areas.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00255\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n* IPCC. 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. H.-O., P\u00f6rtner, D.C., Roberts, M., Tignor, E.S., Poloczanska, K., Mintenbeck, A., Alegr\u00eda, M., Craig, S., Langsdorf, S., L\u00f6schke, V., M\u00f6ller, A., Okem, B., Rama (eds). Cambridge University Press, Cambridge, UK and New York, NY, USA. www.ipcc.ch/report/ar6/wg2/\n* IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. V., Masson-Delmotte, P., Zhai, A., Pirani, S.L., Connors, C., P\u00e9an, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M.I., Gomis, M., Huang, K., Leitzell, E., Lonnoy, J.B.R., Matthews, T.K., Maycock, T., Waterfield, O., Yelek\u00e7i, R., Yu, B. Zhou (eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. www.ipcc.ch/report/ar6/wg1/\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-sst-ibi-sst-mean-and-anomaly-obs", "sst-percentile1-anomaly", "sst-percentile1-mean", "sst-percentile1-std", "sst-percentile1-targetyear", "sst-percentile99-anomaly", "sst-percentile99-mean", "sst-percentile99-std", "sst-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00255", "title": "Iberia Biscay Ireland sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).  \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.The Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present values from 25\u00baC in Ionian Sea and 26\u00ba in the Alboran sea and Gulf of Lion to 27\u00baC in the East of Iberian Peninsula. The standard deviation ranges from 0.6\u00baC to 1.2\u00baC in the Western Mediterranean and is around 2.2\u00baC in the Ionian Sea.\nResults for this year show a slight negative anomaly in the Ionian Sea (-1\u00baC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2\u00baC, almost two times the standard deviation in the area.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00267\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-z.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-extreme-sst-medsea-sst-mean-and-anomaly-obs", "sst-percentile1-anomaly", "sst-percentile1-mean", "sst-percentile1-std", "sst-percentile1-targetyear", "sst-percentile99-anomaly", "sst-percentile99-mean", "sst-percentile99-std", "sst-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00267", "title": "Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a range from 14-15\u00baC in the North of the British Isles, 16-19\u00baC in the West of the North Sea to 19-20\u00baC in the Helgoland Bight. The standard deviation ranges from 0.7-0.8\u00baC in the North of the British Isles, 0.6-2\u00baC in the West of the North Sea to 0.8-3\u00baC in  in the Helgoland Bight.\nResults for this year show positive moderate anomalies (+0.3/+1.0\u00baC) in all the positions except in one station in the West of the Noth Sea where the anomaly is negative (-0.3\u00baC), all of them inside the standard deviation margin.\n\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00274\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer JL, Karagali I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n* IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. V., Masson-Delmotte, P., Zhai, A., Pirani, S.L., Connors, C., P\u00e9an, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M.I., Gomis, M., Huang, K., Leitzell, E., Lonnoy, J.B.R., Matthews, T.K., Maycock, T., Waterfield, O., Yelek\u00e7i, R., Yu, B. Zhou (eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. www.ipcc.ch/report/ar6/wg1/\n* IPCC. 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. H.-O., P\u00f6rtner, D.C., Roberts, M., Tignor, E.S., Poloczanska, K., Mintenbeck, A., Alegr\u00eda, M., Craig, S., Langsdorf, S., L\u00f6schke, V., M\u00f6ller, A., Okem, B., Rama (eds). Cambridge University Press, Cambridge, UK and New York, NY, USA. www.ipcc.ch/report/ar6/wg2/\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "omi-extreme-sst-northwestshelf-sst-mean-and-anomaly-obs", "sst-percentile1-anomaly", "sst-percentile1-mean", "sst-percentile1-std", "sst-percentile1-targetyear", "sst-percentile99-anomaly", "sst-percentile99-mean", "sst-percentile99-std", "sst-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00274", "title": "North West Shelf sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_STATE_GLOBAL_trend": {"description": "**DEFINITION**\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest third of waves. This OMI provides global maps of the seasonal mean and trend of significant wave height (SWH), as well as time series in three oceanic regions of the same variables and their trends from 2002 to 2020, calculated from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum SWH for the selected period and region. The 95th percentile is the value below which 95% of the data points fall, indicating higher than normal wave heights. The mean and 95th percentile of SWH (in m) are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February and March, and July, August and September). Trends have been obtained using linear regression and are expressed in cm/yr. For the time series, the uncertainty around the trend was obtained from the linear regression, while the uncertainty around the mean and 95th percentile was bootstrapped. For the maps, if the p-value obtained from the linear regression is less than 0.05, the trend is considered significant.\n\n**CONTEXT**\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends, regionally and globally.\n\n**KEY FINDINGS**\n\nFrom 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere (a, b). The 95th percentile of wave heights (q95), increases faster than the average values, indicating that extreme waves are growing more rapidly than average wave height (a, b). Extreme SWH\u2019s global maps highlight heavily storms affected regions, including the western North Pacific, the North Atlantic and the eastern tropical Pacific (a). In the North Atlantic, SWH has increased in summertime (July August September) but decreased in winter. Specifically, the 95th percentile SWH trend is decreasing by 2.1 \u00b1 3.3 cm/year, while the mean SWH shows a decrease of 2.2 \u00b1 1.76 cm/year. In the south of Australia, during boreal winter, the 95th percentile SWH is increasing at 2.6 \u00b1 1.5 cm/year (c), with the mean SWH increasing by 0.5 \u00b1 0.66 cm/year (d). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 \u00b1 2.14 cm/year (c) and the mean SWH trend is 1.7 \u00b1 0.84 cm/year (d). These patterns highlight the complex and region-specific nature of wave height trends. Further discussion is available in A. Laloue et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00352", "extent": {"spatial": {"bbox": [[-179, -89, 179, 89]]}, "temporal": {"interval": [["2002-01-01T00:00:00Z", "2020-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-state-global-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00352", "title": "Global Ocean, extreme and mean significant wave height trends from satellite observations - seasonal trends"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).  \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the Baltic Sea, the particular bathymetry and geography of the basin intensify the seasonal and spatial fluctuations in wave conditions. No clear statistically significant trend in the sea state has been appreciated except a rising trend in significant wave height in winter season, linked with the reduction of sea ice coverage (Soomere, 2023; Tuomi et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles shown in the area are from 3 to 4 meters and the standard deviation ranges from 0.2 m to 0.4 m. \nResults for this year show a slight positive or negative anomaly in all the stations, from -0.19 m to +0.28 m, inside the margin of the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00199\n\n**References:**\n\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-wave-baltic-swh-mean-and-anomaly-obs", "swh-percentile1-anomaly", "swh-percentile1-mean", "swh-percentile1-std", "swh-percentile1-targetyear", "swh-percentile99-anomaly", "swh-percentile99-mean", "swh-percentile99-std", "swh-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00199", "title": "Baltic Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"description": "**DEFINITION**\n\nExtreme wave characteristics were derived by analysing and counting individual storm events and their long-term means and trends based on the Black Sea wave model reanalysis (product BLKSEA_MULTIYEAR_WAV_007_006). Storms were identified using the Weisse & G\u00fcnther (2007) method. The basis of the method is the definition of a severe event threshold (SET), which we define as the 99th percentile of the significant wave height (SWH). Events are counted when SWH exceeds and then drops below the SET at any grid point. Three additional key parameters - event lifetime (duration of SET exceedance), intensity (maximum SWH minus SET), and maximum event area (geographic extent of SET exceedance) - were calculated but are not included in this OMI. Annual averages of event number, lifetime, and intensity were computed to determine long-term means. This OMI comprises the annual mean event number, the anomaly for the most recent year in the multi-year dataset, and the linear trend of the annual event number as presented in Staneva et al. (2022). All qualifying events, regardless of location within the domain, contributed to the statistics, based on data from 1950 to -18M.\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect and in the frame of climate change, which also affects regional wind patterns and therewith the wave climate, it is important for coastal regions to gain insights into wave extreme characteristics and the related trends. These insights are crucial to initiate necessary abatement strategies especially in combination with extreme wave power statistics (see OMI OMI_EXTREME_WAVE_BLKSEA_wave_power) and has already successfully been applied to different regions such as the North Sea (Weisse and G\u00fcnther, 2007), Black Sea (Staneva et al., 2022), and South Atlantic (Gramcianinov et al.,  2023a and 2023b).\n\n**CMEMS KEY FINDINGS**\n\nThe yearly mean number of storm events is rather low in regions where the average annual lifetime and intensity of storms are high. In contrast, the number of events is high where their lifetime and intensity are low. While the southwest Black Sea is exposed to yearly mean storm event numbers of below the long-term spatial averages (7.3 events), it is observed that the yearly mean lifetime of the events in the same region is higher than the long-term averages. The extreme wave statistics based on the 99th percentile threshold of the significant wave height (SWH) are very similar to the wind sea wave parameter, and the swell contribution is much lower. On overall, the yearly trend of the storm events is slightly negative (-0.01 events/year) with two areas showing positive trends located in the very east and west. In terms of the mean number of storm events in 2023, the event numbers exceed the average numbers almost everywhere in the domain (+1.8 events on spatial average) indicating a relatively stormy year 2023.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00348\n\n**References:**\n\n* Gramcianinov, C.B., Staneva, J., de Camargo, R., & da Silva Dias, P.L. (2023): Changes in extreme wave events in the southwestern South Atlantic Ocean. Ocean Dynamics, doi:10.1007/s10236-023-01575-7\n* Gramcianinov, C.B., Staneva, J., Souza, C.R.G., Linhares, P., de Camargo, R., & da Silva Dias, P.L. (2023): Recent changes in extreme wave events in the south-western South Atlantic. In: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., & Neukermans, G. (eds.): 7th edition of the Copernicus Ocean State Report (OSR7). Copernicus Publications, State Planet, 1-osr7, 12, doi:10.5194/sp-1-osr7-12-2023\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n* Weisse, R., & G\u00fcnther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958\u20132002. Ocean Dynamics, 57(3), 161\u2013172, doi:10.1007/s10236-006-0094-x\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["1986-01-30T00:00:00Z", "1986-01-30T00:00:00Z"]]}}, "keywords": ["2022-anomaly-of-yearly-mean-number-of-wave-storm-events", "black-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-extreme-wave-blksea-recent-changes", "swh", "weather-climate-and-seasonal-forecasting", "wind-speed", "yearly-mean-number-of-wave-storm-events", "yearly-trend-of-mean-number-of-wave-storm-events"], "license": "proprietary", "providers": [{"name": "HEREON (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00348", "title": "Black Sea extreme wave events"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"description": "**DEFINITION**\n\nThe Wave Power P is defined by:\nP=(\u03c1g^2)/64\u03c0 H_s^2 T_e\nwhere \u03c1 is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10 based on the Black Sea wave model reanalysis (product BLKSEA_MULTIYEAR_WAV_007_006). The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset compared against the long-term average as presented in Staneva et al. (2022). The statistics are based on the period 1950 to -18M and are obtained from yearly averages.\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.\n\n**KEY FINDINGS**\n\nThe 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with an average value of 38 W/m/year and a maximum of 100 W/m/year, which is equivalent to a ~10-15% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2023 correlates well with that of the wind speed anomaly in 2023, revealing a clear positive wave power anomaly in vast regions of the Black Sea basin (8.5 kW/m on spatial average) with the maxima in the southern half of the domain indicating higher waves in 2023 compared to the average.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00350\n\n**References:**\n\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n", "extent": {"spatial": {"bbox": [[27.25, 40.5, 42, 47]]}, "temporal": {"interval": [["1986-01-30T00:00:00Z", "1986-01-30T00:00:00Z"]]}}, "keywords": ["2022-anomaly-of-yearly-average-of-99th-percentile-of-wave-power", "black-sea", "coastal-marine-environment", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-extreme-wave-blksea-wave-power", "swh", "weather-climate-and-seasonal-forecasting", "wind-speed", "yearly-average-of-99th-percentile-of-wave-power", "yearly-trend-of-99th-percentile-of-wave-power"], "license": "proprietary", "providers": [{"name": "HEREON (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00350", "title": "Black Sea wave power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al.  (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. \nResults for this year show slight negative anomalies in the Canary Island (-0.4/0.0m) and in the Gulf of Cadiz (-0.8m) barely out of the standard deviation range in both areas, slight positive or negative anomalies in the West of the British Isles (-0.6/+0.4m) and in the English Channel and the Irish Sea (-0.6/+0.3m) but inside the range of the standard deviation and a general positive anomaly in the Bay of Biscay reaching +1.0m but close to the limit of the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00250\n\n**References:**\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-extreme-wave-ibi-swh-mean-and-anomaly-obs", "swh-percentile1-anomaly", "swh-percentile1-mean", "swh-percentile1-std", "swh-percentile1-targetyear", "swh-percentile99-anomaly", "swh-percentile99-mean", "swh-percentile99-std", "swh-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00250", "title": "Iberia Biscay Ireland significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nFor the Mediterranean Sea an interesting publication (De Leo et al., 2024) analyses recent studies in this basin showing the variability in the different results and the difficulties to reach a consensus, especially in the mean wave conditions. The only significant conclusion is the positive trend in extreme values for the western Mediterranean Sea and in particular in the Gulf of Lion and in the Tyrrhenian Sea.\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a range from 1.5-3.5 in the Gibraltar Strait and Alboran Sea with 0.25-0.6 of standard deviation (std), 2-5m in the East coast of the Iberian Peninsula and Balearic Islands with 0.2-0.4m of std, 3-4m in the Aegean Sea with 0.4-0.6m of std to 2-5m in the Gulf of Lyon with 0.3-0.5m of std. \nResults for this year show a slight negative anomaly in the Gibraltar Strait reaching -0.95m and the Gulf of Lyon (-0.3/-0.7m) slightly over the std in the respective areas, close to zero anomaly in the Aegean Sea (-0.1m) and slight positive or negative anomalies in the East coast of the Iberian Peninsula and Balearic Islands (-0.4/+0.3m) inside the margin of the std.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00263\n\n**References:**\n\n* De Leo F, Briganti R & Besio G. 2024. Trends in ocean waves climate within the Mediterranean Sea: a review. Clim Dyn 62, 1555\u20131566. https://doi.org/10.1007/s00382-023-06984-4\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-extreme-wave-medsea-swh-mean-and-anomaly-obs", "swh-percentile1-anomaly", "swh-percentile1-mean", "swh-percentile1-std", "swh-percentile1-targetyear", "swh-percentile99-anomaly", "swh-percentile99-mean", "swh-percentile99-std", "swh-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00263", "title": "Mediterranean Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"description": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al.  (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young et al., 2011; Young & Ribal, 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS** \n\nThe mean 99th percentiles showed in the area present a wide range from 2.5 meters in the English Channel with 0.3m of standard deviation (std), 3-5m in the southern and central North Sea with 0.3-0.6m of std, 4 meters in the Skagerrak Strait with 0.6m of std, 6-7m in the northern North Sea with 0.4-0.5m of std to 8 meters in the NorthWest of the British Isles with 0.8-1.0m of std. \nResults for this year show either low positive or negative anomalies between -0.3m and +0.4m, inside the margin of the standard deviation, in the English Channel, the Skagerrak Strait and the southern and central North Sea except in the station 6200046 with a positive anomaly of 0.8m and a slight negative anomaly (-0.1/-0.5m) inside the margin of the std in the NorthWest of the British Isles and the northern North Sea.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00270\n\n**References:**\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "omi-extreme-wave-northwestshelf-swh-mean-and-anomaly-obs", "swh-percentile1-anomaly", "swh-percentile1-mean", "swh-percentile1-std", "swh-percentile1-targetyear", "swh-percentile99-anomaly", "swh-percentile99-mean", "swh-percentile99-std", "swh-percentile99-targetyear", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "INS-PUERTOS-MADRID-ES;INS-NOWSYSTEMS-MADRID-ES", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00270", "title": "North West Shelf significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WMF_MEDSEA_area_averaged_mean": {"description": "**DEFINITION**\n\nThe Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 (OSR2, von Schuckmann et al., 2018)  section 3.4 (Simoncelli and Pinardi, 2018): (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW).\nAnnual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria \u0394\u03c3 = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year.\nAnnual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021). Time spans from 1987 to the year preceding the current one [-1Y], operationally extended yearly.\n\n**CONTEXT**\n\nThe formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000).\nThe objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (MEDSEA_MULTIYEAR_PHY_006_004, Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. \nDeep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas: Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. \n\n**KEY FINDINGS**\n\nThe Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). \nThe Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibi\u0107 and \u0160anti\u0107 (2008). An intense deep water formation event is detected in 2012-2013 (Ga\u010di\u0107 et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021).\nCretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014).\nThe Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00318\n\n**References:**\n\n* Escudier R., Clementi E., Cipollone A., Pistoia J., Drudi M., Grandi A., Lyubartsev V., Lecci R., Aydogdu A., Delrosso D., Omar M., Masina S., Coppini G., Pinardi N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Ga\u010di\u0107, M., Civitarese, G., Kova\u010devi\u0107, V., Ursella, L., Bensi, M., Menna, M., et al. 2014. Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm. Ocean Sci. 10, 513\u2013522. doi: 10.5194/os-10-513-2014\n* Houpert, L., de Madron, X.D., Testor, P., Bosse, A., D\u2019Ortenzio, F., Bouin, M.N., Dausse, D., Le Goff, H., Kunesch, S., Labaste, M., et al. 2016. Observations of open-ocean deep convection in the northwestern Mediterranean Sea: seasonal and inter- annual variability of mixing and deep water masses for the 2007-2013 period. J Geophys Res Oceans. 121:8139\u20138171. doi:10.1002/ 2016JC011857.\n* Kontoyiannis, H., Theocharis, A., Nittis, K. 1999. Structures and characteristics of newly formed water masses in the NW levantine during 1986, 1992, 1995. In: Malanotte-Rizzoli P., Eremeev V.N., editor. The eastern Mediterranean as a laboratory basin for the assessment of contrasting ecosys- tems. NATO science series (series 2: environmental secur- ity), Vol. 51. Springer: Dordrecht.\n* Manca, B., Kovacevic, V., Gac\u030cic\u0301, M., Viezzoli, D. 2002. Dense water formation in the Southern Adriatic Sea and spreading into the Ionian Sea in the period 1997\u20131999. J Mar Sys. 33/ 34:33\u2013154.\n* Mihanovi\u0107, H., Vilibi\u0107, I., \u0160epi\u0107, J., Mati\u0107, F., Ljube\u0161i\u0107, Z., Mauri, E., Gerin, R., Notarstefano, G., Poulain, P.-M.. 2021. Observation, preconditioning and recurrence of exceptionally high salinities in the Adriatic Sea. Frontiers in Marine Science, Vol. 8, https://www.frontiersin.org/article/10.3389/fmars.2021.672210\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., ... & Bonaduce, A. 2015. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Progress in Oceanography, 132, 318-332\n* Schroeder, K., Gasparini, G.P., Tangherlini, M., Astraldi, M. 2006. Deep and intermediate water in the western Mediterranean under the influence of the eastern Mediterranean transient. Geophys Res Lett. 33. doi:10. 1028/2006GL02712.\n* Schroeder, K., Ribotti, A., Borghini, M., Sorgente, R., Perilli, A., Gasparini, G.P. 2008. An extensive western Mediterranean deep water renewal between 2004 and 2006. Geophys Res Lett. 35(18):L18605. doi:10.1029/2008GL035146.\n* Simoncelli, S. and Pinardi, N. 2018. Water mass formation processes in the Mediterranean sea over the past 30 years. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208.\n* Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I., Bouin, M.N., Waldman, R., Cassou, C., Sanchez-Gomez, E., et al. 2016. Characterizing, modelling and under- standing the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim Dyn. 1\u201332. doi:10.1007/s00382-016-3295-0.\n* Velaoras, D., Krokos, G., Nittis, K., Theocharis, A. 2014. Dense intermediate water outflow from the Cretan Sea: a salinity driven, recurrent phenomenon, connected to thermohaline circulation changes. J Geophys Res Oceans. 119:4797\u20134820. doi:10.1002/2014JC009937.\n* Vilibic\u0301, I., S\u030cantic\u0301, D. 2008. Deep water ventilation traced by Synechococcus cyanobacteria. Ocean Dyn 58:119\u2013125. doi:10.1007/s10236-008-0135-8.\n* Von Schuckmann K. et al. (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1987-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "in-situ-ts-profiles", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-extreme-wmf-medsea-area-averaged-mean", "sea-level", "water-mass-formation-rate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00318", "title": "Mediterranean Water Mass Formation Rates from Reanalysis"}, "OMI_HEALTH_BLOOM_BALTIC_spatiotemporal_coverage": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product for the Baltic Sea (OCEANCOLOUR_BAL_BGC_L3_MY_009_133) as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021; 2024). \nIn the Baltic Sea, the phytoplankton blooms occurring during summer are dominated by nitrogen-fixing cyanobacteria that can have subsurface and/or surface accumulations (Kahru et al., 2007). Consistent with the HELCOM environmental reporting (e.g. \u00d6berg 2018) the cyanobacterial subsurface and surface summer blooms are detected the applying the thresholds on Rrs at the wavelength of 555 and 670 nm. The chlorophyll dataset of the Baltic Sea MY product (OCEANCOLOUR_BAL_BGC_L3_MY_009_133) contains the cyanobacteria summer bloom mask variable (CYANOBLOOM) which reports whether the thresholds for the cyanobacterial blooms was exceeded on the daily Rrs images (Brando et al. 2024). The spatiotemporal coverage for cyanobacterial blooms is then computed as detailed in the Ocean State Report issues 2 (Raudsepp et al 2018) and in Brando et al. (2021).\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). \nAs cyanobacterial blooms are extremely patchy and the surface scum is unlikely to totally cover a 1 km resolution pixel, it is possible for both thresholds to be exceeded concurrently. Following Hansson and H\u00e5kansson (2007), the cyanobacteria summer bloom mask variable (CYANOBLOOM) in the MY regional chlorophyll dataset was aggregated from June to September (i.e., days 161 to 270) of each year for the three possible values (subsurface =1; surface=2, and 3: both subsurface and surface summer blooms detected).\n\n\n**KEY FINDINGS**\n\nOver the 19982024 period, the spatiotemporal coverage of cyanobacteria summer bloom in the Baltic Sea increased from 1998 to 2005, decreased from 2005 to 2012, and then increased from 2012 to 2019 and decreased to 2024. These oscillations without a consistent decadal trend, are inline to those reported from 1979 to date by previous satellite-based studies and the HELCOM results (e.g. Kahru and Elmgren,2014; Brando et al. ,2021). The maximum values were recorded in 2019 and 2005 (2.95 and 2.56 106 day km2, respectively). The lowest values observed between 1998 and 2001 are likely to reflect the lower spatial coverage of the OC-CCIv6 dataset in that period due to the availability of only one sensor. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00366\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2024. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. CopernicusMarine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf )).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.*HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Hansson, M.; H\u00e5kansson, B. The Baltic Algae Watch System\u2014A remote sensing application for monitoring cyanobacterial blooms in the Baltic Sea. J. Appl. Remote Sens 2007, 1, 011507, doi:10.1117/1.2834769.\n* Kahru, M.; Savchuk, O.; Elmgren, R. Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: Interannual and spatial variability. Mar. Ecol. Prog. Ser. 2007, 343, 15\u201323, doi:10.3354/meps06943.\n* Kahru, M., Elmgren, R., 2014. Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences, 11, 3619\u20133633. doi:10.5194/bg-11-3619-2014\n* \u00d6berg, J. Cyanobacterial Blooms in the Baltic Sea. HELCOM Baltic Sea Environment Fact Sheet 2017; HEL-COM: Helsinki, Finland, 2018.\n* Raudsepp, U.; She, J.; Brando, V.E.; Santoleri, R.; Sammartino, M.; Ko\u00f5uts, M.; Uiboupin, R.; Maljutenko, I. Phytoplankton blooms in the Baltic Sea. In Copernicus Marine Service Ocean State Report. Issue 3. J. Oper. Oceanogr. 2019, 12, s21\u2013s26.\n* Zibordi, G., J.-F. Berthon, F. M\u00e9lin, and D. D\u2019Alimonte. Cross-site consistent in situ measurements for satellite ocean color applications: the BiOMaP radiometric dataset. Remote Sens. Environ., 115 (8): 2104\u20132115, August 2011. ISSN 0034-4257. doi: 10.1016/j.rse.2011.04.013. URL https://\u00acwww.doi.org/\u00ac10.1016/\u00acj.rse.2011.04.013.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1998-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-health-bloom-baltic-spatiotemporal-coverage", "satellite-observation", "spatiotemporal-coverage", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00366", "title": "Baltic Sea summer bloom coverage"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms to remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\n**CONTEXT**\n\nPhytoplankton \u2013 and chlorophyll concentration , which is  a measure of  phytoplankton concentration \u2013 respond rapidly to changes in environmental conditions. Chlorophyll concentration is highly seasonal in the Arctic Ocean region due to a strong dependency on light and nutrient availability, which in turn are driven by seasonal sunlight and sea-ice cover dynamics, as well as changes in mixed layer. In the past two decades, an increase in annual net primary production by Arctic Ocean phytoplankton has been observed and linked to sea-ice decline (Arrigo and van Dijken, 2015); in the same line Kahru et al. (2011) have showed that chlorophyll concentration peaks are appearing increasingly earlier in the year in parts of the Arctic. It is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales in the area, in order to be able to separate potential long-term climate signals from natural variability in the short term.\n\n**CMEMS KEY FINDINGS**\n\nWhile the overall trend average for the 1997-2021 period in the Arctic Sea is positive (0.86 \u00b1 0.17 % per year), a continued plateau in the linear trend, initiated in 2013 is observed in the time series extension, with both the amplitude and the baseline of the cycle continuing to decrease during 2021 as reported for previous years (Sathyendranath et al., 2018). In particular, the annual average for the region in 2021 is 1.05 mg m-3 - a 30% reduction on 2020 values. There appears to be no appreciable changes in the timings or amplitude of the 2021 spring and autumn blooms. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00188\n\n**References:**\n\n* Arrigo, K. R., & van Dijken, G. L., 2015. Continued increases in Arctic Ocean primary production. Progress in Oceanography, 136, 60\u201370. doi: 10.1016/j.pocean.2015.05.002.\n* Kahru, M., Brotas, V., Manzano\u2010Sarabia, M., Mitchell, B. G., 2011. Are phytoplankton blooms occurring earlier in the Arctic? Global Change Biology, 17(4), 1733\u20131739. doi:10.1111/j.1365\u20102486.2010.02312.x.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018. 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. 10.1093/icesjms/fsp107.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater", "multi-year", "oceanographic-geographical-features", "omi-health-chl-arctic-oceancolour-area-averaged-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00188", "title": "Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deseasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\n**CONTEXT**\n\nPhytoplankton \u2013 and chlorophyll concentration as a proxy for phytoplankton \u2013 respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013).\n\n**CMEMS KEY FINDINGS**\n\nWhile the overall trend average for the 1997-2021 period in the North Atlantic Ocean is slightly positive (0.16 \u00b1 0.12 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2021 is 0.25 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2021, a lower peak chlorophyll concentration is observed in the timeseries extension. This decrease in peak concentration with respect to the previous year is contributing to the reduction trend.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00194\n\n**References:**\n\n* Henson, S. A., Dunne, J. P. , and Sarmiento, J. L., 2009, Decadal variability in North Atlantic phytoplankton blooms, J. Geophys. Res., 114, C04013, doi:10.1029/2008JC005139.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n* Zhai, L., Platt, T., Tang, C., Sathyendranath, S., Walne, A., 2013. The response of phytoplankton to climate variability associated with the North Atlantic Oscillation, Deep Sea Research Part II: Topical Studies in Oceanography, 93, 159-168, doi: 10.1016/j.dsr2.2013.04.009.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-health-chl-atlantic-oceancolour-area-averaged-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00194", "title": "North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"description": "**DEFINITION**\n\nWe have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value.  To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations.\n We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic.\n\n**CONTEXT**\n\nEutrophication is the process by which an excess of nutrients \u2013 mainly phosphorus and nitrogen \u2013 in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth \u2013 particularly algal blooms \u2013 resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. \nEutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. \n\n**CMEMS KEY FINDINGS**\n\nThe coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores.  In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00195\n\n**References:**\n\n* Anderson, D.M., Glibert, P.M. & Burkholder, J.M. (2002). Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries 25, 704\u2013726 /10.1007/BF02804901.\n* Balmer, M.B., Downing, J.A. (2011), Carbon dioxide concentrations in eutrophic lakes: undersaturation implies atmospheric uptake, Inland Waters, 1:2, 125-132, 10.5268/IW-1.2.366.\n* Breitburg, D., Levin, L.A., Oschlies, A., Gr\u00e9goire, M., Chavez, F.P., Conley, D.J., Gar\u00e7on, V., Gilbert, D., Guti\u00e9rrez, D., Isensee, K. and Jacinto, G.S. (2018). Declining oxygen in the global ocean and coastal waters. Science, 359 (6371), p.eaam7240.\n* Cai, W., Hu, X., Huang, W. (2011) Acidification of subsurface coastal waters enhanced by eutrophication. Nature Geosci 4, 766\u2013770, 10.1038/ngeo1297.\n* Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J. R., Martinelli, L. A., Seitzinger, S. P., Sutton, M. A. (2008). Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions, Science 320, 5878, 889-892, 10.1126/science.1136674.\n* Howarth, R.W., Anderson, D., Cloern, J., Elfring, C., Hopkinson, C., Lapointe, B., Malone, T., & Marcus, N., McGlathery, K., Sharpley, A., Walker, D. (2000). Nutrient pollution of coastal rivers, bays and seas. Issues in Ecology, 7.\n* Howarth, R.W., Marino, R. (2006). Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades, Limnology and Oceanography, 51(1, part 2), 10.4319/lo.2006.51.1_part_2.0364.\n* Jickells, T. D. (1998). Nutrient biogeochemistry of the coastal zone. Science 281, 217\u2013222. doi: 10.1126/science.281.5374.217\n* OSPAR ICG-EUT. Axe, P., Clausen, U., Leujak, W., Malcolm, S., Ruiter, H., Prins, T., Harvey, E.T. (2017). Eutrophication Status of the OSPAR Maritime Area. Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area.\n* Schindler, D. W. (2006) Recent advances in the understanding and management of eutrophication. Limnology and Oceanography, 51, 356-363.\n* Smith, V.H. (2003). Eutrophication of freshwater and coastal marine ecosystems a global problem. Environmental Science and Pollution Research, 10, 126\u2013139, 10.1065/espr2002.12.142.\n* Van Meerssche, E., Pinckney, J.L. (2019) Nutrient Loading Impacts on Estuarine Phytoplankton Size and Community Composition: Community-Based Indicators of Eutrophication. Estuaries and Coasts 42, 504\u2013512, 10.1007/s12237-018-0470-z.\n* Wallace, R.B., Baumann, H., Grear, J.S., Aller, R.B., Gobler, C.J. (2014). Coastal ocean acidification: The other eutrophication problem, Estuarine, Coastal and Shelf Science, 148, 1-13, 10.1016/j.ecss.2014.05.027.\n", "extent": {"spatial": {"bbox": [[-45.99479293823242, 20.00520896911621, 12.994791984558105, 65.99478912353516]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-health-chl-atlantic-oceancolour-eutrophication", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00195", "title": "North Atlantic Ocean Eutrophication from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_BAL_BGC_L3_MY_009_133) which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\nMonthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012).\n\n**KEY FINDINGS**\n\nOver the 1997\u20132024 period, the Baltic Sea shows a slight positive chlorophyll trend, with a slope of 0.34\u202f\u00b1\u202f0.41% per year, in line with the previous assessment. Annual maxima and minima remain relatively consistent, with the absolute maximum recorded in 2008 and the lowest values observed in 2004 and 2014. \n**DOI (product):**\nhttps://doi.org/10.48670/moi-00197\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b (accepted). Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals. Remote Sens. 2021, 13, x.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kabel K, Moros M, Porsche C, Neumann T, Adolphi F, Andersen TJ, Siegel H, Gerth M, Leipe T, Jansen E, Sinninghe Damste\u0301 JS. 2012. Impact of climate change on the health of the Baltic Sea ecosystem over the last 1000 years. Nat Clim Change. doi:10.1038/nclimate1595.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., et al., 2018. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1. Technical Report Centre for Environmental Data Analysis. doi:10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1997-06-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater", "multi-year", "oceanographic-geographical-features", "omi-health-chl-baltic-oceancolour-area-averaged-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00197", "title": "Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"description": "**DEFINITION**\n\nThis product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC (OCEANCOLOUR_BAL_BGC_L3_MY_009_133) which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b).\nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**KEY FINDINGS**\nOver the 1997\u20132024 period, the Baltic Sea shows a slight positive chlorophyll trend, with a slope of 0.34\u202f\u00b1\u202f0.41% per year, in line with the previous assessment. Annual maxima and minima remain relatively consistent, with the absolute maximum recorded in 2008 and the lowest values observed in 2004 and 2014. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00198\n\n**References:**\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "extent": {"spatial": {"bbox": [[9.25, 53.25, 30.24993896484375, 65.84907531738281]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-health-chl-baltic-oceancolour-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00198", "title": "Baltic Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_BAL_BGC_L3_MY_009_133). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014, Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003).\n\n**KEY FINDINGS**\n\nIn the Black Sea, the average chlorophyll trend for the 1997\u20132024 period is negative, at -1.04\u202f\u00b1\u202f0.98% per year. However, this decline is less pronounced than in previous assessments (covering 1997\u20132021,1997\u20132022 and 1997-2023). The overall negative trend is primarily driven by the significant drop in chlorophyll concentrations in 2002-2003 and the relative low concentration measured in the basin until 2020. This trend appears to reverse somewhat in the following years, with relatively high chlorophyll concentration observed in 2021-2023. The general decreasing trend in the Black Sea is consistent with the findings of Sathyendranath et al. (2018), who reported increasing chlorophyll levels across all European seas except the Black Sea.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00211\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. M\u00e9lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1997-06-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater", "multi-year", "oceanographic-geographical-features", "omi-health-chl-blksea-oceancolour-area-averaged-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00211", "title": "Black Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"description": "**DEFINITION**\n\nThis product includes the Black Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC (OCEANCOLOUR_BAL_BGC_L3_MY_009_133). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023).  \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003). Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**KEY FINDINGS**\n\nThe average chlorophyll trend in the Black Sea for the 1997\u20132024 period closely matches previous estimates (1997\u20132021,1997\u20132022 and 1997-2023), showing an average decline of -1.04% per year. The overall trend across the basin is negative, with weaker trends, occasionally non-significant, in the central region. A pronounced negative trend is observed on the western side of the basin, although slightly weaker compared to the overall 1997\u20132023 period. In the Sea of Azov, negative values are observed, with a notable reversal near the offshore area of the Don River. This widespread negative trend aligns with the findings of Bengil and Mavruk (2018), who reported a decline in chlorophyll levels during the post-eutrophication phase between 1997 and 2017.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00212\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Bengil, F., & Mavruk, S. (2018). Bio-optical trends of seas around Turkey: An assessment of the spatial and temporal variability. Oceanologia, 60(4), 488-499.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "extent": {"spatial": {"bbox": [[26.5, 40, 42.000640869140625, 48.000038146972656]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["black-sea", "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-health-chl-blksea-oceancolour-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00212", "title": "Black Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"description": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies.  A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the North Atlantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.14% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.39%, and is no longer statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24%  trend for the 1997-2020 period but is still statistically significant (p<0.05).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00226\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["area-type-oligotropic-gyre", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean", "multi-year", "oceanographic-geographical-features", "omi-health-chl-global-oceancolour-oligo-nag-area-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00226", "title": "North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"description": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre.  Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the North Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05).  It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.\nFor 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00227\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["area-type-oligotropic-gyre", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean", "multi-year", "oceanographic-geographical-features", "omi-health-chl-global-oceancolour-oligo-npg-area-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00227", "title": "North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"description": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies.  A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016.  \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Altantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05).  The last two years of the timeseries show an increased deviation from the mean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00228\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["area-type-oligotropic-gyre", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean", "multi-year", "oceanographic-geographical-features", "omi-health-chl-global-oceancolour-oligo-sag-area-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00228", "title": "South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"description": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data.  Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05).  An underlying low frequency signal is observed with a period of approximately a decade.\nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of  0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05).  In the last two years of the timeseries, an increase in the variation from the mean is observed.\nFor 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00229\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["area-type-oligotropic-gyre", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean", "multi-year", "oceanographic-geographical-features", "omi-health-chl-global-oceancolour-oligo-spg-area-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00229", "title": "South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"description": "**DEFINITION**\n\nThe trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. \n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**CMEMS KEY FINDINGS**\n\nThe average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension.  The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021.  \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00230\n\n**References:**\n\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018b, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n", "extent": {"spatial": {"bbox": [[-179.9791717529297, -89.97916412353516, 179.9791717529297, 89.97916412353516]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["change-in-mass-concentration-of-chlorophyll-in-seawater-over-time", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "omi-health-chl-global-oceancolour-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "PML (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00230", "title": "Global Ocean Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"description": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\n\n**CONTEXT**\n\nPhytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Nin\u0303o Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).\n\n**KEY FINDINGS**\n\nIn the Mediterranean Sea, the average chlorophyll trend for the 1997\u20132024 period is slightly negative, at -0.77\u202f\u00b1\u202f0.59% per year, reinforcing the findings of the previous releases. This result contrasts with the analysis by Sathyendranath et al. (2018), which reported increasing chlorophyll concentrations across all European seas. From around 2010\u20132011 onward, excluding the 2018\u20132019 period, a noticeable decline in chlorophyll levels is evident in the deseasonalized time series (green line) and in the observed maxima (grey line), particularly from 2015. This sustained decline over the past decade contributes to the overall negative trend observed in the Mediterranean Sea.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00259\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G. (2004). Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, 1 16. https://doi.org/10.1371/journal.pone.0155756.\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1997-06-01T00:00:00Z", "2024-12-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mass-concentration-of-chlorophyll-in-seawater", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-health-chl-medsea-oceancolour-area-averaged-mean", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00259", "title": "Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"description": "**DEFINITION**\n\nThis product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**KEY FINDINGS**\n\nThe chlorophyll trend in the Mediterranean Sea for the 1997\u20132024 period confirms the findings of the previous release, with predominantly negative values observed across most of the basin. On average, the region shows a trend of approximately -0.77% per year, slightly more negative than the overall trend reported previously. As in earlier assessments, weak positive trends persist in specific areas such as the northern Aegean Sea and the Sicily Channel. Compared to the 1997\u20132023 period, new positive trends are now evident in the Gulf of Lion.\nIn contrast to the findings of Salgado-Hernanz et al. (2019), which were based on satellite observations from 1998 to 2014, this analysis does not reveal a distinct difference between the western and eastern Mediterranean basins. Notably, the Ligurian Sea now exhibits a negative trend, diverging from the positive trends identified by Colella et al. (2016) for the 1998\u20132009 period and by Salgado-Hernanz et al. (2019) for 1998\u20132014. Similarly, the waters of the Northern Adriatic Sea show weak positive trends, differing from the strong negative trend previously reported by Colella et al. (2016), and also representing a shift from the positive values observed by Salgado-Hernanz et al. (2019).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00260\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 200.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Salgado-Hernanz, P. M., Racault, M. F., Font-Mu\u00f1oz, J. S., & Basterretxea, G. (2019). Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing. Remote Sensing of Environment, 221, 50-64.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "extent": {"spatial": {"bbox": [[-6, 30, 36.50048065185547, 45.9985466003418]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["change-in-mass-concentration-of-chlorophyll-in-seawater-over-time", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "omi-health-chl-medsea-oceancolour-trend", "satellite-observation", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00260", "title": "Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_cod_volume": {"description": "**DEFINITION**\n\nThe cod reproductive volume ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 3 (Raudsepp et al, 2019) and is derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).\n\nCONTEXT\n\nCod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4\u20135 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2\u20133, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022). \n\nCMEMS KEY FINDINGS\n\nTypically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km\u00b3. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km\u00b3, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km\u00b3 in 2018, there was a rise to 800 km\u00b3 in 2019. The cod reproductive volume hit a second peak of 1000 km\u00b3 in 2022, stabilized at 600 km\u00b3 and increased to 800 km3 by the end of 2024. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00196\n\n**References:**\n\n* Bryhn, A.C.., Bergek, S., Bergstr\u00f6m,U., Casini, M., Dahlgren, E., Ek, C., Hjelm, J., K\u00f6nigson, S., Ljungberg, P., Lundstr\u00f6m, K., Lunneryd, S.G., Oveg\u00e5rd, M., Sk\u00f6ld, M., Valentinsson, D., Vitale, F., Wennhage, H., 2022. Which factors can affect the productivity and dynamics of cod stocks in the Baltic Sea, Kattegat and Skagerrak? Ocean & Coastal Management, 223, 106154. https://doi.org/10.1016/j.ocecoaman.2022.106154\n* Cardinale, M., Modin, J., 1999. Changes in size-at-maturity of Baltic cod (Gadus morhua) during a period of large variations in stock size and environmental conditions. Vol. 41 (3), 285-295. https://doi.org/10.1016/S0165-7836(99)00021-1\n* Heikinheimo, O., 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Environ Res 13:457\n* ICES, 2019. Baltic Sea Ecoregion \u2013 Fisheries overview, ICES Advice, DOI:10.17895/ices.advice.5566\n* Karasiova, E.M., Voss, R., Eero, M., 2008. Long-term dynamics in eastern Baltic cod spawning time: from small scale reversible changes to a recent drastic shift. ICES CM 2008/J:03\n* MacKenzie, B., St. John, M., Wieland, K., 1996. Eastern Baltic cod: perspectives from existing data on processes affecting growth and survival of eggs and larvae. Mar Ecol Prog Ser Vol. 134: 265-281.\n* Plikshs, M., Hinrichsen, H. H., Elferts, D., Sics, I., Kornilovs, G., K\u00f6ster, F., 2015. Reproduction of Baltic cod, Gadus morhua (Actinopterygii: Gadiformes: Gadidae), in the Gotland Basin: Causes of annual variability. Acta Ichtyologica et Piscatoria, Vol. 45, No. 3, 2015, p. 247-258.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Westin, L., Nissling, A., 1991. Effects of salinity on spermatozoa motility, percentage of fertilized eggs and egg development of Baltic cod Gadus morhua, and implications for cod stock fluctuations in the Baltic. Mar. Biol. 108, 5 \u2013 9.\n* Wieland, K., Waller, U., Schnack, D., 1994. Development of Baltic cod eggs at different levels of temperature and oxygen content. Dana 10, 163 \u2013 177.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "crv", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-health-tempsaloxy-baltic-cod-volume", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00196", "title": "Baltic Sea Cod Reproductive Volume from Reanalysis"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_bottom_salinity_arkona_bornholm": {"description": "**DEFINITION**\n\nMajor Baltic inflow bottom salinity ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Raudsepp et al, 2018) and is derived from regional reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. Major Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_sto2tz_gotland). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred.\n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s Lehmann et al., 2022). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). \n\n**KEY FINDINGS**\n\nTime series data of bottom salinity variations in the Arkona Basin are instrumental for monitoring the sporadic nature of water inflow and outflow events. The bottom salinity in the Arkona Basin fluctuates between 11 and 25 g/kg. The highest recorded bottom salinity value is associated with the Major Baltic Inflow of 2014, while other significant salinity peaks align with the Major Baltic Inflows of 1993 and 2002. Low salinity episodes in the Arkona Basin mark the occasions of barotropic outflows of brackish water from the Baltic Sea. In the Bornholm Basin, the bottom salinity record indicates three Major Baltic Inflow events: the first in 1993, followed by 2002, and the most recent in 2014. Following the last Major Baltic Inflow, the bottom salinity in the Bornholm Basin rose to 20 g/kg. Over the subsequent nine years, it has declined to 16 g/kg. The winter of 2024/25 did not experience a Major Baltic Inflow.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00209\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz V, 2018, Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, doi: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, doi: 10.1080/1755876X.2018.1489208\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-health-tempsaloxy-baltic-mbi-bottom-salinity-arkona-bornholm", "satellite-observation", "sob-ark", "sob-bor", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00209", "title": "Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_sto2tz_gotland": {"description": "**DEFINITION**\n\nMajor Baltic inflow time/depth evolution S,T,O2 ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Raudsepp et al, 2018) and is derived from in-situ observations product INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032. Major Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_bottom_salinity_arkona_bornholm) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable us to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. \n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (Lehmann et al., 20225).\n\n\n**KEY FINDINGS**\n\nThe Major Baltic Inflows of 1993, 2002, and 2014 (OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_bottom_salinity_arkona_bornholm) present a distinct signal in the Gotland Basin, influencing water salinity, temperature, and dissolved oxygen up to a depth of 100 meters. Following each event, deep layer salinity in the Gotland Basin increases, reaching peak bottom salinities approximately 1.5 years later, with elevated salinity levels persisting for about three years. Post-2017, salinity below 150 meters has declined, while the halocline has risen, suggesting saline water movement to the Gotland Basin's intermediate layers. Typically, temperatures fall immediately after a Major Baltic Inflow, indicating the descent of cold water from nearby upstream regions to the Gotland Deep's bottom. From 1993 to 1997, deep water temperatures remained relatively low (below 6 \u00b0C). Since 1998, these waters have warmed, with even moderate inflows in 1997/98, 2006/07, and 2018/19 introducing warmer water to the Gotland Basin's bottom layer. From 2019 onwards, water warmer than 7 \u00b0C has filled the layer beneath 100 meters depth. The water temperature below the halocline has risen by approximately 2 \u00b0C since 1993, and the cold intermediate layer's temperature has also increased from 1993 to 2024. Oxygen levels begin to drop sharply after the temporary reoxygenation of the bottom waters. The decline in 2014 was attributed to a shortage of smaller inflows that could bring oxygen-rich water to the Gotland Basin (Neumann et al., 2017) and an increase in biological oxygen demand (Savchuk, 2018; Meier et al., 2018). Additionally, warmer water has accelerated oxygen consumption in the deep layer, leading to increased anoxia. By 2021, oxygen was completely depleted below the depth of 75 metres.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00210\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Bergen, B., Naumann, M., Herlemann, D.P.R., Gr\u00e4we, U., Labrenz, M., J\u00fcrgens, K., 2018. Impact of a Major inflow event on the composition and distribution of bacterioplankton communities in the Baltic Sea. Frontiers in Marine Science, 5:383, doi: 10.3389/fmars.2018.00383\n* Meier, H.E.M., V\u00e4li, G., Naumann, M., Eilola, K., Frauen, C., 2018. Recently Accelerated Oxygen Consumption Rates Amplify Deoxygenation in the Baltic Sea. , J. Geophys. Res. Oceans, doi:10.1029/2017JC013686|\n* Mohrholz, V., 2018. Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, DOI: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* Savchuk, P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2025-01-01T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "in-situ-observation", "marine-resources", "marine-safety", "multi-year", "numerical-model", "oceanographic-geographical-features", "omi-health-tempsaloxy-baltic-mbi-sto2tz-gotland", "satellite-observation", "sea-water-salinity", "sea-water-temperature", "volume-fraction-of-oxygen-in-sea-water", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "providers": [{"name": "BAL-TALTECH-TALLINN-EE", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00210", "title": "Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"description": "For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00320", "extent": {"spatial": {"bbox": [[-179.9875, -85.99375, 179.9875, -45.00625]]}, "temporal": {"interval": [["2023-02-02T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-concentration", "sea-ice-edge", "seaice-ant-phy-auto-l3-nrt-011-012", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00320", "title": "Antarctic Ocean - High Resolution Sea Ice Information"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"description": "Antarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00120", "extent": {"spatial": {"bbox": [[-179.75, -90, 180, -40.25]]}, "temporal": {"interval": [["2003-04-01T00:00:00Z", "2025-06-30T00:00:00Z"]]}}, "keywords": ["antarctic-ocean", "coastal-marine-environment", "eastward-sea-ice-velocity", "level-3", "marine-resources", "marine-safety", "multi-year", "northward-sea-ice-velocity", "oceanographic-geographical-features", "satellite-observation", "seaice-ant-phy-l3-my-011-018", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00120", "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"description": "Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00343", "extent": {"spatial": {"bbox": [[-180, 30.9833984375, 179.9975, 89.9975]]}, "temporal": {"interval": [["2014-10-03T20:11:44Z", "2026-05-09T23:32:04Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "floe-size", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-concentration", "seaice-arc-phy-auto-l3-mynrt-011-023", "stage-of-development", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00343", "title": "Arctic Ocean - High Resolution Sea Ice Information L3"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"description": "Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00344", "extent": {"spatial": {"bbox": [[-180, 30.9853515625, 179.995, 89.995]]}, "temporal": {"interval": [["2014-10-03T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-concentration", "seaice-arc-phy-auto-l4-mynrt-011-024", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00344", "title": "Arctic Ocean - High Resolution Sea Ice Information L4"}, "SEAICE_ARC_PHY_AUTO_L4_MY_011_025": {"description": "Daily sea ice age and sea ice age fractions with uncertainties in the period 1991 - 2025. Coverage:  Arctic Ocean. Resolution: 25 km.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00371", "extent": {"spatial": {"bbox": [[-180, 17, 179.79999999999416, 89.52519226074219]]}, "temporal": {"interval": [["1995-09-15T00:00:00Z", "2026-04-30T00:00:00Z"]]}}, "keywords": ["age-of-sea-ice", "arctic-ocean", "level-4", "multi-year", "oceanographic-geographical-features", "satellite-observation", "seaice-arc-phy-auto-l4-my-011-025", "target-application#seaiceclimate"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00371", "title": "Arctic Sea Ice Age"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"description": "Arctic Sea and Ice surface temperature\n**Detailed description:** Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00315", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-surface-temperature", "sea-surface-temperature", "seaice-arc-phy-climate-l3-my-011-021", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00315", "title": "Arctic Ocean - Sea and Ice Surface Temperature L3S REPROCESSED"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"description": "Arctic Sea and Ice surface temperature\n\n**Detailed description:**\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00123", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-surface-temperature", "sea-surface-temperature", "seaice-arc-phy-climate-l4-my-011-016", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00123", "title": "Arctic Ocean - Sea and Ice Surface Temperature L4 REPROCESSED"}, "SEAICE_ARC_PHY_SST-IST_L3S_NRT_011_022": {"description": "Arctic Sea and Sea-Ice surface temperature product based on observations from AVHRR on METOP, VIIRS on NPP and NOAA20 and SLSTR on Sentinel 3A/B. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00365", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["2025-11-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-surface-temperature", "sea-surface-temperature", "seaice-arc-phy-sst-ist-l3s-nrt-011-022", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00365", "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"description": "Arctic sea ice drift datasets at 3, 6 and 30 day lags during winter. \nThe Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution for ASCAT, QuikSCAT, CFOSAT and SSM/I data. \nAMSR sea ice drift datasets have a 31.25 km grid resolution.\nThese products are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are \u00ab merged \u00bb product from radiometer and scatterometer:\n\n* QuikSCAT, ASCAT observations for over 20 years\n\n* QuikSCAT, ASCAT & SSM/I merged products for over 20 years\n\n* AMSR observations for over 20 years\n\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n* CFOSAT observations\n\n\nThe exhaustive list can be found through the \u201cData access\u201d link from the menu.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00126", "extent": {"spatial": {"bbox": [[-179.5, 60, 180, 89.5]]}, "temporal": {"interval": [["1991-12-03T00:00:00Z", "2025-12-31T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "eastward-sea-ice-velocity", "level-3", "marine-resources", "marine-safety", "multi-year", "northward-sea-ice-velocity", "oceanographic-geographical-features", "satellite-observation", "sea-ice-thickness", "seaice-arc-seaice-l3-rep-observations-011-010", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00126", "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"description": "For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00128", "extent": {"spatial": {"bbox": [[-110.0025, 49.99950000000001, 89.99549999999998, 89.9955]]}, "temporal": {"interval": [["2020-09-01T12:10:00Z", "2027-07-31T07:55:00Z"]]}}, "keywords": ["arctic-ocean", "ca", "cb", "cc", "cd", "cf", "cn", "coastal-marine-environment", "concentration-range", "ct", "data-quality", "fa", "fb", "fc", "ice-poly-id-grid", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "polygon-id", "polygon-type", "sa", "satellite-observation", "sb", "sc", "sea-ice-area-fraction", "seaice-arc-seaice-l4-nrt-observations-011-002", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway - DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00128", "title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"description": "The iceberg product contains 9 (6+3) datasets:\nSix gridded datasets in netCDF format:\nIW, EW and RCMNL modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\nThree datasets \u2013 individual iceberg positions \u2013 in shapefile format:\nThe shapefile format allows the best representation of the icebergs. Each shapefile-dataset also includes a shapefile holding the polygonized satellite coverage\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00129", "extent": {"spatial": {"bbox": [[-180, 30, 179.9255828857422, 89.955]]}, "temporal": {"interval": [["2018-01-01T04:10:59Z", "2026-05-11T11:07:46Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "near-real-time", "number-of-icebergs-per-unit-area", "oceanographic-geographical-features", "satellite-observation", "seaice-arc-seaice-l4-nrt-observations-011-007", "target-application#seaiceforecastingapplication", "target-application#seaiceservices", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00129", "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1 & RCM"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"description": "Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00130", "extent": {"spatial": {"bbox": [[-179.97500610351562, 58, 179.97500610351562, 89.94999694824219]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-surface-temperature", "sea-surface-temperature", "seaice-arc-seaice-l4-nrt-observations-011-008", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00130", "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"description": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00131", "extent": {"spatial": {"bbox": [[9, 53.20000076293945, 31, 66.19999694824219]]}, "temporal": {"interval": [["1980-11-03T00:00:00Z", "2025-06-04T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-classification", "sea-ice-concentration", "sea-ice-extent", "sea-ice-thickness", "seaice-bal-phy-l4-my-011-019", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00131", "title": "Baltic Sea ice concentration, extent, and classification time series"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"description": "For the Baltic Sea, the operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00132\n\n**References:**\n\n* J. Karvonen, M. Simila, SAR-Based Estimation of the Baltic Sea Ice Motion, Proc. of the International Geoscience and Remote Sensing Symposium 2007 (IGARSS 07), pp. 2605-2608, 2007. (Unfortunately there is no publication of the new algorithm version yet).\n", "extent": {"spatial": {"bbox": [[9, 53.20000076293945, 31, 66.19999694824219]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-ice-extent", "sea-ice-thickness", "seaice-bal-seaice-l4-nrt-observations-011-004", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00132", "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"description": "For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively.  The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00133\n\n**References:**\n\n* J. Karvonen, Operational SAR-based sea ice drift monitoring over the Baltic Sea, Ocean Science, v. 8, pp. 473-483, (http://www.ocean-sci.net/8/473/2012/os-8-473-2012.html) 2012.\n", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-ice-thickness", "sea-ice-x-displacement", "sea-ice-x-velocity", "sea-ice-y-displacement", "sea-ice-y-velocity", "seaice-bal-seaice-l4-nrt-observations-011-011", "target-application#seaiceclimate", "target-application#seaiceforecastingapplication", "target-application#seaiceinformation", "target-application#seaiceservices", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00133", "title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"description": "Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00127", "extent": {"spatial": {"bbox": [[-180, 0, 179.867063395126, 90]]}, "temporal": {"interval": [["1995-10-01T00:00:00Z", "2024-04-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-thickness", "seaice-glo-phy-climate-l3-my-011-013", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00127", "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"description": "The  product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00328", "extent": {"spatial": {"bbox": [[-179.95, -89.95, 179.95000000000005, 89.95]]}, "temporal": {"interval": [["2014-10-06T00:00:00Z", "2025-11-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-x-displacement", "sea-ice-y-displacement", "seaice-glo-phy-l4-my-011-020", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DTUSPACE (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00328", "title": "Global Ocean - High Resolution SAR Sea Ice Drift Time Series"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"description": "Global sea ice thickness from merged  L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and April to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00125", "extent": {"spatial": {"bbox": [[-180, -90.1, 179.93360862819299, 89.99999999999974]]}, "temporal": {"interval": [["2023-10-18T00:00:00Z", "2026-04-18T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-thickness", "seaice-glo-phy-l4-nrt-011-014", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00125", "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"description": "For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00134", "extent": {"spatial": {"bbox": [[-180, -90.5, 180, 90.00000000000085]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-ice-classification", "sea-ice-x-displacement", "sea-ice-y-displacement", "seaice-glo-seaice-l4-nrt-observations-011-001", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00134", "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"description": "DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00135", "extent": {"spatial": {"bbox": [[-180, -90, 179.8999999999795, 90]]}, "temporal": {"interval": [["2022-01-02T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-x-displacement", "sea-ice-y-displacement", "seaice-glo-seaice-l4-nrt-observations-011-006", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DTUSPACE (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00135", "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"description": "Two sets of CDR and ICDR sea ice concentration datasets from the EUMETSAT OSI SAF. One set based on AMSR-E/AMSR2 data: OSI-458+OSI-438 (covering 2002-present), and one set based on SMMR/SSMI/SSMIS data, OSI-450-a1+OSI-430-a (covering 1978-2025). The sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). OSI-458 and OSI-430 were released in November 2022, OSI-450-a1 in June 2025 and OSI-438 will be released in May 2026. OSI-450 and OSI-430 will be frozen in April 2026.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00136\n\n**References:**\n\n* [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n", "extent": {"spatial": {"bbox": [[-180, -90, 179.8670654296875, 90]]}, "temporal": {"interval": [["1978-10-25T00:00:00Z", "2026-04-25T00:00:00Z"]]}}, "keywords": ["antarctic-ocean", "arctic-ocean", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "seaice-glo-seaice-l4-rep-observations-011-009", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00136", "title": "Global Ocean Sea Ice Concentration CDRs and ICDRs from PMW data (OSI-SAF)"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"description": "The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00138", "extent": {"spatial": {"bbox": [[25.96875, 39.96875, 42.03125, 48.03125]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", "2003-01-01T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "invariant", "level-4", "marine-resources", "marine-safety", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sealevel-blk-phy-mdt-l4-static-008-067", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00138", "title": "BLACK SEA MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"description": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00139", "extent": {"spatial": {"bbox": [[-35.047833000000026, 15.000005, 47.006004, 71.028958]]}, "temporal": {"interval": [["1992-10-03T07:53:03Z", "2025-10-18T23:42:44Z"]]}}, "keywords": ["baltic-sea", "black-sea", "coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-eur-phy-l3-my-008-061", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00139", "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"description": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\n\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00140", "extent": {"spatial": {"bbox": [[-31.028945999999998, 18.998475, 41.73606000000001, 67.004499]]}, "temporal": {"interval": [["2022-01-01T03:04:52Z", "2026-05-11T06:11:04.273494Z"]]}}, "keywords": ["baltic-sea", "black-sea", "coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-eur-phy-l3-nrt-008-059", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00140", "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"description": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00141", "extent": {"spatial": {"bbox": [[-30.0625, 19.9375, 42.0625, 66.0625]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-eur-phy-l4-my-008-068", "surface-geostrophic-eastward-sea-water-velocity", "surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid", "surface-geostrophic-northward-sea-water-velocity", "surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00141", "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"description": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00142", "extent": {"spatial": {"bbox": [[-30.0625, 19.9375, 42.0625, 66.0625]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["baltic-sea", "black-sea", "coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-eur-phy-l4-nrt-008-060", "surface-geostrophic-eastward-sea-water-velocity", "surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid", "surface-geostrophic-northward-sea-water-velocity", "surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00142", "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"description": "The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00337", "extent": {"spatial": {"bbox": [[-31.96875, 19.03125, 42.09375, 66.96875]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", "2003-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "invariant", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sealevel-eur-phy-mdt-l4-static-008-070", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00337", "title": "EUROPEAN SEAS MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"description": "DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00145", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2025-05-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-sea-level", "sealevel-glo-phy-climate-l4-my-008-057", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00145", "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"description": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details).\nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) \n\n**Associated products**\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00146", "extent": {"spatial": {"bbox": [[-180, -78.556412, 179.999999, 87.987923]]}, "temporal": {"interval": [["1992-10-03T01:42:25Z", "2025-10-18T23:50:14Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-glo-phy-l3-my-008-062", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00146", "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"description": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00147", "extent": {"spatial": {"bbox": [[-180, -78.55641200000001, 179.999999, 85.75155400000001]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-11T08:46:48Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-glo-phy-l3-nrt-008-044", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00147", "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"description": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00148", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2025-10-18T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-glo-phy-l4-my-008-047", "surface-geostrophic-eastward-sea-water-velocity", "surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid", "surface-geostrophic-northward-sea-water-velocity", "surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00148", "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"description": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00149", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sea-surface-height-above-sea-level", "sealevel-glo-phy-l4-nrt-008-046", "surface-geostrophic-eastward-sea-water-velocity", "surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid", "surface-geostrophic-northward-sea-water-velocity", "surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00149", "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"description": "Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It  is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the  DUACS products\n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00150", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", "2003-01-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "invariant", "level-4", "marine-resources", "marine-safety", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sealevel-glo-phy-mdt-008-063", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00150", "title": "GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"description": "In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00144", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "invariant", "level-4", "marine-resources", "marine-safety", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-sea-level", "sealevel-glo-phy-noise-l4-static-008-033", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00144", "title": "GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"description": "The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00151", "extent": {"spatial": {"bbox": [[-6.062497138977051, 29.02083969116211, 36.14582824707031, 47.0625]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", "2003-01-01T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "invariant", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "oceanographic-geographical-features", "satellite-observation", "sea-surface-height-above-geoid", "sealevel-med-phy-mdt-l4-static-008-066", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00151", "title": "MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY"}, "SST_ATL_PHY_L3S_MY_010_038": {"description": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive .  The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00311", "extent": {"spatial": {"bbox": [[-20.975, 8.925, 12.975, 61.975]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2026-01-05T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sst-atl-phy-l3s-my-010-038", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00311", "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "SST_ATL_PHY_L3S_NRT_010_037": {"description": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites .  The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00310", "extent": {"spatial": {"bbox": [[-23.989999771118164, 6.010000228881836, 15.989999771118164, 64.98999786376953]]}, "temporal": {"interval": [["2020-12-20T00:00:00Z", "2026-05-02T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sea-surface-temperature", "sst-atl-phy-l3s-nrt-010-037", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00310", "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"description": "For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00152", "extent": {"spatial": {"bbox": [[-20.989999771118164, 9.010000228881836, 12.989999771118164, 61.9900016784668]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2026-05-02T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-atl-sst-l4-nrt-observations-010-025", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00152", "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"description": "For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00153", "extent": {"spatial": {"bbox": [[-20.975, 8.925, 12.975, 61.975]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2025-11-04T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-atl-sst-l4-rep-observations-010-026", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00153", "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_L3S_MY_010_040": {"description": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis provides daily Level 3 Super-Collated fields of the sea surface temperature fields, at 0.02deg. x 0.02deg. horizontal resolution. It is produced by the DMI Optimal Interpolation (DMIOI) system (H\u00f8yer and She, 2007) as the first step prior to providing the high resolution (1/50deg. - approx. 2km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth. It uses satellite data from infra-red radiometers, from the ESA SST_cci v3.0 (Embury et al., 2024) and Copernicus C3S projects, namely L2P data from (A)ATSRs, SLSTR and AVHRR for the period 1982-2021, L3U data from SLSTR and AVHRR for 2022-July 19 2024 and L2P data from SLSTR and AVHRR from July 20 2024 onward. For the Sea Ice Concentration it uses the Baltic high resolution sea ice concentration data from the Copernicus Marine Service SI TAC (SEAICE_BAL_PHY_L4_MY_011_019). \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00312\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w\"\n", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bal-phy-l3s-my-010-040", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00312", "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"description": "For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP, NOAA20 and 21. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00309\n\n**References:**\n\n* Karagali I. and H\u00f8yer, J. L. (2014). Characterisation and quantification of regional diurnal cycles from SEVIRI. Ocean Science, 10 (5), 745-758.\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["2022-05-01T00:00:00Z", "2026-05-10T23:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bal-phy-subskin-l4-nrt-010-034", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00309", "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"description": "For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00154\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["2019-03-11T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bal-sst-l3s-nrt-observations-010-032", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00154", "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"description": "For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00155", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["2018-12-04T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bal-sst-l4-nrt-observations-010-007-b", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00155", "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"description": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis provides daily gap-free sea surface temperature fields, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution. It is produced by the DMI Optimal Interpolation (DMIOI) system (H\u00f8yer and She, 2007) to provide a high resolution (1/50deg. - approx. 2km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth. It uses satellite data from infra-red radiometers, from the ESA SST_cci v3.0 (Embury et al., 2024) and Copernicus C3S projects, namely L2P data from (A)ATSRs, SLSTR and AVHRR for the period 1982-2021, L3U data from SLSTR and AVHRR for 2022-July 19 2024 and L2P data from SLSTR and AVHRR from July 20 2024 onward. For the Sea Ice Concentration it uses the Baltic high resolution sea ice concentration data from the Copernicus Marine Service SI TAC (SEAICE_BAL_PHY_L4_MY_011_019). \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00156\n\n**References:**\n\n* H\u00f8yer, J. L., & Karagali, I. (2016). Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w\"\n", "extent": {"spatial": {"bbox": [[-10, 48, 30, 66]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["baltic-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-surface-temperature", "sst-bal-sst-l4-rep-observations-010-016", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "DMI (Denmark)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00156", "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_L3S_MY_010_041": {"description": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00313\n\n**References:**\n\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18. Pisano, A., Buongiorno Nardelli, B., Tronconi, C. & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sens. Environ. 176, 107\u2013116.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n", "extent": {"spatial": {"bbox": [[26.375, 38.724998474121094, 42.375, 48.775001525878906]]}, "temporal": {"interval": [["1981-08-25T00:00:00Z", "2026-04-11T00:00:00Z"]]}}, "keywords": ["adjusted-sea-surface-temperature", "black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sst-bs-phy-l3s-my-010-041", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00313", "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"description": "For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00157\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "extent": {"spatial": {"bbox": [[26.375, 38.75, 42.375, 48.8125]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-02T23:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-subskin-temperature", "sst-bs-phy-subskin-l4-nrt-010-035", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00157", "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"description": "This product provides daily (nighttime), merged multi-sensor (Level-3S, L3S) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Black Sea, at high (HR, 1/16\u00b0) and ultra-high (UHR, 1/100\u00b0) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values and is produced by the Italian National Research Council \u2013 Institute of Marine Sciences (CNR-ISMAR).\nL3S maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI.\nThese L3S data serve as input to an optimal interpolation procedure used to generate gap-free Level-4 (L4) SST fields, as implemented in product 010_006 (Buongiorno Nardelli et al., 2009; Buongiorno Nardelli et al., 2013). \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00158\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri (2013). High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16. https://doi.org/10.1016/j.rse.2012.10.012\n", "extent": {"spatial": {"bbox": [[26.375, 38.75, 42.375, 48.8125]]}, "temporal": {"interval": [["2008-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sea-surface-temperature", "sst-bs-sst-l3s-nrt-observations-010-013", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00158", "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"description": "This product provides daily (nighttime), gap-free (Level-4, L4) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Black Sea, at high (HR, 1/16\u00b0) and ultra-high (UHR, 1/100\u00b0) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values (centered at 00:00 UTC) and is produced by the Italian National Research Council \u2013 Institute of Marine Sciences (CNR-ISMAR).\nL4 maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI. A two-step algorithm allows to interpolate SST data at high and ultra-high spatial resolution, applying statistical techniques (Buongiorno Nardelli et al., 2009; Buongiorno Nardelli et al., 2013). \n\n**DOI (product):**  \nhttps://doi.org/10.48670/moi-00159\n\n**References:**\n\n* Buongiorno Nardelli B., S. Colella, R. Santoleri, M. Guarracino, A. Kholod, 2009: A re-analysis of Black Sea Surface Temperature, J. Mar. Sys.., doi:10.1016/j.jmarsys.2009.07.001\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "extent": {"spatial": {"bbox": [[26.375, 38.75, 42.375, 48.8125]]}, "temporal": {"interval": [["2008-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bs-sst-l4-nrt-observations-010-006", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00159", "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"description": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00160\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "extent": {"spatial": {"bbox": [[26.375, 38.724998474121094, 42.375, 48.775001525878906]]}, "temporal": {"interval": [["1981-08-25T00:00:00Z", "2026-04-11T00:00:00Z"]]}}, "keywords": ["black-sea", "coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-bs-sst-l4-rep-observations-010-022", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00160", "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_GLO_PHY_L3S_MY_010_039": {"description": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive .  The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00329", "extent": {"spatial": {"bbox": [[-179.9499969482422, -79.94999694824219, 179.9499969482422, 79.94999694824219]]}, "temporal": {"interval": [["1982-01-01T00:00:00Z", "2026-01-05T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sea-surface-temperature", "sst-glo-phy-l3s-my-010-039", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00329", "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_PHY_L4_NRT_010_005": {"description": "For The Global Ocean - The GHRSST Multi-Product Ensemble (GMPE) system has been implemented at the Met Office which takes inputs from various analysis production centres on a routine basis and produces ensemble products at 0.25deg.x0.25deg. horizontal resolution.\n \nA large number of sea surface temperature (SST) analyses are produced by various institutes around the world, making use of the SST observations provided by the Global High Resolution SST (GHRSST) project. These are used by a number of groups including: numerical weather prediction centres; ocean forecasting groups; climate monitoring and research groups. There is a requirement to develop international collaboration in this field in order to assess and inter-compare the different analyses, and to provide uncertainty estimates on both the analyses and observational products. The GMPE system has been developed for these purposes and is run on a daily basis at the Met Office, producing global ensemble median and standard deviations for SST on a regular 0.25 degree resolution global grid.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00378\n\n**References:**\n\n* www.ghrsst.org\n* Matthew Martin, Prasanjit Dash, Alexander Ignatov, Viva Banzon, Helen Beggs, Bruce Brasnett, Jean-Francois Cayula, James Cummings, Craig Donlon, Chelle Gentemann, Robert Grumbine, Shiro Ishizaki, Eileen Maturi, Richard W. Reynolds, Jonah Roberts-Jones, Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE), Deep Sea Research Part II: Topical Studies in Oceanography, Volumes 77\u201380, 2012, Pages 21-30, ISSN 0967-0645, https://doi.org/10.1016/j.dsr2.2012.04.013.\n", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["2026-01-01T00:00:00Z", "2026-05-09T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-glo-phy-l4-nrt-010-005", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00378", "title": "Global Ocean GMPE Sea Surface Temperature Multi Product Ensemble"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"description": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00164", "extent": {"spatial": {"bbox": [[-179.97500610351562, -79.9749984741211, 179.97500610351562, 79.9749984741211]]}, "temporal": {"interval": [["2020-12-20T00:00:00Z", "2026-05-02T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-3", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sea-surface-temperature", "sst-glo-sst-l3s-nrt-observations-010-010", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "IFREMER (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00164", "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"description": "For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00165\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, -89.9749984741211, 179.97500610351562, 89.9749984741211]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-surface-temperature", "sst-glo-sst-l4-nrt-observations-010-001", "target-application#seaiceforecastingapplication", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00165", "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"description": "The OSTIA (Worsfold et al. 2024) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Worsfold, M.; Good, S.; Atkinson, C.; Embury, O. Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System. Remote Sens. 2024, 16, 3358. https://doi.org/10.3390/rs16183358\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, -89.9749984741211, 179.97500610351562, 89.9749984741211]]}, "temporal": {"interval": [["1981-10-01T00:00:00Z", "2025-12-18T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-surface-temperature", "sst-glo-sst-l4-rep-observations-010-011", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00168", "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"description": "The C3S global Sea Surface and Sea Ice Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth and IST skin at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the ESA SST_cci v3.0 L3U data from (A)ATSRs, SLSTR and AVHRR, L2P data from the AMSRE and AMSR2 Passive Microwave Instruments (Embury et al., 2024) and L2P data from the AASTI and C3S IST CDR/ICDR v.1. The C3S level 4 SST/IST analyses were produced by running the DMI Optimal Interpolation (DMIOI) system (H\u00f8yer and She, 2007; H\u00f8yer et al., 2014; Nielsen-Englyst et al., 2023, Nielsen-Englyst et al., 2024) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth and sea ice surface temperature (IST) at the surface skin to cover surface temperatures in the global ocean, the sea ice and the marginal ice zone. It uses a Multi-Source Composite Sea-Ice concentration dataset (from a combination of EUMETSAT OSI-SAF OSI-450a (Lavergne et al., 2019), OSI-458, ESA CCI Sea ice CDR, SICCI-HR-SIC, U.S. National Ice Centre\u2019s (NIC) ice charts, Swedish Meteorological and Hydrological Institute (SHMI) and Finnish Meteorological Institute\u2019s (FMI) ice charts used for the Baltic region) developed at DMI for the purpose of the CARRA2 project (Pan-Arctic) and extended to the South Hemisphere. \n\nThe ESA SST CCI global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Embury et al., 2024). The ESA SST CCI  level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430-b; Lavergne et al., 2019). \n\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00169\n\n**References:**\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n* H\u00f8yer, J. L., Le Borgne, P., & Eastwood, S. (2014) A bias correction method for Arctic satellite sea surface temperature observations. Remote Sensing of Environment, 146, 201-213.\n* Nielsen-Englyst, P., H\u00f8yer, J. L., Kolbe, W. M., Dybkjar, G., Lavergne, T., Tonboe, R. T., Skarpalezos, S., Karagali, I. (2023) A combined sea and sea-ice surface temperature climate dataset of the Arctic, 1982\u20132021. Remote Sensing of Environment, 284, 113331, doi: https://doi.org/10.1016/j.rse.2022.113331.\n* Nielsen-Englyst, P., H\u00f8yer, J. L., Karagali, I., Kolbe, W. M., Tonboe, R. T., Pedersen, L. T. (2024) Impact of passive microwave observations on the estimation of Arctic sea surface temperatures. Remote Sensing of Environment, 301, 113949, doi: https://doi.org/10.1016/j.rse.2023.113949.\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w\n* Lavergne, T., S\u00f8rensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkj\u00e6r, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n", "extent": {"spatial": {"bbox": [[-179.97500610351562, -89.9749984741211, 179.97500610351562, 89.97500610351562]]}, "temporal": {"interval": [["1980-01-01T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-ice-area-fraction", "sea-water-temperature", "sst-glo-sst-l4-rep-observations-010-024", "target-application#seaiceclimate", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "SST-DMI-COPENHAGEN-DK;SST-METOFFICE-EXETER-UK", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00169", "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "SST_MED_PHY_L3S_MY_010_042": {"description": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00314\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "extent": {"spatial": {"bbox": [[-18.125, 30.125, 36.32500076293945, 46.025001525878906]]}, "temporal": {"interval": [["1981-08-25T00:00:00Z", "2026-04-11T00:00:00Z"]]}}, "keywords": ["adjusted-sea-surface-temperature", "coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sst-med-phy-l3s-my-010-042", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00314", "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"description": "For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[How to cite](https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00170\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "extent": {"spatial": {"bbox": [[-18.125, 30.25, 36.25, 46]]}, "temporal": {"interval": [["2019-01-01T00:00:00Z", "2026-05-02T23:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-subskin-temperature", "sst-med-phy-subskin-l4-nrt-010-036", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00170", "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"description": "This product provides daily (nighttime), merged multi-sensor (Level-3S, L3S) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Mediterranean Sea, at high (HR, 1/16\u00b0) and ultra-high (UHR, 1/100\u00b0) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values and is produced by the Italian National Research Council \u2013 Institute of Marine Sciences (CNR-ISMAR).\nL3S maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI.\nThese L3S data serve as input to an optimal interpolation procedure used to generate gap-free Level-4 (L4) SST fields, as implemented in product 010_004 (Buongiorno Nardelli et al., 2013). \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00171\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "extent": {"spatial": {"bbox": [[-18.125, 30.25, 36.25, 46]]}, "temporal": {"interval": [["2008-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-foundation-temperature", "sea-surface-temperature", "sst-med-sst-l3s-nrt-observations-010-012", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00171", "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"description": "This product provides daily (nighttime), gap-free (Level-4, L4) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Mediterranean Sea, at high (HR, 1/16\u00b0) and ultra-high (UHR, 1/100\u00b0) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values (centered at 00:00 UTC) and is produced by the Italian National Research Council \u2013 Institute of Marine Sciences (CNR-ISMAR).\nL4 maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI. A two-step algorithm allows to interpolate SST data at high and ultra-high spatial resolution, applying statistical techniques (Buongiorno Nardelli et al., 2013; Buongiorno Nardelli et al., 2015). Additionally, from 2024 onwards, an improved first-guess field has been used in the generation of the MED UHR L4 data, enhancing the product's spatial resolution of SST features and the accuracy of SST gradients via machine learning techniques (Fanelli et al., 2024).  \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00172\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Fanelli, C., Ciani, D., Pisano, A., & Buongiorno Nardelli, B. (2024). Deep Learning for Super-Resolution of Mediterranean Sea Surface Temperature Fields. EGUsphere, 2024, 1-18 (pre-print)\n* Buongiorno Nardelli, B., Pisano, A., Tronconi, C., & Santoleri, R. (2015). Evaluation of different covariance models for the operational interpolation of high resolution satellite Sea Surface Temperature data over the Mediterranean Sea. Remote Sensing of Environment, 164, 334-343. https://doi.org/10.1016/j.rse.2015.04.025\n", "extent": {"spatial": {"bbox": [[-18.125, 30.25, 36.25, 46]]}, "temporal": {"interval": [["2008-01-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-med-sst-l4-nrt-observations-010-004", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00172", "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"description": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00173\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "extent": {"spatial": {"bbox": [[-18.125, 30.125, 36.32500076293945, 46.025001525878906]]}, "temporal": {"interval": [["1981-08-25T00:00:00Z", "2026-04-11T00:00:00Z"]]}}, "keywords": ["coastal-marine-environment", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "oceanographic-geographical-features", "satellite-observation", "sea-surface-temperature", "sst-med-sst-l4-rep-observations-010-021", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00173", "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"description": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.\nUsing linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.\nThe dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).\nThe integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.\nThis product is processed by the WAVE-TAC multi-mission SAR data processing system.\nIt processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.\nOne file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00178", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "wave-glo-phy-spc-fwk-l3-nrt-014-002", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00178", "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC-FWK_L4_NRT_014_004": {"description": "Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding products. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. The ouput products corresponds to one file per month gathering all the swell systems at a global scale. This product is processed by the WAVE-TAC multi-mission SAR and CFOSAT/SWIM data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The spectral data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00175", "extent": {"spatial": {"bbox": [[-179.5, -66.5, 179.5, 64.5]]}, "temporal": {"interval": [["2021-11-01T00:00:00Z", "2026-05-22T21:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "wave-glo-phy-spc-fwk-l4-nrt-014-004", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00175", "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"description": "Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00174", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "wave-glo-phy-spc-l3-my-014-006", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00174", "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"description": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00382", "extent": {"spatial": {"bbox": [[0, 0, 0, 0]]}, "temporal": {"interval": [["1970-01-01T00:00:00.000000Z", "1970-01-01T00:00:00.000000Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "global-ocean", "iberian-biscay-irish-seas", "level-3", "mediterranean-sea", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-from-direction-at-variance-spectral-density-maximum", "sea-surface-wave-period-at-variance-spectral-density-maximum", "sea-surface-wave-significant-height", "wave-glo-phy-spc-l3-nrt-014-009", "wave-spectrum"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00382", "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"description": "Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nThis product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00176", "extent": {"spatial": {"bbox": [[-180, -78.635744, 179.999024, 82.569]]}, "temporal": {"interval": [["2002-01-15T06:29:22Z", "2020-12-31T23:59:14Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-significant-height", "wave-glo-phy-swh-l3-my-014-005", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00176", "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"description": "Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00179", "extent": {"spatial": {"bbox": [[-180, -82.649, 179.999999, 87.987402]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", "2026-05-11T10:52:10Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-3", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-significant-height", "wave-glo-phy-swh-l3-nrt-014-001", "weather-climate-and-seasonal-forecasting", "wind-speed"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00179", "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"description": "Multi-Year gridded multi-mission merged satellite significant wave height  based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00177", "extent": {"spatial": {"bbox": [[-180, -90, 179.5, 90]]}, "temporal": {"interval": [["2002-01-15T00:00:00Z", "2020-12-31T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-significant-height", "sea-surface-wave-significant-height-daily-maximum", "sea-surface-wave-significant-height-daily-mean", "sea-surface-wave-significant-height-daily-number-of-observations", "sea-surface-wave-significant-height-daily-standard-deviation", "sea-surface-wave-significant-height-flag", "sea-surface-wave-significant-height-number-of-observations", "wave-glo-phy-swh-l4-my-014-007", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00177", "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"description": "Near-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at  a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00180", "extent": {"spatial": {"bbox": [[-179, -89, 179, 89]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "global-ocean", "iberian-biscay-irish-seas", "level-4", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "oceanographic-geographical-features", "satellite-observation", "sea-surface-wave-significant-height", "wave-glo-phy-swh-l4-nrt-014-003", "weather-climate-and-seasonal-forecasting"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00180", "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"description": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00338", "extent": {"spatial": {"bbox": [[-89.99, 50.00999999999999, 86.99, 89.99]]}, "temporal": {"interval": [["2018-03-13T00:00:00Z", "2025-07-26T00:00:00Z"]]}}, "keywords": ["/observational-data/satellite", "cds-coriolis", "eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind", "wind-arc-phy-hr-l3-my-012-105", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-IFREMER-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00338", "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"description": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.'\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00330", "extent": {"spatial": {"bbox": [[-89.99, 50.00999999999999, 86.99, 89.99]]}, "temporal": {"interval": [["2024-04-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "eastward-wind", "level-3", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-arc-phy-hr-l3-nrt-012-100", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-CLS-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00330", "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"description": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00339", "extent": {"spatial": {"bbox": [[-21.091100692749023, 26.006000518798828, 12.994000434875488, 64.11949920654297]]}, "temporal": {"interval": [["2018-03-13T00:00:00Z", "2025-07-26T00:00:00Z"]]}}, "keywords": ["eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-atl-phy-hr-l3-my-012-106", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-IFREMER-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00339", "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"description": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00331", "extent": {"spatial": {"bbox": [[-19.993999481201172, 26.006000518798828, 12.994000434875488, 61.99399948120117]]}, "temporal": {"interval": [["2024-04-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["eastward-wind", "iberian-biscay-irish-seas", "level-3", "near-real-time", "north-west-shelf-seas", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-atl-phy-hr-l3-nrt-012-101", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-CLS-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00331", "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Atlantic Sea"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"description": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00340", "extent": {"spatial": {"bbox": [[9.005999565124512, 53.50600051879883, 30.5939998626709, 66.09400177001953]]}, "temporal": {"interval": [["2018-03-13T00:00:00Z", "2025-07-26T00:00:00Z"]]}}, "keywords": ["eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-bal-phy-hr-l3-my-012-107", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-IFREMER-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00340", "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"description": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00332", "extent": {"spatial": {"bbox": [[9.005999565124512, 53.50600051879883, 30.5939998626709, 66.09400177001953]]}, "temporal": {"interval": [["2024-04-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["baltic-sea", "eastward-wind", "level-3", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-bal-phy-hr-l3-nrt-012-102", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-CLS-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00332", "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"description": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00341", "extent": {"spatial": {"bbox": [[27.31599998474121, 40.816001892089844, 42.00400161743164, 47.5]]}, "temporal": {"interval": [["2018-03-13T00:00:00Z", "2025-07-26T00:00:00Z"]]}}, "keywords": ["eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-blk-phy-hr-l3-my-012-108", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-IFREMER-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00341", "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"description": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00333", "extent": {"spatial": {"bbox": [[27.31599998474121, 40.816001892089844, 42.00400161743164, 47.5]]}, "temporal": {"interval": [["2024-04-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["black-sea", "eastward-wind", "level-3", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-blk-phy-hr-l3-nrt-012-103", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-CLS-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00333", "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"description": "For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments.  The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00181", "extent": {"spatial": {"bbox": [[-179.875, -89.875, 179.875, 89.875]]}, "temporal": {"interval": [["1994-07-01T00:00:00Z", "2026-01-01T00:00:00Z"]]}}, "keywords": ["arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "eastward-wind", "global-ocean", "iberian-biscay-irish-seas", "level-4", "magnitude-of-surface-downward-stress", "marine-resources", "marine-safety", "mediterranean-sea", "multi-year", "north-west-shelf-seas", "northward-wind", "oceanographic-geographical-features", "satellite-observation", "surface-downward-eastward-stress", "surface-downward-northward-stress", "weather-climate-and-seasonal-forecasting", "wind-glo-phy-climate-l4-my-012-003", "wind-speed"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00181", "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L3_MY_012_005": {"description": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding  to the L2 swath products:\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00183", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["1991-08-01T00:00:00Z", "2026-01-31T00:00:00Z"]]}}, "keywords": ["air-density", "coastal-marine-environment", "eastward-wind", "global-ocean", "level-3", "magnitude-of-surface-downward-stress", "marine-resources", "marine-safety", "multi-year", "northward-wind", "oceanographic-geographical-features", "satellite-observation", "status-flag", "stress-curl", "stress-divergence", "surface-downward-eastward-stress", "surface-downward-northward-stress", "weather-climate-and-seasonal-forecasting", "wind-glo-phy-l3-my-012-005", "wind-speed", "wind-to-direction", "wvc-index"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00183", "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L3_NRT_012_002": {"description": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\n*The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\n*The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00182", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["2016-01-01T00:00:00Z", "2026-05-10T00:00:00Z"]]}}, "keywords": ["air-density", "arctic-ocean", "baltic-sea", "black-sea", "coastal-marine-environment", "eastward-wind", "global-ocean", "iberian-biscay-irish-seas", "level-3", "magnitude-of-surface-downward-stress", "marine-resources", "marine-safety", "mediterranean-sea", "near-real-time", "north-west-shelf-seas", "northward-wind", "oceanographic-geographical-features", "satellite-observation", "status-flag", "stress-curl", "stress-divergence", "surface-downward-eastward-stress", "surface-downward-northward-stress", "weather-climate-and-seasonal-forecasting", "wind-glo-phy-l3-nrt-012-002", "wind-speed", "wind-to-direction", "wvc-index", "wvc-index-eastward-wind"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00182", "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L4_MY_012_006": {"description": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00185", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["1994-06-01T00:00:00Z", "2026-01-21T23:00:00Z"]]}}, "keywords": ["air-density", "coastal-marine-environment", "eastward-wind", "global-ocean", "level-4", "marine-resources", "marine-safety", "multi-year", "northward-wind", "numerical-model", "oceanographic-geographical-features", "satellite-observation", "stress-curl", "stress-divergence", "surface-downward-eastward-stress", "surface-downward-northward-stress", "weather-climate-and-seasonal-forecasting", "wind-glo-phy-l4-my-012-006"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00185", "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L4_NRT_012_004": {"description": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00305", "extent": {"spatial": {"bbox": [[-179.9375, -89.9375, 179.9375, 89.9375]]}, "temporal": {"interval": [["2020-07-01T00:00:00Z", "2026-05-10T23:00:00Z"]]}}, "keywords": ["air-density", "coastal-marine-environment", "eastward-wind", "global-ocean", "level-4", "marine-resources", "marine-safety", "near-real-time", "northward-wind", "numerical-model", "oceanographic-geographical-features", "satellite-observation", "stress-curl", "stress-divergence", "surface-downward-eastward-stress", "surface-downward-northward-stress", "weather-climate-and-seasonal-forecasting", "wind-curl", "wind-divergence", "wind-glo-phy-l4-nrt-012-004"], "license": "proprietary", "processing:level": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/moi-00305", "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"description": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00342", "extent": {"spatial": {"bbox": [[-5.604000091552734, 30.120899200439453, 36.80400085449219, 46.00199890136719]]}, "temporal": {"interval": [["2018-03-13T00:00:00Z", "2024-12-31T00:00:00Z"]]}}, "keywords": ["eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-med-phy-hr-l3-my-012-109", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-IFREMER-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00342", "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"description": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00334", "extent": {"spatial": {"bbox": [[-5.604000091552734, 30.125999450683594, 36.80400085449219, 46.00199890136719]]}, "temporal": {"interval": [["2024-04-01T00:00:00Z", "2026-05-11T00:00:00Z"]]}}, "keywords": ["eastward-wind", "level-3", "mediterranean-sea", "near-real-time", "northward-wind", "oceanographic-geographical-features", "quality-flag", "quality-flag-wind-speed", "satellite-observation", "status-flag", "time", "wind-med-phy-hr-l3-nrt-012-104", "wind-speed", "wind-to-direction"], "license": "proprietary", "processing:level": "Level 3", "providers": [{"name": "WIND-CLS-BREST-FR", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "sci:doi": "10.48670/mds-00334", "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"ANTARCTIC_OMI_SI_extent": {"collection": "ANTARCTIC_OMI_SI_extent"}, "ANTARCTIC_OMI_SI_extent_obs": {"collection": "ANTARCTIC_OMI_SI_extent_obs"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"collection": "ARCTIC_ANALYSISFORECAST_BGC_002_004"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_002_001"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"collection": "ARCTIC_ANALYSIS_FORECAST_WAV_002_014"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"collection": "ARCTIC_MULTIYEAR_BGC_002_005"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"collection": "ARCTIC_MULTIYEAR_PHY_002_003"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"collection": "ARCTIC_MULTIYEAR_PHY_ICE_002_016"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"collection": "ARCTIC_MULTIYEAR_WAV_002_013"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"collection": "ARCTIC_OMI_SI_Transport_NordicSeas"}, "ARCTIC_OMI_SI_extent": {"collection": "ARCTIC_OMI_SI_extent"}, "ARCTIC_OMI_SI_extent_obs": {"collection": "ARCTIC_OMI_SI_extent_obs"}, "ARCTIC_OMI_TEMPSAL_FWC": {"collection": "ARCTIC_OMI_TEMPSAL_FWC"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"collection": "BALTICSEA_ANALYSISFORECAST_BGC_003_007"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"collection": "BALTICSEA_ANALYSISFORECAST_PHY_003_006"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"collection": "BALTICSEA_ANALYSISFORECAST_WAV_003_010"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"collection": "BALTICSEA_MULTIYEAR_BGC_003_012"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"collection": "BALTICSEA_MULTIYEAR_PHY_003_011"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"collection": "BALTICSEA_MULTIYEAR_WAV_003_015"}, "BATHYMETRY_GLO_PHY_COASTAL_L4_MY_016_001": {"collection": "BATHYMETRY_GLO_PHY_COASTAL_L4_MY_016_001"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"collection": "BLKSEA_ANALYSISFORECAST_BGC_007_010"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"collection": "BLKSEA_ANALYSISFORECAST_PHY_007_001"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"collection": "BLKSEA_ANALYSISFORECAST_WAV_007_003"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"collection": "BLKSEA_MULTIYEAR_BGC_007_005"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"collection": "BLKSEA_MULTIYEAR_PHY_007_004"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"collection": "BLKSEA_MULTIYEAR_WAV_007_006"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"collection": "BLKSEA_OMI_HEALTH_oxygen_trend"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"collection": "BLKSEA_OMI_TEMPSAL_sst_trend"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"collection": "GLOBAL_ANALYSISFORECAST_BGC_001_028"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"collection": "GLOBAL_ANALYSISFORECAST_PHY_001_024"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"collection": "GLOBAL_ANALYSISFORECAST_WAV_001_027"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"collection": "GLOBAL_MULTIYEAR_BGC_001_029"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"collection": "GLOBAL_MULTIYEAR_BGC_001_033"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"collection": "GLOBAL_MULTIYEAR_PHY_001_030"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"collection": "GLOBAL_MULTIYEAR_PHY_ENS_001_031"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"collection": "GLOBAL_MULTIYEAR_WAV_001_032"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"collection": "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"collection": "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_trend"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_26N_profile"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"collection": "GLOBAL_OMI_TEMPSAL_sst_trend"}, "GLOBAL_OMI_WMHE_heattrp": {"collection": "GLOBAL_OMI_WMHE_heattrp"}, "GLOBAL_OMI_WMHE_northward_mht": {"collection": "GLOBAL_OMI_WMHE_northward_mht"}, "GLOBAL_OMI_WMHE_voltrp": {"collection": "GLOBAL_OMI_WMHE_voltrp"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"collection": "IBI_ANALYSISFORECAST_BGC_005_004"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"collection": "IBI_ANALYSISFORECAST_PHY_005_001"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"collection": "IBI_ANALYSISFORECAST_WAV_005_005"}, "IBI_MULTIYEAR_BGC_005_003": {"collection": "IBI_MULTIYEAR_BGC_005_003"}, "IBI_MULTIYEAR_PHY_005_002": {"collection": "IBI_MULTIYEAR_PHY_005_002"}, "IBI_MULTIYEAR_WAV_005_006": {"collection": "IBI_MULTIYEAR_WAV_005_006"}, "IBI_OMI_CURRENTS_cui": {"collection": "IBI_OMI_CURRENTS_cui"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "IBI_OMI_SEASTATE_swi": {"collection": "IBI_OMI_SEASTATE_swi"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "IBI_OMI_WMHE_mow": {"collection": "IBI_OMI_WMHE_mow"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"collection": "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"collection": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"collection": "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"collection": "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"collection": "INSITU_GLO_BGC_DISCRETE_MY_013_046"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"collection": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"collection": "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"collection": "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"collection": "INSITU_GLO_PHY_TS_OA_MY_013_052"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"collection": "INSITU_GLO_PHY_TS_OA_NRT_013_002"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"collection": "INSITU_GLO_WAV_DISCRETE_MY_013_045"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"collection": "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"collection": "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"collection": "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"collection": "MEDSEA_ANALYSISFORECAST_BGC_006_014"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"collection": "MEDSEA_ANALYSISFORECAST_PHY_006_013"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"collection": "MEDSEA_ANALYSISFORECAST_WAV_006_017"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"collection": "MEDSEA_MULTIYEAR_BGC_006_008"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"collection": "MEDSEA_MULTIYEAR_PHY_006_004"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"collection": "MEDSEA_MULTIYEAR_WAV_006_012"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"collection": "MEDSEA_OMI_TEMPSAL_sst_trend"}, "MULTIOBS_GLO_BGC_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BGC_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"collection": "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009"}, "MULTIOBS_GLO_BGC_SURFACE_NRT_015_016": {"collection": "MULTIOBS_GLO_BGC_SURFACE_NRT_015_016"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"collection": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"collection": "MULTIOBS_GLO_PHY_MYNRT_015_003"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"collection": "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"collection": "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"collection": "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"collection": "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012"}, "MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007": {"collection": "MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"collection": "NWSHELF_ANALYSISFORECAST_BGC_004_002"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_004_013"}, "NWSHELF_ANALYSISFORECAST_PHY_LR_004_001": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_LR_004_001"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"collection": "NWSHELF_ANALYSISFORECAST_WAV_004_014"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"collection": "NWSHELF_MULTIYEAR_BGC_004_011"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"collection": "NWSHELF_MULTIYEAR_PHY_004_009"}, "NWSHELF_REANALYSIS_WAV_004_015": {"collection": "NWSHELF_REANALYSIS_WAV_004_015"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"collection": "OCEANCOLOUR_ARC_BGC_L3_MY_009_123"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"collection": "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"collection": "OCEANCOLOUR_ARC_BGC_L4_MY_009_124"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"collection": "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"collection": "OCEANCOLOUR_ATL_BGC_L3_MY_009_113"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"collection": "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"collection": "OCEANCOLOUR_ATL_BGC_L4_MY_009_118"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"collection": "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"collection": "OCEANCOLOUR_BAL_BGC_L3_MY_009_133"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"collection": "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"collection": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"collection": "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"collection": "OCEANCOLOUR_BLK_BGC_L3_MY_009_153"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"collection": "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"collection": "OCEANCOLOUR_BLK_BGC_L4_MY_009_154"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"collection": "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_103"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"collection": "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_104"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_108"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"collection": "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"collection": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"collection": "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"collection": "OCEANCOLOUR_MED_BGC_L3_MY_009_143"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"collection": "OCEANCOLOUR_MED_BGC_L3_NRT_009_141"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"collection": "OCEANCOLOUR_MED_BGC_L4_MY_009_144"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"collection": "OCEANCOLOUR_MED_BGC_L4_NRT_009_142"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209"}, "OMI_CIRCULATION_BOUNDARY_ATLANTIC_gulf_stream_destabilization_point": {"collection": "OMI_CIRCULATION_BOUNDARY_ATLANTIC_gulf_stream_destabilization_point"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"collection": "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"collection": "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"collection": "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"collection": "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_2000": {"collection": "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_2000"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_300": {"collection": "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_300"}, "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_700": {"collection": "OMI_CLIMATE_OHC_GLOBAL_area_averaged_anomalies_0_700"}, "OMI_CLIMATE_OHC_GLOBAL_trend": {"collection": "OMI_CLIMATE_OHC_GLOBAL_trend"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"collection": "OMI_CLIMATE_OSC_MEDSEA_volume_mean"}, "OMI_CLIMATE_SI_ARCTIC_transport": {"collection": "OMI_CLIMATE_SI_ARCTIC_transport"}, "OMI_CLIMATE_SI_BALTIC_extent": {"collection": "OMI_CLIMATE_SI_BALTIC_extent"}, "OMI_CLIMATE_SI_BALTIC_volume": {"collection": "OMI_CLIMATE_SI_BALTIC_volume"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"collection": "OMI_CLIMATE_SL_GLOBAL_regional_trends"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_BAL_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_trend": {"collection": "OMI_CLIMATE_SST_BAL_trend"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IBI_trend": {"collection": "OMI_CLIMATE_SST_IBI_trend"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_anomaly"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_trend"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_trend"}, "OMI_CLIMATE_TEMPSAL_BALTIC_Stz_trend": {"collection": "OMI_CLIMATE_TEMPSAL_BALTIC_Stz_trend"}, "OMI_CLIMATE_TEMPSAL_BALTIC_Ttz_trend": {"collection": "OMI_CLIMATE_TEMPSAL_BALTIC_Ttz_trend"}, "OMI_CLIMATE_TEMPSAL_IBI_extreme_var_mean_and_anomaly": {"collection": "OMI_CLIMATE_TEMPSAL_IBI_extreme_var_mean_and_anomaly"}, "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_2000": {"collection": "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_2000"}, "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_700": {"collection": "OMI_CLIMATE_THSL_GLOBAL_area_averaged_anomalies_0_700"}, "OMI_CLIMATE_THSL_GLOBAL_trend": {"collection": "OMI_CLIMATE_THSL_GLOBAL_trend"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"collection": "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"collection": "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_STATE_GLOBAL_trend": {"collection": "OMI_EXTREME_STATE_GLOBAL_trend"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"collection": "OMI_EXTREME_WAVE_BLKSEA_recent_changes"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"collection": "OMI_EXTREME_WAVE_BLKSEA_wave_power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WMF_MEDSEA_area_averaged_mean": {"collection": "OMI_EXTREME_WMF_MEDSEA_area_averaged_mean"}, "OMI_HEALTH_BLOOM_BALTIC_spatiotemporal_coverage": {"collection": "OMI_HEALTH_BLOOM_BALTIC_spatiotemporal_coverage"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_cod_volume": {"collection": "OMI_HEALTH_TEMPSALOXY_BALTIC_cod_volume"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_bottom_salinity_arkona_bornholm": {"collection": "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_bottom_salinity_arkona_bornholm"}, "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_sto2tz_gotland": {"collection": "OMI_HEALTH_TEMPSALOXY_BALTIC_mbi_sto2tz_gotland"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"collection": "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"collection": "SEAICE_ANT_PHY_L3_MY_011_018"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"collection": "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024"}, "SEAICE_ARC_PHY_AUTO_L4_MY_011_025": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MY_011_025"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"collection": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"collection": "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016"}, "SEAICE_ARC_PHY_SST-IST_L3S_NRT_011_022": {"collection": "SEAICE_ARC_PHY_SST-IST_L3S_NRT_011_022"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"collection": "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"collection": "SEAICE_BAL_PHY_L4_MY_011_019"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"collection": "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"collection": "SEAICE_GLO_PHY_L4_MY_011_020"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"collection": "SEAICE_GLO_PHY_L4_NRT_011_014"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"collection": "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"collection": "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"collection": "SEALEVEL_EUR_PHY_L3_MY_008_061"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"collection": "SEALEVEL_EUR_PHY_L3_NRT_008_059"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"collection": "SEALEVEL_EUR_PHY_L4_MY_008_068"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"collection": "SEALEVEL_EUR_PHY_L4_NRT_008_060"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"collection": "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"collection": "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"collection": "SEALEVEL_GLO_PHY_L3_MY_008_062"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"collection": "SEALEVEL_GLO_PHY_L3_NRT_008_044"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"collection": "SEALEVEL_GLO_PHY_L4_MY_008_047"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"collection": "SEALEVEL_GLO_PHY_L4_NRT_008_046"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"collection": "SEALEVEL_GLO_PHY_MDT_008_063"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"collection": "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"collection": "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066"}, "SST_ATL_PHY_L3S_MY_010_038": {"collection": "SST_ATL_PHY_L3S_MY_010_038"}, "SST_ATL_PHY_L3S_NRT_010_037": {"collection": "SST_ATL_PHY_L3S_NRT_010_037"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"collection": "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"collection": "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026"}, "SST_BAL_PHY_L3S_MY_010_040": {"collection": "SST_BAL_PHY_L3S_MY_010_040"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"collection": "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"collection": "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"collection": "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"collection": "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016"}, "SST_BS_PHY_L3S_MY_010_041": {"collection": "SST_BS_PHY_L3S_MY_010_041"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"collection": "SST_BS_PHY_SUBSKIN_L4_NRT_010_035"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"collection": "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"collection": "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"collection": "SST_BS_SST_L4_REP_OBSERVATIONS_010_022"}, "SST_GLO_PHY_L3S_MY_010_039": {"collection": "SST_GLO_PHY_L3S_MY_010_039"}, "SST_GLO_PHY_L4_NRT_010_005": {"collection": "SST_GLO_PHY_L4_NRT_010_005"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"collection": "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"collection": "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"}, "SST_MED_PHY_L3S_MY_010_042": {"collection": "SST_MED_PHY_L3S_MY_010_042"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"collection": "SST_MED_PHY_SUBSKIN_L4_NRT_010_036"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"collection": "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"collection": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"collection": "SST_MED_SST_L4_REP_OBSERVATIONS_010_021"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"collection": "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002"}, "WAVE_GLO_PHY_SPC-FWK_L4_NRT_014_004": {"collection": "WAVE_GLO_PHY_SPC-FWK_L4_NRT_014_004"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"collection": "WAVE_GLO_PHY_SPC_L3_MY_014_006"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"collection": "WAVE_GLO_PHY_SPC_L3_NRT_014_009"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"collection": "WAVE_GLO_PHY_SWH_L3_MY_014_005"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"collection": "WAVE_GLO_PHY_SWH_L3_NRT_014_001"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"collection": "WAVE_GLO_PHY_SWH_L4_MY_014_007"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"collection": "WAVE_GLO_PHY_SWH_L4_NRT_014_003"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"collection": "WIND_ARC_PHY_HR_L3_MY_012_105"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"collection": "WIND_ARC_PHY_HR_L3_NRT_012_100"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"collection": "WIND_ATL_PHY_HR_L3_MY_012_106"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"collection": "WIND_ATL_PHY_HR_L3_NRT_012_101"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"collection": "WIND_BAL_PHY_HR_L3_MY_012_107"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"collection": "WIND_BAL_PHY_HR_L3_NRT_012_102"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"collection": "WIND_BLK_PHY_HR_L3_MY_012_108"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"collection": "WIND_BLK_PHY_HR_L3_NRT_012_103"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"collection": "WIND_GLO_PHY_CLIMATE_L4_MY_012_003"}, "WIND_GLO_PHY_L3_MY_012_005": {"collection": "WIND_GLO_PHY_L3_MY_012_005"}, "WIND_GLO_PHY_L3_NRT_012_002": {"collection": "WIND_GLO_PHY_L3_NRT_012_002"}, "WIND_GLO_PHY_L4_MY_012_006": {"collection": "WIND_GLO_PHY_L4_MY_012_006"}, "WIND_GLO_PHY_L4_NRT_012_004": {"collection": "WIND_GLO_PHY_L4_NRT_012_004"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"collection": "WIND_MED_PHY_HR_L3_MY_012_109"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"collection": "WIND_MED_PHY_HR_L3_NRT_012_104"}}}, "earth_search": {"collections_config": {"cop-dem-glo-30": {"description": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-04-22T00:00:00Z", "2021-04-22T00:00:00Z"]]}}, "keywords": ["cop-dem-glo-30", "copernicus", "dem", "dsm", "elevation", "tandem-x"], "license": "proprietary", "platform": "tandem-x", "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"description": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-04-22T00:00:00Z", "2021-04-22T00:00:00Z"]]}}, "keywords": ["cop-dem-glo-90", "copernicus", "dem", "elevation", "tandem-x"], "license": "proprietary", "platform": "tandem-x", "title": "Copernicus DEM GLO-90"}, "landsat-c2-l2": {"description": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}, "instruments": ["tm", "etm+", "oli", "tirs"], "keywords": ["etm+", "global", "imagery", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2-l2", "nasa", "oli", "reflectance", "satellite", "temperature", "tirs", "tm", "usgs"], "license": "proprietary", "platform": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "title": "Landsat Collection 2 Level-2"}, "naip": {"description": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR).  NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA).  Data are captured at least once every three years for each state.  This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "extent": {"spatial": {"bbox": [[-160, 17, -67, 50]]}, "temporal": {"interval": [["2010-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "keywords": ["aerial", "afpo", "agriculture", "imagery", "naip", "united-states", "usda"], "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program"}, "sentinel-1-grd": {"constellation": "sentinel-1", "description": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2014-10-10T00:28:21Z", null]]}}, "keywords": ["c-band", "copernicus", "esa", "grd", "sar", "sentinel", "sentinel-1", "sentinel-1-grd", "sentinel-1a", "sentinel-1b"], "license": "proprietary", "platform": "sentinel-1a,sentinel-1b", "title": "Sentinel-1 Level-1C Ground Range Detected (GRD)"}, "sentinel-2-c1-l2a": {"constellation": "sentinel-2", "description": "Sentinel-2 Collection 1 Level-2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-06-27T10:25:31.456000Z", null]]}}, "instruments": ["msi"], "keywords": ["earth-observation", "esa", "msi", "sentinel", "sentinel-2", "sentinel-2-c1-l2a", "sentinel-2a", "sentinel-2b"], "license": "proprietary", "platform": "sentinel-2a,sentinel-2b", "title": "Sentinel-2 Collection 1 Level-2A"}, "sentinel-2-l1c": {"constellation": "sentinel-2", "description": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-06-27T10:25:31.456000Z", null]]}}, "instruments": ["msi"], "keywords": ["earth-observation", "esa", "msi", "sentinel", "sentinel-2", "sentinel-2-l1c", "sentinel-2a", "sentinel-2b"], "license": "proprietary", "platform": "sentinel-2a,sentinel-2b", "title": "Sentinel-2 Level-1C"}, "sentinel-2-l2a": {"constellation": "sentinel-2", "description": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-06-27T10:25:31.456000Z", null]]}}, "instruments": ["msi"], "keywords": ["earth-observation", "esa", "msi", "sentinel", "sentinel-2", "sentinel-2-l2a", "sentinel-2a", "sentinel-2b"], "license": "proprietary", "platform": "sentinel-2a,sentinel-2b", "title": "Sentinel-2 Level-2A"}, "sentinel-2-pre-c1-l2a": {"constellation": "sentinel-2", "description": "Sentinel-2 Pre-Collection 1 Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-06-27T10:25:31.456000Z", null]]}}, "instruments": ["msi"], "keywords": ["earth-observation", "esa", "msi", "sentinel", "sentinel-2", "sentinel-2-pre-c1-l2a", "sentinel-2a", "sentinel-2b"], "license": "proprietary", "platform": "sentinel-2a,sentinel-2b", "title": "Sentinel-2 Pre-Collection 1 Level-2A "}}, "providers_config": {"cop-dem-glo-30": {"_collection": "cop-dem-glo-30"}, "cop-dem-glo-90": {"_collection": "cop-dem-glo-90"}, "landsat-c2-l2": {"_collection": "landsat-c2-l2"}, "naip": {"_collection": "naip"}, "sentinel-1-grd": {"_collection": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"_collection": "sentinel-2-c1-l2a"}, "sentinel-2-l1c": {"_collection": "sentinel-2-l1c"}, "sentinel-2-l2a": {"_collection": "sentinel-2-l2a"}, "sentinel-2-pre-c1-l2a": {"_collection": "sentinel-2-pre-c1-l2a"}}}, "eocat": {"collections_config": {"ALOS": {"description": "ALOS Africa is a dataset of the best available (cloud minimal, below 10%) African coverage acquired by AVNIR-2 in OBS mode and PRISM in OB1 mode (all Backward, Nadir and Forward views, in separated products), two different collections one for each instrument. The processing level for both AVNIR-2 and PRISM products is L1B.", "extent": {"spatial": {"bbox": [[-26, -37, 53, 37]]}, "temporal": {"interval": [["2006-07-09T00:00:00.000Z", "2009-05-12T23:59:59.999Z"]]}}, "instruments": ["AVNIR-2"], "keywords": ["35-km-prism", "691.65-km", "70-km-avnir-2", "agriculture", "alos", "alos-1", "av2-obs-11", "avnir-2", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-terrestrial-hydrosphere->-snow/ice", "forestry", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "land-surface", "medium-resolution---mr-(20---500)-m", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "prism", "psm-ob1-11", "snow-and-ice", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ALOS-1", "title": "Alos African Coverage ESA archive"}, "ALOS.AVNIR-2.L1C": {"description": "This collection is providing access to the ALOS-1 AVNIR-2 (Advanced Visible and Near Infrared Radiometer type 2) L1C data acquired by ESA stations in the ADEN zone plus some worldwide data requested by European scientists. The ADEN zone (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf) was the area belonging to the European Data node and covered both the European and the African continents, large part of the Greenland and the Middle East.\r\nThe full mission is covered, obviously with gaps outside to the ADEN zone:\r\n\u2022 Time windows: from 2006-04-28 to 2011-04-20\r\n\u2022 Orbits: from 1375 to 27898\r\n\u2022 Path (corresponds to JAXA track number): from 1 to 670\r\n\u2022 Row (corresponds to JAXA scene centre frame number): from 370 to 5230\r\nOne single Level 1C product types is offered for the OBS instrument mode: AV2_OBS_1C.\r\nThe Level 1C product is a multispectral image (three bands in VIS and one in NIR) in GEOTIFF format with 10 m resolution.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2006-04-28T00:00:00.000Z", "2011-04-20T23:59:59.999Z"]]}}, "instruments": ["AVNIR-2"], "keywords": ["691.65-km", "70-km", "agriculture", "alos-1", "alos.avnir-2.l1c", "avnir-2", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "forestry", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ALOS-1", "title": "ALOS AVNIR-2 L1C"}, "ALOS.PALSAR.FBS.FBD.PLR.products": {"description": "The dataset contains all ESA acquisitions over the ADEN zone (Europe, Africa and the Middle East) plus some products received from JAXA over areas of interest around the world. Further information on ADEN zones can be found in this technical note (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf).\r\n\r\nALOS PALSAR products are available in following modes:\u2022 Fine Beam Single polarisation(FBS): single polarisation (HH or VV), swath 40-70km, resolution 10m, temporal coverage from 02/05/2006 to 30/03/2011\r\n\u2022 Fine Beam Double polarisation (FBD): double polarisation (HH/HV or VV/VH) ), swath 40-70km, resolution 10m, temporal coverage from 02/05/2006 to 30/03/2011\r\n\u2022 Polarimetry mode (PLR), with four polarisations simultaneously: swath 30km, resolution 30m, temporal coverage from 26/08/2006 to 14/04/2011\r\n\u2022 ScanSAR Burst mode 1 (WB1), single polarization: swath 250-350km, resolution 100m, temporal coverage from 12/06/2006 to 21/04/2011\r\n\r\nFollowing processing levels are available:\r\n\u2022 RAW( level 1.0): Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene.\r\n\u2022 GDH (level 1.5):Ground range Detected, Normal resolution product\r\n\u2022 GEC (level 1.5): Geocoded product", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2006-05-02T00:00:00.000Z", "2011-04-14T23:59:59.999Z"]]}}, "instruments": ["PALSAR"], "keywords": ["250-km-(wb1)", "30-km-(plr)", "40-70-km-(fbd-and-fbs)", "691.65-km", "alos-1", "alos.palsar.fbs.fbd.plr.products", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-environmental-governance/management", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-terrestrial-hydrosphere->-snow/ice", "environmental-governance-and-management", "fbd-gdh-1p", "fbd-gec-1p", "fbd-raw-0p", "fbd-slc-1p", "fbs-gdh-1p", "fbs-gec-1p", "fbs-raw-0p", "fbs-slc-1p", "imaging-radars", "l-band-(19.4---76.9)-cm", "land-surface", "medium-resolution---mr-(20---500)-m", "natural-hazards-and-disaster-risk", "palsar", "plr-gdh-1p", "plr-gec-1p", "plr-raw-0p", "plr-slc-1p", "snow-and-ice", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "wb1-gdh-1p", "wb1-gec-1p", "wb1-raw-0p"], "license": "other", "platform": "ALOS-1", "title": "ALOS PALSAR products"}, "ALOSIPY": {"description": "International Polar Year (IPY), focusing on the north and south polar regions, aimed to investigate the impact of how changes to the ice sheets affect ocean and climate change to the habitats in these regions. IPY was a collaborative project involving over sixty countries for two years from March 2007 to March 2009. To meet the project goal, world space agencies observed these regions intensively using their own Earth observation satellites. One of these satellites, ALOS - with the PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor - observed these regions independently from day-night conditions or weather conditions. Carrying on this initiative, ESA is providing the ALOS PALSAR IPY Antarctica dataset, which consists of full resolution ALOS PALSAR ScanSAR WB1 products (100m spatial resolution) over Antarctica from July 2008 (cycle 21) to December 2008 (Cycle 24) and from May 2009 (cycle 27) to March 2010 (cycle 31). Missing products between the two periods above is due to L0 data over Antarctica not being available in ADEN archives and not processed to L1. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://alos-ds.eo.esa.int/smcat/ALOSIPY/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-180, -90, 180, -50]]}, "temporal": {"interval": [["2008-07-25T00:00:00.000Z", "2010-03-31T23:59:59.999Z"]]}}, "instruments": ["PALSAR"], "keywords": ["250---360-km", "691.65-km", "alos-1", "alosipy", "earth-science->-cryosphere->-snow/ice", "earth-science->-oceans", "earth-science->-solid-earth", "earth-science->-terrestrial-hydrosphere->-snow/ice", "imaging-radars", "l-band-(19.4---76.9)-cm", "medium-resolution---mr-(20---500)-m", "oceans", "palsar", "psr-wb1-15", "snow-and-ice", "solid-earth", "sun-synchronous"], "license": "other", "platform": "ALOS-1", "title": "ALOS PALSAR International Polar Year Antarctica"}, "ALOS_PRISM_L1B": {"description": "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) L1B data acquired by ESA stations in the ADEN zone plus some data requested by European scientists over their areas of interest around the world. The ADEN zone (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf) was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East.\r\n\r\nThe full mission is covered, though with gaps outside of the ADEN zone:\r\n\r\nTime window: from 2006-07-09 to 2011-03-31\r\nOrbits: from 2425 to 24189\r\nPath (corresponds to JAXA track number): from 1 to 668\r\nRow (corresponds to JAXA scene centre frame number): from 55 to 7185.\r\nTwo different Level 1B product types (Panchromatic images in VIS-NIR bands, 2.5 m resolution at nadir) are offered, one for each available sensor mode:\r\n\r\nPSM_OB1_11 -> composed of up to three views; Nadir, Forward and Backward at 35 km swath\r\nPSM_OB2_11 -> composed of up to two views; Nadir view at 70 km width and Backward view at 35 km width.\r\nAll ALOS PRISM EO-SIP products have, at least, the Nadir view which is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the view ID according to the JAXA naming convention.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2006-07-09T00:00:00.000Z", "2011-03-31T23:59:59.999Z"]]}}, "instruments": ["PRISM"], "keywords": ["35-km", "691.65-km", "70-km", "alos-1", "alos-prism-l1b", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-surface-water", "human-dimensions", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "mapping-and-cartography", "natural-hazards-and-disaster-risk", "prism", "psm-ob1-11", "psm-ob2-11", "sun-synchronous", "surface-water", "terrestrial-hydrosphere", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ALOS-1", "title": "Alos PRISM L1B"}, "ASA_AP__0P_Scenes": {"description": "The ASAR Alternating Polarization Mode Level 0 (Co-polar and Cross-polar H and V) products contain time-ordered Annotated Instrument Source Packets (AISPs) corresponding to one of the three possible polarisation combinations: HH & HV, VV & VH and HH & VV, respectively. The echo samples in the AISPs have been compressed to 4 bits/sample using FBAQ. This is a high-rate, narrow swath mode, so data is only acquired for partial orbit segments. There are two co-registered images per acquisition and may be from one of seven different image swaths. The Level 0 product was produced systematically for all data acquired within this mode. Data Size: 56-100 km across track x 100 km along track There are three AP Mode Level 0 products: - ASA_APH_0P: The Cross-polar H Level 0 product corresponds to the polarisation combination HH/HV. - ASA_APV_0P: The Cross-polar V Level 0 product corresponds to the polarisation combination VV/VH. - ASA_APC_0P: The Co-polar Level 0 product corresponds to the polarisation combination HH/VV= H and H received/V transmit and V received.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-11-15T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asa-ap--0p-scenes", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "imaging-radars", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "snow-and-ice", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR AP Co- and Cross-polar L0 [ASA_APC/APH/APV_0P]"}, "AUX_Dynamic_Open": {"description": "The Level 2 ECMWF SMOS Auxiliary data product, openly available to all users, contains ECMWF data on the ISEA 4-9 DGG corresponding to SMOS half-orbit. It is used by both the ocean salinity and soil moisture operational processors to store the geophysical parameters from ECMWF forecasts. Access to other SMOS Level 1 and Level 2 &quot;dynamic&quot; and &quot;static&quot; auxiliary datasets is restricted to Cal/Val users. The detailed content of the SMOS Auxiliary Data Files (ADF) is described in the Products Specification documents available in the Resources section below.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2010-06-01T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "758-km", "aux-dynamic-open", "earth-science->-agriculture->-soils", "earth-science->-agriculture->-soils->-soil-moisture/water-content", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-salinity/density", "interferometric-radiometers", "land-surface", "miras", "oceans", "salinity-and-density", "smos", "soil-moisture", "soils", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS Auxiliary Data"}, "AVHRRLocalAreaCoverageImagery10": {"description": "Level-1B description\r\nThis collection is composed of AVHRR L1B products (1.1 km) reprocessed from the NOAA POES and Metop AVHRR sensors data acquired at the University of Dundee and University of Bern ground stations and from the ESA and University of Bern data historical archive.\r\nThe product format is the NOAA AVHRR Level 1B that combines the AVHRR data from the HRPT stream with ancillary information like Earth location and calibration data which can be applied by the user. Other appended parameters are time codes, quality indicators, solar and satellite angles and telemetry.\r\nTwo data collections cover Europe and the neighbouring regions in the period of 1 January 1981 to 31 December 2020 and the acquired data in the context of the 1-KM project in the \u201890s.\r\nDuring the early 1990\u2019s various groups, including the International Geosphere-Biosphere Programme (IGBP), the Commission of the European Communities (CEC), the Moderate Resolution Imaging Spectrometer (MODIS) Science Team and ESA concluded that a global land 1 KM AVHRR data set would have been crucial to study and develop algorithms for several land products for the Earth Observing System.\r\nUSGS, NOAA, ESA and other non-U.S. AVHRR receiving stations endorsed the initiative to collect a global land 1-km multi-temporal AVHRR data set over all land surfaces using NOAA's TIROS \"afternoon\" polar-orbiting satellite. On 1 April 1992, the project officially began up to the end of 1999 with the utilisation of 23 stations worldwide plus the NOAA local area coverage (LAC) on-board recorders. The global land 1-km AVHRR dataset is composed of 5 channels, raw AVHRR dataset at 1.1 km resolution from the NOAA-11 and NOAA-14 satellites covering land surfaces, inland water and coastal areas.\r\n\r\nLevel-1C Description\r\nThis data collection consists of measurements from the Advanced Very High Resolution Radiometer (AVHRR) at 1.1km full Local Area Coverage (LAC) resolution. It is based on the ESA AVHRR Level 1B European Data Set, a curated collection of AVHRR 1km data from 1981 to 2020 covering Europe, selected areas in Africa and the acquired data out-of-Europe in the context of the 1-KM project in the \u201890s (see the Level-1B description for details). The AVHRR LAC measurements were processed by the Remote Sensing Research Group of the University Bern, Switzerland. A landmark based navigation correction software adjusted time and satellite attitude to improve the georeferencing accuracy. The PyGAC software was used to convert the counts to reflectances for the visible and near-infrared channels 1, 2, 3A, and to brightness temperatures for the infrared channels 3B, 4, 5. The infrared calibration uses on-board calibration data and is satellite specific without cross-calibration between satellites. Due to the lack of on-board calibration data for the visible channels calculated coefficients from the CIMSS PATMOS-X project, version 2017r1, were used for the visible calibration aiming to minimize spectral differences among the various AVHRR sensors. \r\nThe data format is NetCDF. The calibrated AVHRR data are accompanied by coordinates, satellite and solar angles, additional metadata, and basic quality indicators. The NOAA nomenclature is used for the data record labelling it as a set of AVHRR L1C data.", "extent": {"spatial": {"bbox": [[-30, 35, 70, 90]]}, "temporal": {"interval": [["1981-01-01T00:00:00.000Z", "2020-12-31T23:59:59.999Z"]]}}, "instruments": ["AVHRR", "AVHRR"], "keywords": ["1.0", "3000-km", "804---870-km", "avh-l1b-1p", "avh-l1c-1p", "avhrr", "avhrrlocalareacoverageimagery10", "clouds", "earth-science->-atmosphere->-clouds", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-oceans->-ocean-temperature", "earth-science->-terrestrial-hydrosphere->-snow/ice", "essential-climate-variables", "imaging-spectrometers/radiometers", "low-resolution---lr-(500---1200)-m", "metop", "mwir-(3.0---6.0)-\u00b5m", "nir-(0.75---1.30)-\u00b5m", "noaa-poes", "ocean-temperature", "snow-and-ice", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "NOAA POES,Metop", "title": "AVHRR Level-1B/1C Local Area Coverage Imagery"}, "Cartosat-1.Euro-Maps.3D": {"description": "A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage.\r\n\r\nThe Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers:\r\n\r\nThe dsm layer (*_dsm.tif) contains the elevation heights as a geocoded raster file\r\nThe source layer (*_src.tif) contains information about the data source for each height value/pixel\r\nThe number layer (*_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM\r\nThe quality layer (*_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications\r\nThe accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel.\r\nThe ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types.\r\n\r\nEO-SIP product type\tDescription\r\nPAN_PAM_3O\tIRS-P5 Cartosat-1 ortho image\r\nDSM_DEM_3D\tIRS-P5 Cartosat-1 DSM", "extent": {"spatial": {"bbox": [[-33, 27, 47, 72]]}, "temporal": {"interval": [["2007-09-30T00:00:00.000Z", "2015-06-04T23:59:59.999Z"]]}}, "instruments": ["PAN"], "keywords": ["27km", "618-km", "cameras", "cartosat-1.euro-maps.3d", "diseases-and-pests", "dsm-dem-3d", "earth-science->-agriculture->-agricultural-plant-science->-plant-diseases/disorders/pests", "earth-science->-agriculture->-agricultural-plant-science->-weeds", "invasive-species", "irs-p5", "noxious-plants-or-invasive-plants", "pan", "pan-pam-3o", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "IRS-P5", "title": "Cartosat-1 Euro-Maps 3D"}, "CosmoSkyMed": {"description": "The COSMO-SkyMed ESA archive collection is a dataset of COSMO-SkyMed products that ESA collected over the years with worldwide coverage. The dataset regularly grows as ESA collects new products. The following list delineates the characteristics of the SAR measurement modes that are disseminated under ESA Third Party Missions (TPM). - STRIPMAP HIMAGE (HIM): achieving medium resolution (3m x 3m single look), wide swath imaging (swath extension \u226540 km) . - STRIPMAP PINGPONG (SPP): achieving medium resolution (15 m)), medium swath imaging (swath \u226530 km) with two radar polarization's selectable among HH, HV, VH and VV. - SCANSAR WIDE (SCW): achieving radar imaging with swath extension of 100x100 km2 and a spatial resolution of 30x30 m2. - SCANSAR HUGE (SCH): achieving radar imaging with swath extension of 200x200 km2 and a spatial resolution selectable of 100x100 m2. Processing Levels: - Level 1A - Single-look Complex Slant (SCSB and SCSU) : RAW data focused in slant range-azimuth projection, that is the sensor natural acquisition projection; product contains In-Phase and Quadrature of the focused data, weighted and radiometrically equalised. The processing of the 1A_SCSU product differs from that of the 1A_SCSB product for the following features: a non-weighted processing is performed, which means that windowing isn't applied on the processed bandwidth; radiometric equalisation (in terms of compensation of the range antenna pattern and incidence angle) is not performed; hence only compensation of the antenna transmitter gain and receiver attenuation and range spreading loss is applied.\u2022 Level 1B - Detected Ground Multi-look (DGM): product obtained detecting, multi-looking and projecting the Single-look Complex Slant data onto a grid regular in ground. Spotlight Mode products are not multi-looked - Level 1C - Geocoded Ellipsoid Corrected (GEC) and Level 1D - Geocoded Terrain Corrected (GTC): Obtained projecting the Level 1A product onto a regular grid in a chosen cartographic reference system. In case of Lev 1C the surface is the earth ellipsoid while for the Lev 1D a DEM (Digital Elevation Model) is used to approximate the real earth surface. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/CosmoSkyMed/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2008-09-15T00:00:00.000Z", null]]}}, "instruments": ["SAR"], "keywords": ["100-km-for-scansar-wide", "200-km-for-scansar-huge", "30-km-stripmap-ping-pong", "40-km-stripmap-himage", "619.6-km", "cosmo-skymed", "cosmoskymed", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-tectonic-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans->-marine-environment-monitoring", "earth-science->-solid-earth->-geomorphic-landforms/processes", "geomorphic-landforms-and-processes", "high-resolution---hr-(5---20)-m", "imaging-radars", "marine-environment-monitoring", "medium-resolution---mr-(20---500)-m", "natural-hazards-and-disaster-risk", "sar", "sar-him-1b", "sar-him-1c", "sar-him-1d", "sar-him-ab", "sar-him-au", "sar-sch-1b", "sar-sch-1c", "sar-sch-1d", "sar-sch-ab", "sar-sch-au", "sar-scw-1b", "sar-scw-1c", "sar-scw-1d", "sar-scw-ab", "sar-scw-au", "sar-spp-1b", "sar-spp-1c", "sar-spp-1d", "sar-spp-ab", "sar-spp-au", "soils", "sun-synchronous", "topography", "vegetation", "very-high-resolution---vhr-(0---5)-m", "x-band-(2.8---5.2)-cm"], "license": "other", "platform": "COSMO-SkyMed", "title": "COSMO-SkyMed ESA archive"}, "CryoSat.products": {"description": "CryoSat's primary payload is the SAR/Interferometric Radar Altimeter (SIRAL) (https://earth.esa.int/eogateway/instruments/siral) which has extended capabilities to meet the measurement requirements for ice-sheet elevation and sea-ice freeboard. CryoSat also carries three star trackers for measuring the orientation of the baseline. In addition, a radio receiver called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS) and a small laser retroreflector ensures that CryoSat's position will be accurately tracked. More detailed information on CryoSat instruments is available on the CryoSat mission page. The following CryoSat datasets are available and distributed to registered users: Level 1B and L2 Ice products: FDM, LRM, SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO EOLIS Gridded Products Detailed information concerning each of the above datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products) and in the news item: CryoSat Ocean Products now open to scientific community (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/cryosat/news/-/asset_publisher/47bD/content/cryosat-ocean-products-now-open-to-scientific-community). CryoSat Level 1B altimetric products contain time and geo-location information as well as SIRAL measurements in engineering units. Calibration corrections are included and have been applied to the window delay computations. In Offline products, geophysical corrections are computed from Analysis Auxiliary Data Files (ADFs), whereas in FDM products corrections are computed for Forecast ADFs. All corrections are included in the data products and therefore the range can be calculated by taking into account the surface type. The Offline Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after data acquisition and are principally dedicated to glaciologists working on sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean products, generated 2-3 hours after data acquisition, and fulfill the needs of some ocean operational services. Level 2 products contain the time of measurement, the geo-location and the height of the surface. IOP and GOP are outputs of the CryoSat Ocean Processor. These products are dedicated to the study of ocean surfaces, and provided specifically for the needs of the oceanographic community. IOP are generated 2-3 days after data sensing acquisition and use the DORIS Preliminary Orbit. GOP are typically generated 30 days after data sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections are computed from the Analysis ADFs, however following the oceanographic convention the corrections are available but not directly applied to the range (as for FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that are easily accessible to new communities of non-altimeter experts and end users. The Cryo-TEMPO datasets include dedicated products over five thematic areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water, together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing dedicated to each thematic area. Throughout the project, the products will be constantly evolved, and validated by a group of Thematic Users, thus ensuring optimal relevance and impact for the intended target communities. More information on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html). The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users.The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. This dataset will further the ability of the community to analyse and understand trends across the Greenland Ice Sheet margin, Antarctica and several mountain glaciers and ice caps around the world.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2010-04-08T00:00:00.000Z", null]]}}, "keywords": ["cryosat", "cryosat.products", "earth-science->-cryosphere->-snow/ice", "earth-science->-oceans", "earth-science->-terrestrial-hydrosphere->-snow/ice", "fdm", "gop", "grd", "inclined", "iop", "lrm", "non-sun-synchronous", "oceans", "sar", "sarin", "snow-and-ice"], "license": "other", "platform": "CryoSat", "title": "CryoSat products"}, "ENVISAT.ASA.APM_1P": {"description": "This ASAR Alternating Polarization Medium Resolution Image product has been generated from Level 0 data collected when the instrument was in Alternating Polarisation Mode. The product has lower geometric resolution but higher radiometric resolution than ASA_APP and contains one or two co-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). This product has been processed using the SPECAN algorithm and contains radiometric resolution good enough for ice applications and covers a continuous area along the imaging swath. The ASAR AP L0 full mission data archive has been bulk processed to Level 1 (ASA_APM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m ground range x 150 m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-11-15T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.apm-1p", "imaging-radars", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "snow-and-ice", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR AP Medium Resolution L1 [ASA_APM_1P]"}, "ENVISAT.ASA.APP_1P": {"description": "This ASAR Alternating Polarisation Mode Precision product is generated from Level 0 data collected when the instrument is in Alternating Polarisation Mode (7 possible swaths). The product contains two CO-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). This is a stand-alone multi-look, ground range, narrow swath digital image generated using the SPECAN algorithm and the most up to date auxiliary information available at the time of processing. Engineering corrections and relative calibration (antenna elevation gain, range spreading loss) are applied to compensate for well-understood sources of system variability. Generation of this product uses a technique to allow half the looks of an image to be acquired in horizontal polarisation and the other half in vertical polarisation and processed to 30-m resolution (with the exception of IS1). Absolute calibration parameters are available depending on external calibration activities and are provided in the product annotations. Spatial Resolution: 30 m ground range x 30 m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-11-15T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.app-1p", "imaging-radars", "medium-resolution---mr-(20---500)-m", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "snow-and-ice", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR AP Precision L1 [ASA_APP_1P]"}, "ENVISAT.ASA.APS_1P": {"description": "This product is a complex, slant-range, digital image generated from Level 0 data collected when the instrument is in Alternating Polarisation mode. (7 possible swaths). It contains two CO-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). In addition, the product uses the Range Doppler algorithm and the most up to date processing parameters available at the time of processing. It can be used to derive higher level products for SAR image quality assessment, calibration and interferometric applications, if allowed by the instrument acquisition. A minimum number of corrections and interpolations are performed on the data in order to allow the end-user maximum freedom to derive higher level products. Complex output data is retained to avoid loss of information. Absolute calibration parameters are available depending on external calibration activities and are provided in the product annotations. Spatial Resolution: approximately 8m slant range x approximately 4m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-11-15T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.aps-1p", "high-resolution---hr-(5---20-m)", "imaging-radars", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "snow-and-ice", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR AP Single-Look Complex L1 [ASA_APS_1P]"}, "ENVISAT.ASA.GM1_1P": {"description": "This product has been generated from Level 0 data collected when the instrument was in Global Monitoring Mode. One product covers a full orbit. The product includes slant range to ground range corrections. This strip-line product is the standard for ASAR Global Monitoring Mode. It is processed to approximately 1 km resolution using the SPECAN algorithm. The swath width is approximately 400 km. The ASAR GM L0 full mission data archive has been bulk processed to Level 1 (ASA_GM1_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 1 km ground range x 1 km azimuth.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2004-02-02T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "cryosphere", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "envisat", "envisat.asa.gm1-1p", "imaging-radars", "land-surface", "oceans", "sea-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR Global Monitoring L1 [ASA_GM1_1P]"}, "ENVISAT.ASA.IMM_1P": {"description": "This ASAR Medium Resolution strip-line product has been generated from Level 0 data collected when the instrument was in Image Mode. This product has lower resolution but higher radiometric resolution than the ASA_IMP. The product covers a continuous area along the imaging swath and features an ENL (radiometric resolution) good enough for ice applications. It is intended to perform applications-oriented analysis on large scale phenomena and multi-temporal imaging. This product provides a continuation of the ERS-SAR Image Mode data. The ASAR IM L0 full mission data archive has been bulk processed to Level 1 (ASA_IMM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m ground range x 150 m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-18T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "cryosphere", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.imm-1p", "imaging-radars", "land-surface", "oceans", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR IM Medium Resolution L1 [ASA_IMM_1P]"}, "ENVISAT.ASA.IMP_1P": {"description": "This is a multi-look, ground range, digital Precision Image generated from Level 0 data collected when the instrument was in Image Mode (7 possible swaths HH or VV polarisation). The product includes slant range to ground range correction. It is for users wishing to perform applications-oriented analysis and applies to multi-temporal imaging and to derive backscattering coefficients. The stand-alone image is generated using the Range/Doppler algorithm. The processing uses up to date (at time of processing) auxiliary parameters corrected for antenna elevation gain, and range spreading loss. Engineering corrections and relative calibration are applied to compensate for well-understood sources of system variability. Absolute calibration parameters, when available (depending on external calibration activities) are provided in the product annotations. This product provides a continuation of the ERS-SAR_PRI product. Spatial Resolution: 30 m ground range x 30 m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-18T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "cryosphere", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.imp-1p", "imaging-radars", "land-surface", "oceans", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR IM Precision L1 [ASA_IMP_1P]"}, "ENVISAT.ASA.IMS_1P": {"description": "This data product represents a single-look, complex, slant-range, digital image generated from Level 0 ASAR data collected when the instrument is in Image Mode. Seven possible swaths in HH or VV polarisation are available. The product is primarily intended for use in SAR quality assessment and calibration or applications requiring complex SAR images such as interferometry, and can be used to derive higher level products. The spatial coverage is about 100 km along track per 56- 100 km across track, and the radiometric resolution is 1 look in azimuth, 1 look in range. The file size is 741 Mbytes. It is worth highlighting that Azimuth pixel spacing depends on Earth-Satellite relative velocity and actual PRF and slant range pixel spacing is given by ASAR sampling frequency (19.208 Mhz). Auxiliary data include: Orbit state vector, Time correlation parameters, Main Processing parameters ADS, Doppler Centroid ADS, Chirp ADS, Antenna Elevation Pattern ADS, Geolocation Grid ADS, SQ ADS. Spatial Resolution: approximately 8m slant range x approximately 4m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-18T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "cryosphere", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.ims-1p", "imaging-radars", "land-surface", "oceans", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR IM Single Look Complex L1 [ASA_IMS_1P]"}, "ENVISAT.ASA.IM__0P": {"description": "The ASAR Image Mode source packets Level 0 data product offers Level 0 data for possible images processing on an other processing site. It includes some mandatory information for SAR processing. The Image Mode Level 0 product consists of time-ordered Annotated Instrument Source Packets (AISPs) collected by the instrument in Image Mode. The echo samples contained in the AISPs are compressed to 4 bits/sample using Flexible Block Adaptive Quantisation (FBAQ). This is a high-rate, narrow swath mode so data is only acquired for partial orbit segments and may be from one of seven possible image swaths. The Level 0 product is produced systematically for all data acquired within this mode. This product provides a continuation of the ERS-SAR_RAW product.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-18T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "envisat", "envisat.asa.im--0p", "imaging-radars", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR IM L0 [ASA_IM__0P]"}, "ENVISAT.ASA.WSM_1P": {"description": "This strip-line product has been generated from Level 0 data collected when the instrument was in Wide Swath Mode. The product includes slant range to ground range corrections and it covers a continuous area along the imaging swath. It is intended to perform applications-oriented analysis on large scale phenomena over a wide region and for multi-temporal imaging. This is the standard product for ASAR Wide Swath Mode. The ASAR WS L0 full mission data archive has been bulk processed to Level 1 (ASA_WSM_1P) in Envisat format with the IPF-ASAR processor version 6.03. Spatial Resolution: 150 m slant range x 150 m azimuth.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-28T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "cryosphere", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat.asa.wsm-1p", "imaging-radars", "land-surface", "oceans", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR WS Medium Resolution L1 [ASA_WSM_1P]"}, "ENVISAT.ASA.WSS_1P": {"description": "The Level-1B data product offered by ESA from the ASAR Wide-Swath Mode (WS) is the multi-look detected product (ASA_WSM_1P), intended to support applications that exploit intensity data. In order to support the development of new applications with the ASAR ScanSAR data, a WSM product providing phase information has been developed and implemented in the ESA ASAR processor, the Wide-Swath Single-Look complex product (ASA_WSS_1P). This product is mainly used for INSAR applications based either on wide-swath/wide-swath pairs or wide-swath/image mode pairs, applications of ocean current mapping, large-area ocean wave retrievals, and atmospheric water vapour characterisation. It shall be mentioned that the standard ESA WSS product is based on the prototype WSS processor developed by Polimi/Poliba, which has also been used to generate prototype products for testing the potential and preparing the exploitation of the WSS product. The ESA ASA_WSS_1P product is available as a standard Envisat ASAR product. The ASA_WSS_1P product format is slightly different from other ASAR products since: - there are 5 different MDSs, one per sub-swath - a &quot;Doppler Grid&quot; ADS has been included to support ocean current mapping applications - there are 5 records in the MPP ADS, one per sub-swath - there are 5 records in the SQ ADS, one per sub-swath Other key characteristics of the ASA_WSS_1P product are summarised below: - processing is fully phase preserving - data in the MDSs is sampled in a common grid both in range and in azimuth - standard product is 60 sec long with 80 m az. pixel spacing - auxiliary timeline information has been added in the Main Processing Parameters ADS - elevation antenna pattern correction is applied by default (although the product is a single-look complex) - Spatial Resolution: approximately 8 m slant range x 80 m azimuth", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-28T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "earth-science->-oceans", "earth-science->-oceans->-sea-surface-topography", "envisat", "envisat.asa.wss-1p", "imaging-radars", "oceans", "sea-surface-topography", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR WS Single Look Complex L1 [ASA_WSS_1P]"}, "ENVISAT.ASA.WS__0P": {"description": "The WS Mode Level 0 product consists of time-ordered AISPs collected while the instrument was is in Wide Swath Mode. The echo samples in the AISPs have been compressed to 4 bits/sample using FBAQ. This is a high-rate, wide swath (ScanSAR) mode so data is only acquired for partial orbit segments and is composed of data from five image swaths (SS1 to SS5). The Level 0 product is produced systematically for all data acquired within this mode. The objective of this product is to offer Level 0 data for possible images processing on another processing site. It includes mandatory information for SAR processing. Data Size: 400 km across track x 400 km along track", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-28T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "envisat", "envisat.asa.ws--0p", "imaging-radars", "natural-hazards-and-disaster-risk", "ocean-waves", "oceans", "soils", "sun-synchronous", "vegetation"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR WS L0 [ASA_WS__0P]"}, "ENVISAT.ASA.WVI_1P": {"description": "This is the basic Level 1B ASAR Wave Mode product, including up to 400 single-look, complex, slant range, imagettes generated from Level 0 data, and up to 400 imagette power spectra computed using the cross-spectra methodology.\r\n\r\nThe auxiliary parameters used are the most up to date at the time of processing. A minimum number of corrections and interpolations are performed in order to allow the end-user maximum freedom to derive higher level products. Complex output data is retained to avoid loss of information. Absolute calibration parameters, when available (depending on external calibration activities), are provided in the product annotations.\r\n\r\nImagette power spectrum is equivalent to the ERS UWA (Near Real Time) product with revisited algorithm (cross-spectra) taking into account the higher quality of the SLC imagette. Note that starting from an SLC imagette, the generation of an ERS UWA-type product might be ensured by a simple look detection and summation.\r\n\r\nThis product provides a continuation of the ERS SAR Wave Mode data.\r\n\r\nThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of processing and are available in Envisat format.\r\n\r\nImagette Spatial Resolution: 20 m ground range x 20 m azimuth.\r\n\r\nCross Spectra Output: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-10-30T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "envisat", "envisat.asa.wvi-1p", "imaging-radars", "ocean-waves", "oceans", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR Wave SLC and Cross-Spectra Imagettes L1 [ASA_WVI_1P]"}, "ENVISAT.ASA.WVS_1P": {"description": "This ASAR Wave Mode product is extracted from the combined SLC and Cross Spectra product,(_$$ASA_WVI_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-slc-and-cross-spectra-imagettes-l1-asa_wvi_1p-), which is generated from data collected when the instrument was in Wave Mode using the Cross Spectra methodology.\r\n\r\nThe product is meant for Meteo users. The spatial coverage is up to 20 spectra acquired every 100 km, with a minimum coverage of 5km x 5km.\r\n\r\nThe file size has a maximum of 0.2 Mbytes. Auxiliary data include Orbit state vector, Time correlation parameters, Wave Processing parameters ADS, Wave Geolocation ADS, and SQ ADS.\r\n\r\n\r\nThe product provides a continuation of the ERS -SAR Wave Mode data. \r\n\r\nThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of .processing and are available in Envisat format.\r\n\r\nOutput: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-12-10T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-chemistry", "earth-science->-oceans->-ocean-temperature", "earth-science->-oceans->-ocean-waves", "earth-science->-oceans->-sea-surface-topography", "envisat", "envisat.asa.wvs-1p", "imaging-radars", "ocean-chemistry", "ocean-temperature", "ocean-waves", "oceans", "sea-surface-topography", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR Wave Cross Spectra Imagette L1 [ASA_WVS_1P]"}, "ENVISAT.ASA.WVW_2P": {"description": "This ASAR Wave Mode product is created by inverting the cross-spectra which is computed from inter-look processing of the SLC wave imagettes in order to derive the directional ocean product ocean wave spectra. \r\nAuxiliary ADSs included with the product remains the same as for the ASAR Wave Mode Cross-Spectra product (_$$ASA_WVS_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-imagette-cross-spectra-l1-asa_wvs_p- ).\r\n\r\nThe output follows the format of the Envisat ASAR Level 1B Wave Mode Cross-Spectra Imagette (_$$ASA_WVS_1P$$ https://earth.esa.int/eogateway/catalog/envisat-asar-wave-imagette-cross-spectra-l1-asa_wvs_p- ) product. This is done in order to be compatible with the ground segment products of Envisat ASAR.\r\n \r\nThis product provides a continuation of the ERS SAR Wave Mode data.\r\n\r\nThe ASAR Wave products were processed operationally using the version of PF-ASAR available at the time of processing and are available in Envisat format.\r\n\r\n\r\nOutput: Wavelength range from 20 to 1000 m in 24 logarithmic steps.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-12-10T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["ASAR"], "keywords": ["5---1150-km", "800-km", "asar", "coastal-processes", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-ocean-waves", "envisat", "envisat.asa.wvw-2p", "imaging-radars", "ocean-waves", "oceans", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat ASAR WM Ocean Wave Spectra L2 [ASA_WVW_2P]"}, "ENVISAT.MIP.NL__1P": {"description": "This MIPAS Level 1 data product covers the geo-located, spectrally and radiometrically calibrated limb emission spectra in the 685-2410 cm-1 wave number range. It comprises 5 bands: 685-980 cm-1, 1010-1180 cm-1, 1205-1510 cm-1, 1560-1760 cm-1, 1810-2410 cm-1 and covers the following spatial ranges:  -Tangent height range: 5 to 170 km -Pointing range: (azimuth pointing range relative to satellite velocity vector): 160 deg - 195 deg (rearward anti-flight direction); 80 deg - 110 deg (sideward anti-Sun direction)  The instantaneous field of view (IFOV) is 0.05230 (elevation) x 0.5230 (azimuth) deg. The length of measurement cell for an individual height step is approximately 300-500 km (dependent on tangent height and optical properties of the atmosphere). The spectral resolution spans from 0.030 to 0.035 cm-1, with a radiometric sensitivity of 4.2 to 50 nW / cm-1 / sr / cm2.  The resolution range of the dataset is: 3 km (vertical) x 30 km (horizontal) at the tangent point.  Please consult the Product Quality Readme - https://earth.esa.int/documents/700255/3711375/Read_Me_File_MIP_NL__1PY_ESA-EOPG-EBA-TN-1+issue1.1.pdf - file for MIPAS Level 1b IPF 8.03 before using the data.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-07-01T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MIPAS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-chemistry", "atmospheric-radiation", "atmospheric-temperature", "climate", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-chemistry", "earth-science->-atmosphere->-atmospheric-radiation", "earth-science->-atmosphere->-atmospheric-temperature", "earth-science->-climate-indicators", "envisat", "envisat.mip.nl--1p", "interferometers", "mipas", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat MIPAS L1 - Geo-located and calibrated atmospheric spectra [MIP_NL__1P]"}, "ENVISAT.MIP.NL__2P": {"description": "This MIPAS Level 2 data product describes localised vertical profiles of pressure, temperature and 21 target species (H2O, O3, HNO3, CH4, N2O, NO2, CFC-11, ClONO2, N2O5, CFC-12, COF2, CCL4, HCN, CFC-14, HCFC-22, C2H2, C2H6, COCl2, CH3Cl, OCS and HDO).\r\nIt has a global coverage of Earth&apos;s stratosphere and mesosphere at all latitudes and longitudes. The vertical resolution of p, T and VMR profiles varies from 3 to 4 km, whereas the horizontal resolution is approximately 300 km to 500 km along track. This depends on the tangent height range and optical properties of the atmosphere. Auxiliary data include spectroscopic data, microwindows data, validation data, initial guess p, T and trace gas VMR profiles.\r\nThe resolution range of the dataset is: 3 km (vertical) x 30 km (horizontal) at the tangent point.\r\nThe latest reprocessed MIPAS Level 2 data (v8.22) is available as \r\n1)\tStandard products (MIPAS_2PS): \r\nA complete product containing 22 MIPAS L2 chemical species covering a single orbit and single species providing information generally needed by data users.\r\n\r\n2)\tExtended products (MIPAS_2PE): \r\nA complete product containing 22 MIPAS L2 chemical species covering a single orbit and single species intended for diagnostics and expert users who need complete information about the retrieval process.\r\nBoth products are available in NetCDF format\r\nPlease refer to the MIPAS L2 v8.22 _$$Product Quality Readme file$$ https://earth.esa.int/eogateway/documents/20142/37627/README_V8_issue_1.0_20201221.pdf for further details.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-07-01T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MIPAS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-chemistry", "atmospheric-radiation", "atmospheric-temperature", "climate", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-chemistry", "earth-science->-atmosphere->-atmospheric-radiation", "earth-science->-atmosphere->-atmospheric-temperature", "earth-science->-climate-indicators", "envisat", "envisat.mip.nl--2p", "interferometers", "mipas", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat MIPAS L2 - Temperature, pressure and atmospheric constituents profiles [MIPAS_2PS/2PE]"}, "ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40": {"description": "The ERS-1/2 ATSR Level 1B Brightness Temperature/Radiance products (RBT) contain top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, when available, on a 1-km pixel grid. The visible channels are only available for the ATSR-2 instrument.\r\nValues for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information.\r\nThe ATSR-1 and ATSR-2 products [ER1_AT_1_RBT and ER2_AT_1_RBT], in NetCDF format stemming from the 4th ATSR reprocessing, are precursors of Envisat AATSR and Sentinel-3 SLSTR data. They have replaced the former L1B products [AT1_TOA_1P and AT2_TOA_1P] in Envisat format from the 3rd reprocessing. \r\nUsers with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products, and consult the ERS _$$ATSR Product Notice Readme document$$ https://earth.esa.int/eogateway/documents/20142/37627/ATSR-Level-1B-ERn-AT-1-RBT-Product-Notices-Readme.pdf  \r\nThe processing updates that have been put in place and the expected scientific improvements for the ERS ATSR 4th reprocessing data have been outlined in full in the _$$User Documentation for (A)ATSR 4th Reprocessing Products$$ https://earth.esa.int/documents/20142/37627/QA4EO-VEG-OQC-MEM-4538_User_Documentation_for__A_ATSR_4th_Reprocessing_Level_1.pdf .", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-01T00:00:00.000Z", "2003-06-22T23:59:59.999Z"]]}}, "instruments": ["ATSR-1", "ATSR-2"], "keywords": ["500-km", "800-km", "atmosphere", "atmospheric-temperature", "atsr-1", "atsr-2", "earth-science->-agriculture->-forest-science->-forest-fire-science", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-temperature", "earth-science->-oceans", "ers-1", "ers-2", "ersatsrl1bbrightnesstemperatureradianceer1at1rbter2at1rbt40", "forest-fires", "imaging-spectrometers/radiometers", "low-resolution---lr-(500---1200)-m", "mwir-(3.0---6.0)-\u00b5m", "nir-(0.75---1.30)-\u00b5m", "oceans", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS ATSR L1B Brightness Temperature/Radiance [ER1_AT_1_RBT / ER2_AT_1_RBT]"}, "ERS_SAR_Envisat_ASAR_Alaska_mountains_L1_IM_AP": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR IMP and Envisat ASAR IMP/APP Precision Level 1 products covering Alaska Mountains.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-08-08T00:00:00.000Z", "2012-04-07T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-app-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-imp-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-imp-1p", "ers-sar-envisat-asar-alaska-mountains-l1-im-ap", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR and Envisat ASAR Alaska mountains - L1 (IM/AP)"}, "ERS_SAR_Envisat_ASAR_Alps_mountains_L1_IM_AP": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR IMP and Envisat ASAR IMP/APP Precision Level 1 products covering the European Alps.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-07-30T00:00:00.000Z", "2012-04-03T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-app-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-imp-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-imp-1p", "ers-sar-envisat-asar-alps-mountains-l1-im-ap", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sea-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Alps mountains - L1 (IM/AP)"}, "ERS_SAR_Envisat_ASAR_Austria_coverage_L1_IM": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR and Envisat ASAR Image Mode Single Look Complex (IMS) and Precision (IMP) Level 1 products covering Austria generated by EODC.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-07-31T00:00:00.000Z", "2012-04-03T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-imp-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "envisat", "envisat-5---1150-km", "envisat:-800-km", "envisat:-asa-ims-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-ims-1p", "ers-sar-envisat-asar-austria-coverage-l1-im", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sar-imp-1p", "sea-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Austria coverage - L1 (IM)"}, "ERS_SAR_Envisat_ASAR_Iceland_mountains_L1_IM_AP": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR IMP and Envisat ASAR IMP/APP Precision Level 1 products covering the Iceland mountains.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-07-30T00:00:00.000Z", "2012-04-07T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-app-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-imp-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-imp-1p", "ers-sar-envisat-asar-iceland-mountains-l1-im-ap", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sea-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Iceland mountains - L1 (IM/AP)"}, "ERS_SAR_Envisat_ASAR_Italy_volcanoes_L1_IM": {"description": "Thematic collection: ESAR OTF bulk processed ERS-1/2 SAR and Envisat ASAR Image Mode Single Look Complex (IMS) Level 1 products covering Italian volcanoes.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1992-05-01T00:00:00.000Z", "2011-02-19T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-ims-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-ims-1p", "ers-sar-envisat-asar-italy-volcanoes-l1-im", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.05", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Italy volcanoes - L1 (IM)"}, "ERS_SAR_Envisat_ASAR_Pyrenees_mountains_L1_IM_AP": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR IMP and Envisat ASAR IMP/APP Precision Level 1 products covering the Pyrenees mountains", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-08-04T00:00:00.000Z", "2012-04-07T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-app-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-imp-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-imp-1p", "ers-sar-envisat-asar-pyrenees-mountains-l1-im-ap", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Pyrenees mountains - L1 (IM/AP)"}, "ERS_SAR_Envisat_ASAR_Scandinavia_mountains_L1_IM_AP": {"description": "Thematic collection: Bulk processed ERS-1/2 SAR IMP and Envisat ASAR IMP/APP Precision Level 1 products covering the Scandinavia mountains", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-07-30T00:00:00.000Z", "2012-04-03T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR", "ASAR"], "keywords": ["agriculture", "ami/sar", "asa-app-1p", "asar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "envisat-5-\u2013-1150-km", "envisat:-800-km", "envisat:-asa-imp-1p", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-imp-1p", "ers-sar-envisat-asar-scandinavia-mountains-l1-im-ap", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-asar-v6.03", "pf-ers-v6.0*", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "ERS SAR & Envisat ASAR Scandinavia mountains - L1 (IM/AP)"}, "ERS_SAR_Svalbard_coverage_L1_IM": {"description": "Thematic collection: ESAR OTF bulk processed ERS-1/2 SAR Image Mode Single Look Complex (IMS) Level 1 products covering the Svalbard area generated for ESA CCI Glacier project.", "extent": {"spatial": {"bbox": [[180, 90, 180, 90]]}, "temporal": {"interval": [["1991-07-30T00:00:00.000Z", "2011-07-03T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR"], "keywords": ["agriculture", "ami/sar", "biosphere", "coastal-processes", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "ers-1", "ers-2", "ers-2:-5-km", "ers-2:-782-to-785-km", "ers-2:-sar-ims-1p", "ers-sar-svalbard-coverage-l1-im", "human-dimensions", "imaging-radars", "land-surface", "natural-hazards-and-disaster-risk", "oceans", "pf-ers-v6.05", "sea-ice", "snow-and-ice", "soils", "sun-synchronous", "terrestrial-hydrosphere", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS SAR Svalbard coverage - L1 (IM)"}, "ESA_Orthorectified_Map_oriented_Level1_products": {"description": "The ESA Orthorectified Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\n\r\nThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. \r\nThe collection consists of data products of the following type: \r\n\r\nMES_GEC_1P: Geocoded Ellipsoid GCP Corrected Level 1 MOS-1/1B MESSR products which are the default products generated by the MOS MESSR processor in all cases (where possible), with usage of the latest set of LANDSAT improved GCP (Ground Control Points). These are orthorectified map-oriented products, corresponding to the old MOS-1/1B MES_ORT_1P products with geolocation improvements. \r\n \r\n\r\nMESSR Instrument Characteristics\r\nBand\tWavelength Range (nm)\tSpatial Resolution (m)\tSwath Width (km)\r\n1 (VIS)\t510 \u2013 690\t50\t100\r\n2 (VIS)\t610 \u2013 690\t50\t100\r\n3 (NIR)\t720 \u2013 800\t50\t100\r\n4 (NIR)\t800 \u2013 1100\t50\t100", "extent": {"spatial": {"bbox": [[-120, 19, 95, 87]]}, "temporal": {"interval": [["1987-09-08T00:00:00.000Z", "1993-08-20T23:59:59.999Z"]]}}, "instruments": ["MESSR", "MESSR"], "keywords": ["100-km", "909-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-oceans->-sea-surface-topography", "esa-orthorectified-map-oriented-level1-products", "imaging-spectrometers/radiometers", "medium-resolution---mr-(20---500)-m", "mes-gec-1p", "messr", "mos-1", "mos-1b", "nir-(0.75---1.30)-\u00b5m", "oceans", "sea-ice", "sea-surface-topography", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "MOS-1,MOS-1B", "title": "MOS-1/1B ESA Orthorectified Map-oriented Products [MES_GEC_1P]"}, "ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product": {"description": "The ESA System Corrected (Level 1) MOS-1/1B VTIR Products collection is composed of MOS-1/1B VTIR (Visible and Thermal Infrared Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\n\r\nThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the MOS Product Format Specification for further details.\r\nThe collection consists of data products of the following type:\r\n\r\nVTI_SYC_1P: System corrected Level 1 MOS-1/1B VTIR products in EO-SIP format. \r\n\r\nBand\tWavelength Range (\u00b5m)\tSpatial Resolution (km)\tSwath Width (km)\r\n1 (VIS)\t0.5 \u2013 0.7\t 0.9\t 1500\r\n2 (TIR)\t6.0 \u2013 7.0\t 2.7\t 1500\r\n3 (TIR)\t10.5 \u2013 11.5\t 2.7\t 1500\r\n4 (TIR)\t11.5 \u2013 12.5\t 2.7\t 1500", "extent": {"spatial": {"bbox": [[-120, 19, 95, 87]]}, "temporal": {"interval": [["1987-09-08T00:00:00.000Z", "1993-09-30T23:59:59.999Z"]]}}, "instruments": ["VTIR", "VTIR"], "keywords": ["100-km", "909-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-oceans->-sea-surface-topography", "esa-system-corrected-level-1-mos-1-1b-vtir-product", "imaging-spectrometers/radiometers", "medium-resolution---mr-(20---500)-m", "mos-1", "mos-1b", "nir-(0.75---1.30)-\u00b5m", "oceans", "sea-ice", "sea-surface-topography", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m", "vti-syc-1p", "vtir"], "license": "other", "platform": "MOS-1,MOS-1B", "title": "MOS-1/1B ESA System Corrected VTIR Products [VTI_SYC_1P]"}, "ESA_System_corrected_map_oriented_Level_1_products": {"description": "The ESA System Corrected Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02.\r\n\r\nThe products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. \r\nThe collection consists of data products of the following type:\r\n\r\nMES_GES_1P: Geocoded Ellipsoid System Corrected Level 1 MOS-1/1B MESSR products as generated by the MOS MESSR processor where the generation of MES_GEC_1P products is not possible. These replace the old MES_SYC_1P products. \r\n\r\nMESSR Instrument Characteristics\r\nBand\tWavelength Range (nm)\tSpatial Resolution (m)\tSwath Width (km)\r\n1 (VIS)\t510 \u2013 690\t50\t100\r\n2 (VIS)\t610 \u2013 690\t50\t100\r\n3 (NIR)\t720 \u2013 800\t50\t100\r\n4 (NIR)\t800 \u2013 1100\t50\t100", "extent": {"spatial": {"bbox": [[-120, 19, 95, 87]]}, "temporal": {"interval": [["1987-09-08T00:00:00.000Z", "1993-08-20T23:59:59.999Z"]]}}, "instruments": ["MESSR", "MESSR"], "keywords": ["100-km", "909-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "earth-science->-oceans->-sea-surface-topography", "esa-system-corrected-map-oriented-level-1-products", "imaging-spectrometers/radiometers", "medium-resolution---mr-(20---500)-m", "mes-ges-1p", "messr", "mos-1", "mos-1b", "nir-(0.75---1.30)-\u00b5m", "oceans", "sea-ice", "sea-surface-topography", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "MOS-1,MOS-1B", "title": "MOS-1/1B ESA System Corrected Map-oriented Products [MES_GES_1P]"}, "EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT": {"description": "The Envisat AATSR Level 1B Brightness Temperature/Radiance product (RBT) contains top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, on a 1-km pixel grid. Values for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information. This AATSR product [ENV_AT_1_RBT] in NetCDF format stemming from the 4th AATSR reprocessing, is a continuation of ERS ATSR data and a precursor of Sentinel-3 SLSTR data. It has replaced the former L1B product [ATS_TOA_1P] in Envisat format from the 3rd reprocessing. Users with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products.  The 4th reprocessing of ENVISAT AATSR data was completed in 2022; the processing updates that have been put in place and the expected scientific improvements have been outlined in full in the _$$User Documentation for (A)ATSR 4th Reprocessing Products$$ https://earth.esa.int/documents/20142/37627/QA4EO-VEG-OQC-MEM-4538_User_Documentation_for__A_ATSR_4th_Reprocessing_Level_1.pdf .", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-05-20T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["AATSR"], "keywords": ["500-km", "800-km", "aatsr", "earth-science->-oceans", "earth-science->-oceans->-ocean-temperature", "earth-science->-oceans->-sea-surface-topography", "envisat", "envisataatsrl1bbrightnesstemperatureradianceat1rbt", "imaging-spectrometers/radiometers", "low-resolution---lr-(500---1200)-m", "mwir-(3.0---6.0)-\u00b5m", "nir-(0.75---1.30)-\u00b5m", "ocean-temperature", "oceans", "sea-surface-topography", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Envisat", "title": "Envisat AATSR L1B Brightness Temperature/Radiance [ENV_AT_1_RBT]"}, "FDRforAltimetry": {"description": "This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ .\r\nThe Fundamental Data Record for Altimetry V1 products contain Level 0 and Level 1 altimeter-related parameters including calibrated radar waveforms and supplementary instrumental parameters describing the altimeter operating status and configuration through the satellite lifetime.\r\n\r\nThe data record consists of data for the ERS-1, ERS-2 and Envisat missions for the period ranging from 1991 to 2012, and bases on the Level 1 data obtained from previous ERS REAPER and ENVISAT V3.0 reprocessing efforts incorporating new algorithms, flags, and corrections to enhance the accuracy and reliability of the data.\r\n\r\nFor many aspects, the Altimetry FDR product has improved compared to the existing individual mission datasets:\r\n\r\nNew neural-network waveform classification, surface type classification, distance to shoreline and surface flag based on GSHHG\r\nInstrumental calibration information directly available in the product\r\nImproved Orbit solutions\r\nCorrection of REAPER drawbacks (i.e., time jumps and negative waveforms)\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "atmosphere", "bathymetry-and-seafloor-topography", "cryosphere", "earth-science->-atmosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-snow/ice", "earth-science->-oceans", "earth-science->-oceans->-bathymetry/seafloor-topography", "earth-science->-oceans->-marine-geophysics", "earth-science->-oceans->-ocean-circulation", "earth-science->-oceans->-ocean-waves", "earth-science->-oceans->-sea-surface-topography", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "ers-1", "ers-2", "fdrforaltimetry", "imaging-spectrometers/radiometers", "marine-geophysics", "mwr", "ocean-circulation", "ocean-waves", "oceans", "ra", "ra-2", "radar-altimeters", "sea-surface-topography", "snow-and-ice", "sun-synchronous"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Fundamental Data Records for Altimetry [ALT_FDR___]"}, "FDRforAtmosphericCompositionATMOSL1B": {"description": "The Fundamental Data Record (FDR) for Atmospheric Composition UVN Level 1b v.1.0 dataset is a cross-instrument Level-1 product [ATMOS__L1B] generated in 2023 and resulting from the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ .\r\nThe FDR contains selected Earth Observation Level 1b parameters (irradiance/reflectance) from the nadir-looking measurements of the ERS-2 GOME and Envisat SCIAMACHY missions for the period ranging from 1995 to 2012. \r\nThe data record offers harmonised cross-calibrated spectra, essential for subsequent trace gas retrieval. The focus lies on spectral windows in the Ultraviolet-Visible-Near Infrared regions the retrieval of critical atmospheric constituents like ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) column densities, alongside cloud parameters in the NIR spectrum.\r\nFor many aspects, the FDR product has improved compared to the existing individual mission datasets:\r\n\u2022\tGOME solar irradiances are harmonised using a validated SCIAMACHY solar reference spectrum, solving the problem of the fast-changing etalon present in the original GOME Level 1b data; \r\n\u2022\tReflectances for both GOME and SCIAMACHY are provided in the FDR product. GOME reflectances are harmonised to degradation-corrected SCIAMACHY values, using collocated data from the CEOS PIC sites;\r\n\u2022\tSCIAMACHY data are scaled to the lowest integration time within the spectral band using high-frequency PMD measurements from the same wavelength range. This simplifies the use of the SCIAMACHY spectra which were split in a complex cluster structure (with own integration time) in the original Level 1b data;\r\n\u2022\tThe harmonization process applied mitigates the viewing angle dependency observed in the UV spectral region for GOME data;\r\n\u2022\tUncertainties are provided.\r\n\r\n\r\nEach FDR product covers three FDRs (irradiance/reflectance for UV-VIS-NIR) for a single day within the same product including information from the individual ERS-2 GOME and Envisat SCIAMACHY orbits therein.\r\n\r\nFDR has been generated in two formats: Level 1A and Level 1B targeting expert users and nominal applications respectively. The Level 1A [ATMOS__L1A] data include additional parameters such as harmonisation factors, PMD, and polarisation data extracted from the original mission Level 1 products. The ATMOS__L1A dataset is not part of the nominal dissemination to users. In case of specific requirements, please contact _$$EOHelp$$ http://esatellus.service-now.com/csp?id=esa_simple_request&sys_id=f27b38f9dbdffe40e3cedb11ce961958 .\r\n\r\n\r\nThe FDR4ATMOS products should be regarded as experimental due to the innovative approach and the current use of a limited-sized test dataset to investigate the impact of harmonization on the Level 2 target species, specifically SO2, O3 and NO2. Presently, this analysis is being carried out within follow-on activities.\r\n\r\nOne of the main aspects of the project was the characterization of Level 1 uncertainties for both instruments, based on metrological best practices. The following documents are provided:\r\n\r\n1.\tGeneral guidance on a metrological approach to Fundamental Data Records (FDR) -> link TBC\r\n2.\tUncertainty Characterisation document -> link TBC\r\n3.\tEffect tables  -> link TBC\r\n4.\tNetCDF files containing example uncertainty propagation analysis and spectral error correlation matrices for SCIAMACHY (Atlantic and Mauretania scene for 2003 and 2010) and GOME (Atlantic scene for 2003) links TBC  reflectance_uncertainty_example_FDR4ATMOS_GOME.nc\r\nreflectance_uncertainty_example_FDR4ATMOS_SCIA.nc\r\n\r\nThe FDR V1 is currently being extended to include the MetOp GOME-2 series.\r\n\r\nAll the new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \r\n\r\nPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1995-06-28T00:00:00.000Z", "2012-04-07T23:59:59.999Z"]]}}, "instruments": ["SCIAMACHY", "GOME"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-chemistry", "atmospheric-radiation", "atmospheric-temperature", "climate", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-chemistry", "earth-science->-atmosphere->-atmospheric-radiation", "earth-science->-atmosphere->-atmospheric-temperature", "earth-science->-climate-indicators", "envisat", "ers-2", "fdrforatmosphericcompositionatmosl1b", "gome", "sciamachy", "spectrometers", "sun-synchronous"], "license": "other", "platform": "Envisat,ERS-2", "title": "Fundamental Data Record for Atmospheric Composition [ATMOS__L1B]"}, "FDRforRadiometry": {"description": "This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ .\r\nThe Fundamental Data Record for Radiometry V1 products contain intercalibrated Top of the Atmosphere brightness temperatures at 23.8 and 36.5 GHz. The collection covers data for the ERS-1, ERS-2 and Envisat missions, and is built upon a new processing of Level 0 data, incorporating numerous improvements in terms of algorithms, flagging procedures, and corrections.\r\n\r\nCompared to existing datasets, the Radiometry FDR demonstrates notable improvements in several aspects:\r\n\r\nNew solutions for instrumental effects (ERS Reflector loss, Skyhorn, and Sidelobe corrections)\r\nNative sampling rate of 7Hz with enhanced coverage\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "agriculture", "atmosphere", "atmospheric-water-vapour", "cryosphere", "earth-science->-agriculture", "earth-science->-agriculture->-soils", "earth-science->-agriculture->-soils->-soil-moisture/water-content", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-water-vapor", "earth-science->-cryosphere", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface->-soils", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "ers-1", "ers-2", "fdrforradiometry", "imaging-spectrometers/radiometers", "mwr", "ra", "ra-2", "radar-altimeters", "snow-and-ice", "soil-moisture", "soils", "sun-synchronous"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Fundamental Data Records for Radiometry [MWR_FDR___]"}, "GEOSAT-1.and.2.ESA.archive": {"description": "GEOSAT 1 and 2 collection is composed of products acquired by the GEOSAT 1 and GEOSAT 2 Spanish satellites. The collection regularly grows as ESA collects new products.\r\nGEOSAT-1 standard products offered are:\r\n\u2022 SL6_22P: SLIM6, 22m spatial resolution, from bank P\r\n\u2022 SL6_22S: SLIM6, 22m spatial resolution, from bank S\r\n\u2022 SL6_22T: SLIM6, 22m spatial resolution, 2 banks merged together\r\n\r\nGEOSAT-1 products are available in two different processing levels:\r\n\u2022 Level 1R: All 3 Spectral channels combined into a band-registered image using L0R data. Geopositioned product based on rigorous sensor model. Coefficients derived from internal and external satellite orientation parameters coming from telemetry and appended to metadata.\r\n\u2022 Level 1T: data Orthorectified to sub-pixel accuracy (10 meters RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m). The use of the GCPs, it is not automatic, as it is done manually, which gives greater precision. (GCPs by human operators).\r\n\r\nGEOSAT-2 standard products offered are:\r\n\u2022 Pan-sharpened (HRA_PSH four-band image, HRA_PS3 321 Natural Colours, HRA_PS4 432 False Colours): a four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserve all spectral features of the multispectral bands, so it should not be used for radiometric purposes.\r\n\u2022 Panchromatic (HRA_PAN): a single-band image coming from the panchromatic sensor.HRA_MS4: Multispectral (HRA_MS4): a four-band image coming for the multispectral sensor, with band co-registration.\r\n\u2022 Bundle (HRA_PM4): a five-band image contains the panchromatic and multispectral products packaged together, with band co-registration.\r\n\u2022 Stereo Pair (HRA_STP): The image products obtained from two acquisitions of the same target performed from different viewpoints in the same pass by using the agility feature of the platform. It can be provided as a pair of pan sharpened or panchromatic images.\r\n\r\nGEOSAT-2 products are available in two different processing levels:\r\n\u2022 Level 1B: A calibrated and radiometrically corrected product, but not resampled. The product includes the Rational Polynomial Coefficients (RPC), the metadata with gain and bias values for each band, needed to convert the digital numbers into radiances at pixel level, and information about geographic projection (EPGS), corners geolocation, etc.\r\n\u2022 Level 1C: A calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid. The geometric information is contained in the GeoTIFF tags.\r\nSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/GEOSAT/ available on the Third Party Missions Dissemination Service.\r\n\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-08-01T00:00:00.000Z", null]]}}, "instruments": ["SLIM6", "HiRAIS"], "keywords": ["12-km-(geosat-2)", "620-km-(geosat-2)", "625-km-(geosat-1)", "663-km-(geosat-1)", "agriculture", "cameras", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "forestry", "geosat-1", "geosat-1.and.2.esa.archive", "geosat-2", "hirais", "hra-ms4-1b", "hra-ms4-1c", "hra-pan-1b", "hra-pan-1c", "hra-pm4-1b", "hra-pm4-1c", "hra-ps3-1b", "hra-ps3-1c", "hra-ps4-1b", "hra-ps4-1c", "hra-psh-1b", "hra-psh-1c", "hra-stp-1b", "hra-stp-1c", "imaging-spectrometers/radiometers", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sl6-22p-1r", "sl6-22p-1t", "sl6-22p-2t", "sl6-22s-1r", "sl6-22s-1t", "sl6-22s-2t", "sl6-22t-1r", "sl6-22t-1t", "sl6-22t-2t", "slim6", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "GEOSAT-1,GEOSAT-2", "title": "GEOSAT-1 and 2 ESA archive"}, "GEOSAT-2.Portugal.Coverage": {"description": "Description\r\nGEOSAT-2 Portugal coverage is a collection of a 2021`s data over the Portugal area, including islands. The available dataset hasve a cloud cover less thaen 10%, and is acquired up to 1m resolution with Geometric accuracy <6m CE90 based on Copernicus DEM @10m. in tThe following acquisition modesproduct types are available:\r\n\u2022\tPan-sharpened (4 bands, 321 Natural Colours or 432 False Colours): A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not prereserves all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Resolution 1m; Bands: All, R-G-B or Ni-R-G\r\n\u2022\tBundle: Panchromatic (1m resolution) + Multispectral bands (4m resolution): five-band image containing the panchromatic and multispectral products packaged together, with band co-registration.\r\n\r\nThe available processing level is L1C orthorectified: a calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid.\r\n\r\nProduct Type HRA_PM4_1C , HRA_PSH_1C Processing Level and Spatial Resolution\r\n\tL1B (native)\tL1C (ortho)\r\nPan-sharpened\t1.0m\t1.0m\r\nBundle (PAN+MS)\t1.0m (P), 4.0m (MS)\t1.0m (P), 4.0m(MS)\r\n \r\nDetails", "extent": {"spatial": {"bbox": [[-39, 28, -2, 48]]}, "temporal": {"interval": [["2021-01-08T00:00:00.000Z", "2021-12-11T23:59:59.999Z"]]}}, "instruments": ["HiRAIS"], "keywords": ["12-km-at-nadir", "620-km", "earth-science->-agriculture->-agricultural-plant-science->-weeds", "earth-science->-land-surface", "geosat-2", "geosat-2.portugal.coverage", "hirais", "imaging-spectrometers/radiometers", "invasive-species", "land-surface", "nir-(0.75---1.30)-\u00b5m", "noxious-plants-or-invasive-plants", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "GEOSAT-2", "title": "GEOSAT-2 Portugal Coverage 2021"}, "GEOSAT2SpainCoverage10": {"description": "The GEOSAT-2 Spain Coverage collection consists of three separate coverages of Spain, including the Balearic and Canary islands, acquired by GEOSAT-2 between March and November of 2021, 2022, and 2023, respectively.\r\n \r\nSpatial coverage of the 2021 collection.\r\nThe following product types are available:\r\n\t\u2022 Pan-sharpened: A four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The fusion does not preserves all spectral features of the multispectral bands, so it should not be used for radiometric purposes. Resolution 1 m; Bands: All.\r\n\t\u2022 Bundle: Panchromatic (1 m resolution) + Multispectral bands (4 m resolution): five-band image containing the panchromatic and multispectral products packaged together, with band co-registration.\r\nThe available processing level is L1C orthorectified: a calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid.\r\nProduct Type\t\tSpatial Resolution\r\nPan-sharpened\t\t1.0 m\r\nBundle (PAN + MS)\t1.0 m (PAN), 4.0 m (MS)", "extent": {"spatial": {"bbox": [[-19, 26, 6, 45]]}, "temporal": {"interval": [["2021-03-01T00:00:00.000Z", "2023-11-15T23:59:59.999Z"]]}}, "instruments": ["HiRAIS"], "keywords": ["12-km", "620-km", "agriculture", "biosphere", "earth-science->-agriculture", "earth-science->-biosphere", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface", "forestry", "geosat-2", "geosat2spaincoverage10", "hirais", "hra-pm4-1c", "hra-psh-1c", "imaging-spectrometers/radiometers", "land-surface", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "GEOSAT-2", "title": "GEOSAT-2 Spain Coverage"}, "GOCE_Global_Gravity_Field_Models_and_Grids": {"description": "This collection contains gravity gradient and gravity anomalies grids at ground level, at satellite height. In addition it contains the GOCE gravity field models (EGM_GOC_2,EGM_GCF_2) and their covariance matrices (EGM_GVC_2):  Gridded Gravity gradients and anomalies at ground level:  GO_CONS_GRC_SPW_2__20091101T000000_20111231T235959_0001.TGZ GO_CONS_GRC_SPW_2__20091101T055147_20120731T222822_0001.TGZ GO_CONS_GRC_SPW_2__20091101T055226_20131020T033415_0002.TGZ GO_CONS_GRC_SPW_2__20091009T000000_20131021T000000_0201.TGZ Latest baseline is: GO_CONS_GRC_SPW_2__20091009T000000_20131021T000000_0201.TGZ  Gridded Gravity gradients and anomalies at satellite height:  GO_CONS_GRD_SPW_2__20091101T055147_20100630T180254_0001.TGZ GO_CONS_GRD_SPW_2__20091101T055147_20120731T222822_0001.TGZ GO_CONS_GRD_SPW_2__20091101T055226_20131020T033415_0002.TGZ GO_CONS_GRD_SPW_2__20091009T000000_20131021T000000_0201.TGZ Latest baseline is: GO_CONS_GRD_SPW_2__20091009T000000_20131021T000000_0201.TGZ  As output from the ESA-funded GOCE+ GeoExplore project, GOCE gravity gradients were combined with heterogeneous other satellite gravity information to derive a combined set of gravity gradients complementing (near)-surface data sets spanning all together scales from global down to 5 km. The data is useful for various geophysical applications and demonstrate their utility to complement additional data sources (e.g., magnetic, seismic) to enhance geophysical modelling and exploration.  The GOCE+ GeoExplore project is funded by ESA through the Support To Science Element (STSE) and was undertaken as a collaboration of the Deutsches Geod\u00e4tisches Forschungsinstitut (DGFI), Munich, DE, the Christian-Albrechts-Universit\u00e4t zu Kiel, the Geological Survey of Norway (NGU), Trondheim, Norway, TNO, the Netherlands and the University of West Bohemia, Plzen, CZ.  Read more about gravity gradients and how GOCE delivered them in this Nature article: Satellite gravity gradient grids for geophysics (https://www.nature.com/articles/srep21050)  View images of the GOCE original gravity gradients and gradients with topographic reduction grids (https://earth.esa.int/eogateway/missions/goce/data/goce-gravity-gradients-grids-map).  Available Data GRIDS File Type: GGG_225 Gridded data  - full Gravity Gradients, at 225 km and 255 km with and without topographic correction:  Computed from GOCE/GRACE gradients lower orbit phase February 2010 - October 2013  File Type: GGG_255 Gridded data  - full Gravity Gradients, at 225 km and 255 km with and without topographic correction:  Computed from GOCE/GRACE gradients nominal orbit phase February 2010 - October 2013  File Type: TGG_255 Gridded data  - full Gravity Gradients, at 225 km and 255 km with and without topographic correction:  Gravity gradient grids from topography at fixed height of 225/255 km above ellipsoid given in LNOF (Local North Oriented Frame)  File Type: TGG_225 Gridded data  - full Gravity Gradients, at 225 km and 255 km with and without topographic correction:  File Type: TGG_225 Gridded data  - full Gravity Gradients, at 225 km and 255 km with and without topographic correction:  Gravity gradient grids from topography at fixed height of 225/255 km above ellipsoid given in LNOF (Local North Oriented Frame)  MAPS File Type: Vij_225km_Patch_n.jpg Maps of Gravity Gradients with and without topographic corrections: Maps of grids from lower orbit phase with topographic correction from ETOPO1  File Type: Vij_225km_Patch_n.jpg Maps of Gravity Gradients with and without topographic corrections: Maps of the original grids from lower orbit phase without topographic correction  ALONG-ORBIT File Type: GGC_GRF Full Gravity Gradients, along-orbit, in GRF and TRF reference frames. A detailed description is provided in the data set user manual: Combined gradients from GRACE (long wavelengths) &amp; GOCE (measurement band) in the GRF (Gradiometer Reference Frame)  File Type: GGC_TRF Full Gravity Gradients, along-orbit, in GRF and TRF reference frames. A detailed description is provided in the data set user manual:  Combined gradients from GRACE (long wavelengths) &amp; GOCE (measurement band) rotated from GRF to TRF (Terrestrial Reference Frame: North, West, Up)  Direct Solution First Generation   Product:  GO_CONS_EGM_GOC_2__20091101T000000_20100110T235959_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20100110T235959_0002.TGZ  Second Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20100630T235959_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20100630T235959_0001.TGZ  Third Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20110419T235959_0001.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20110419T235959_0001.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GCF_2__20091101T000000_20110419T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Fourth Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20120801T060000_0001.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20120801T060000_0002.TGZ  Fifth Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20131020T235959_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20131020T235959_0001.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GOC_2__20091101T000000_20131020T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Sixth Generation  Product:  GO_CONS_EGM_GOC_2__20091009T000000_20131020T235959_0201.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091009T000000_20131020T235959_0201.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GOC_2__20091009T000000_20131020T235959_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Release 6 gravity model validation report (https://earth.esa.int/eogateway/documents/20142/37627/Release-6-gravity-model-validation-report-GO-TN-HPF-GS-0337-1.0.pdf)  GO-TN-HPF-GS-0337_1.0 - Rel6_Validation_Report.pdf  Time-Wise solution  First Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20100111T000000_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20100111T000000_0002.TGZ  Second Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20100705T235500_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20100705T235500_0001.TGZ  Third Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20110430T235959_0001.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20110430T235959_0001.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GCF_2__20091101T000000_20110430T235959_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Fourth Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20120618T235959_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20120618T235959_0001.TGZ  Fifth Generation  Product:  GO_CONS_EGM_GOC_2__20091101T000000_20131021T000000_0002.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091101T000000_20131021T000000_0001.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GOC_2__20091101T000000_20131021T000000_0001.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Sixth Generation Product:  GO_CONS_EGM_GOC_2__20091009T000000_20131021T000000_0201.TGZ  Variance/Covariance matrix:  GO_CONS_EGM_GVC_2__20091009T000000_20131021T000000_0202.TGZ  Coefficients (ICGEM format):  GO_CONS_EGM_GOC_2__20091009T000000_20131021T000000_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Combined gravity field GOCE model plus Antarctic and Arctic data (ICGEM format):  GO_CONS_EGM_GOC_2__20091009T000000_20160119T235959_0201.IDF (http://icgem.gfz-potsdam.de/tom_longtime)  Release 6 gravity model validation report (https://earth.esa.int/eogateway/documents/20142/37627/Release-6-gravity-model-validation-report-GO-TN-HPF-GS-0337-1.0.pdf)  GO-TN-HPF-GS-0337_1.0 - Rel6_Validation_Report.pdf", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-global-gravity-field-models-and-grids", "gps", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE Global Gravity Field Models and Grids"}, "GOCE_Level_1": {"description": "This collection contains the GOCE L1b data of the gradiometer, the star trackers, the GPS receiver, the magnetometers, magnetotorquers and the DFACS data of each accelerometer of the gradiometer. EGG_NOM_1b: latest baseline _0202  SST_NOM_1b: latest baseline _000x (always take the highest number available)  ACC_DFx_1b: latest baseline _0001 (x=1:6)  MGM_GOx_1b: latest baseline _0001 (x=1:3)  MTR_GOC_1b: latest baseline _0001  SST_RIN_1b: latest baseline _000x (always take the highest number available)  STR_VC2_1b: latest baseline _000x (always take the highest number available)  STR_VC3_1b:latest baseline _000x (always take the highest number available)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-level-1", "gps", "magnetometers", "mgm", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE Level 1"}, "GOCE_Level_2": {"description": "This collection contains GOCE level 2 data: Gravity Gradients in the gradiometer reference frame (EGG_NOM_2), in the terrestrial reference frame (EGG_TRF_2), GPS receiver derived precise science orbits (SST_PSO_2) and the non-tidal time variable gravity field potential with respect to a mean value in terms of a spherical harmonic series determined from atmospheric and oceanic mass variations as well as from a GRACE monthly gravity field time series (SST_AUX_2). EGG_NOM_2_: latest baseline: _0203  EGG_TRF_2_: latest baseline _0101  SST_AUX_2_: latest baseline _0001  SST_PSO_2_: latest baseline _0201", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-level-2", "gps", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE Level 2"}, "GOCE_TEC_and_ROTI": {"description": "GOCE total electron content (TEC) and rate of TEC index (ROTI) data.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-tec-and-roti", "gps", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE TEC and ROTI"}, "GOCE_Telemetry": {"description": "This collection contains all GOCE platform and instruments telemetry. For details see http://eo-virtual-archive1.esa.int/products/GOCE_BACKUP/MUST_TLM/GOCE_TLM_packets_description.xlsx.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-telemetry", "gps", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE Telemetry"}, "GOCE_Thermosphere_Data": {"description": "Thermospheric density and crosswind data products derived from GOCE data. latest baseline _0200  The GOCE+ Air Density and Wind Retrieval using GOCE Data project produced a dataset of thermospheric density and crosswind data products which were derived from ion thruster activation data from GOCE telemetry. The data was combined with the mission&apos;s accelerometer and star camera data products. The products provide data continuity and extend the accelerometer-derived thermosphere density data sets from the CHAMP and GRACE missions.  The resulting density and wind observations are made available in the form of time series and grids. These data can be applied in investigations of solar-terrestrial physics, as well as for the improvement and validation of models used in space operations.  Funded by ESA through the Support To Science Element (STSE) of ESA&apos;s Earth Observation Envelope Programme, supporting the science applications of ESA&apos;s Living Planet programme, the project was a partnership between TU Delft, CNES and Hypersonic Technology G\u00f6ttingen.  Dataset History Date:  18/04/2019 Change: - Time series data v2.0, covering the whole mission - Updated data set user manual - New satellite geometry and aerodynamic model (http://thermosphere.tudelft.nl/) - New vertical wind field - New data for the deorbit phase, (GPS+ACC and GPS-only versions) Reason: Updated satellite models and additional data  Date: 14/07/2016 Change: - Time series data v1.5, covering the whole mission - Updated data set user manual Reason: Removal of noisy data  Date: 31/07/2014 Change: - Time series data v1.4, covering the whole mission - Gridded data, now including error estimates - Updated data set user manual - Updated validation report; Updated ATBD Reason: Full GOCE dataset available  Date: 28/09/2013 Change: - Version 1.3 density/winds timeseries and gridded data released - User manual updated to v1.3 Reason: Bug fix and other changes  Date: 04/09/2013 - Version 1.2 density/winds timeseries and gridded data released, with user manual Reason: First public data release of thermospheric density/winds data", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["EGG"], "keywords": ["250-km", "accelerometers", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "goce-thermosphere-data", "gps", "radar-altimeters", "solid-earth", "ssti", "str", "sun-synchronous"], "license": "other", "platform": "GOCE", "title": "GOCE Thermosphere Data"}, "GeoEye-1.ESA.archive": {"description": "The GeoEye-1 archive collection consists of GeoEye-1 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years.\r\n\r\nPanchromatic (up to 40 cm resolution) and 4-Bands (up to 1.65 m resolution) products are available. The 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR1).\r\n\r\nThe processing levels are:\r\n\r\nSTANDARD (2A): normalised for topographic relief\r\nView Ready Standard (OR2A): ready for orthorectification\r\nView Ready Stereo: collected in-track for stereo viewing and manipulation\r\nMap-Ready (Ortho) 1:12,000 Orthorectified: additional processing unnecessary.\r\nSpatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service.\r\nThe following table summarises the offered product types\r\n\r\nEO-SIP product type\tBand Combination\tDescription\r\nGIS_4B__2A\t4-Band (4B)\t4-Band Standard/ 4-Band Ortho Ready Standard\r\nGIS_4B__MP\t4-Band (4B)\t4-Band Map Scale Ortho\r\nGIS_4B__OR\t4-Band (4B)\t4-Band Ortho Ready Stereo\r\nGIS_PAN_2A\tPanchromatic (PAN)\tPanchromatic Standard/ Panchromatic Ortho Ready Standard\r\nGIS_PAN_MP\tPanchromatic (PAN)\tPanchromatic Map Scale Ortho\r\nGIS_PAN_OR\tPanchromatic (PAN)\tPanchromatic Ortho Ready Stereo\r\n\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-03-29T00:00:00.000Z", "2020-07-31T23:59:59.999Z"]]}}, "instruments": ["GIS"], "keywords": ["15.2-km", "681-km", "cameras", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-environmental-governance/management", "earth-science->-human-dimensions->-human-settlements", "environmental-governance-and-management", "geoeye-1", "geoeye-1.esa.archive", "gis", "gis-4b--2a", "gis-4b--mp", "gis-4b--or", "gis-pan-2a", "gis-pan-mp", "gis-pan-or", "human-dimensions", "human-settlements", "l2", "l3", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "GeoEye-1", "processing:level": "L2,L3", "title": "GeoEye-1 ESA archive"}, "ICEYE.ESA.Archive": {"description": "The ICEYE ESA archive collection consists of ICEYE Level 1 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Three different modes are available: \u2022\tSpot: with a slant resolution of 50 cm in range by 25 cm in azimuth that translated into the ground generates a ground resolution of 1 m over an area of 5 km x 5 km. Due to multi-looking, speckle noise is significantly reduced. \u2022\tStrip: the ground swath is 30 x 50 km2 and the ground range resolution is 3 m. \u2022\tScan: a large area (100km x 100kmis acquired with ground resolution of 15m. Two different processing levels: \u2022\tSingle Look Complex (SLC): Level 1A geo-referenced product and stored in the satellite's native image acquisition geometry (the slant imaging plane) \u2022\tGround Range Detected (GRD): Level 1B product; detected, multi-looked and projected to ground range using an Earth ellipsoid model; the image coordinates are oriented along the flight direction and along the ground range; no image rotation to a map coordinate system is performed, interpolation artefacts not introduced. The following table defines the offered product types EO-SIP product type\tMode\tProcessing level XN_SM__SLC\tStrip\tSingle Look Complex (SLC) - Level 1A XN_SM__GRD\tStrip\tGround Range Detected (GRD) - Level 1B XN_SL__SLC\tSpot\tSingle Look Complex (SLC) - Level 1A XN_SL__GRD\tSpot\tGround Range Detected (GRD) - Level 1B XN_SR__GRD\tScan\tGround Range Detected (GRD) - Level 1B", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-12-03T00:00:00.000Z", null]]}}, "instruments": ["X-SAR"], "keywords": ["100-km-scan", "30-km-strip", "5-km-spot", "570-km", "earth-science->-agriculture->-agricultural-aquatic-sciences->-fisheries", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-oceans->-aquatic-sciences->-fisheries", "fisheries", "forestry", "high-resolution---hr-(5---20)-m", "iceye", "iceye.esa.archive", "imaging-radars", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "x-band-(2.8---5.2)-cm", "x-sar", "xn-sl--grd", "xn-sl--slc", "xn-sm--grd", "xn-sm--slc", "xn-sr--grd"], "license": "other", "platform": "ICEYE", "title": "ICEYE ESA archive"}, "IKONOS.ESA.archive": {"description": "ESA maintains an archive of IKONOS Geo Ortho Kit data previously requested through the TPM scheme and acquired between 2000 and 2008, over Europe, North Africa and the Middle East. The imagery products gathered from IKONOS are categorised according to positional accuracy, which is determined by the reliability of an object in the image to be within the specified accuracy of the actual location of the object on the ground. Within each IKONOS-derived product, location error is defined by a circular error at 90% confidence (CE90), which means that locations of objects are represented on the image within the stated accuracy 90% of the time. There are six levels of IKONOS imagery products, determined by the level of positional accuracy: Geo, Standard Ortho, Reference, Pro, Precision and PrecisionPlus. The product provided by ESA to Category-1 users is the Geo Ortho Kit, consisting of IKONOS Black-and-White images with radiometric and geometric corrections (1-metre pixels, CE90=15 metres) bundled with IKONOS multispectral images with absolute radiometry (4-metre pixels, CE90=50 metres). IKONOS collects 1m and 4m Geo Ortho Kit imagery (nominally at nadir 0.82m for panchromatic image, 3.28m for multispectral mode) at an elevation angle between 60 and 90 degrees. To increase the positional accuracy of the final orthorectified imagery, customers should select imagery with IKONOS elevation angle between 72 and 90 degrees. The Geo Ortho Kit is tailored for sophisticated users such as photogrammetrists who want to control the orthorectification process. Geo Ortho Kit images include the camera geometry obtained at the time of image collection. Applying Geo Ortho Kit imagery, customers can produce their own highly accurate orthorectified products by using commercial off the shelf software, digital elevation models (DEMs) and optional ground control. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/IKONOS2/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-8, -9, 75, 65]]}, "temporal": {"interval": [["2000-12-25T00:00:00.000Z", "2008-12-09T23:59:59.999Z"]]}}, "instruments": ["OSA"], "keywords": ["681-709-km", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-landscape", "earth-science->-land-surface->-topography", "ikonos-2", "ikonos.esa.archive", "landscape", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "osa", "osa-geo-1p", "photon/optical-detectors", "single-image:-11.3-km-x-11.3-km.-nominal-strip:-11-km-x-100-km", "sun-synchronous", "topography", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "IKONOS-2", "title": "IKONOS ESA archive"}, "IRS-1.archive": {"description": "IRS-1C/1D dataset is composed of products generated by the Indian Remote Sensing (IRS) Satellites 1C/1D PAN sensor. The products, acquired from 1996 to 2004 over Europe, are radiometrically and ortho corrected level 1 black and white images at 5 metre resolution and cover an area of up to 70 x 70 km. Sensor: PAN Type: Panchromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected Ortho corrected (DN) Acquisition in Neustrelitz: 1996 - 2004 5 70 x 70 X X Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/IRS1/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-20, -25, 50, 75]]}, "temporal": {"interval": [["1996-06-25T00:00:00.000Z", "2004-10-28T23:59:59.999Z"]]}}, "instruments": ["PAN", "PAN"], "keywords": ["70-km", "817-km", "cameras", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-oceans->-sea-surface-topography", "earth-science->-terrestrial-hydrosphere->-snow/ice", "high-resolution---hr-(5---20)-m", "irs-1.archive", "irs-1c", "irs-1d", "land-surface", "pan", "pan-p---1a", "sea-surface-topography", "snow-and-ice", "sun-synchronous", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "IRS-1C,IRS-1D", "title": "IRS-1C/1D European coverage"}, "Image2006": {"description": "Image 2006 collection is a SPOT-4, SPOT-5 and ResourceSat-1 (also known as IRS-P6) cloud free coverage over 38 European countries in 2006 (from February 2005 to November 2007). The Level 1 data provided in this collection originate from the SPOT-4 HRVIR instrument (with 20m spatial resolution), from SPOT-5 HRG (with 10m spatial resolution resampled to 20m) and IRS-P6 LISS III (with 23m spatial resolution), each with four spectral bands. The swath is of about 60 km for the SPOT satellites and 140 km for the IRS-P6 satellite. In addition to the Level 1, the collection provides the same data geometrically corrected towards a European Map Projection with 25m resolution. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2006/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2005-02-03T00:00:00.000Z", "2007-11-08T23:59:59.999Z"]]}}, "instruments": ["LISS-3", "HRVIR", "HRG"], "keywords": ["140-km-(irs-p6)", "60-(spot-4", "820-km-(irs-p6)", "832-km-(spot-4", "agriculture", "cameras", "earth-science->-agriculture", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "high-resolution---hr-(5---20)-m", "hrg", "hrg--x--2o", "hri--x--2o", "hrvir", "image2006", "imaging-spectrometers/radiometers", "irs-p6", "land-surface", "li3-ort-2o", "liss-3", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "spot-4", "spot-5", "spot-5)", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "IRS-P6,SPOT 4,SPOT 5", "title": "Image 2006 European coverage"}, "Image2007": {"description": "The Image 2007 collection is composed by products acquired by Disaster Monitoring Constellation 1st generation (DMC-1) satellites over European countries (plus Turkey) in 2007. The data provided in this collection are 32m multispectral images captured by the DMC SLIM-6 imager sensor, with two processing levels: \u2022 L1R Band registered product derived from the L0R product \u2022 L1T Orthorectified product derived from the L1R product using manually collected GCPs from Landsat ETM+ data and SRTM DEM V31 data Data disseminated come from the following satellites belonging to DMC-1 constellation: \u2022 UK-DMC-1 \u2022 Bejing-1 \u2022 NigeriaSat-1 Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2007/ available on the Third Party Missions Dissemination Service.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2007-04-07T00:00:00.000Z", "2007-10-09T23:59:59.999Z"]]}}, "instruments": ["SLIM6", "SLIM6", "SLIM6"], "keywords": ["600-km", "686-km", "agriculture", "beijing-1", "cameras", "earth-science->-agriculture", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "image2007", "land-surface", "medium-resolution---mr-(20---500)-m", "natural-hazards-and-disaster-risk", "nigeriasat-1", "nir-(0.75---1.30)-\u00b5m", "sl6-l1t-1p", "slim6", "sun-synchronous", "uk-dmc-1", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "UK-DMC-1,Beijing-1,NigeriaSat-1", "title": "Image 2007 European coverage"}, "JERS-1.OPS.SYC": {"description": "The JERS-1 Optical System (OPS) is composed of a Very Near Infrared Radiometer (VNIR) and a Short Wave Infrared Radiometer (SWIR). The instrument has 8 observable spectral bands from visible to short wave infrared. Data acquired by ESA ground stations The JERS-1 OPS products are available in GeoTIFF format. These products are available only for the VNIR sensor. All four bands are corrected. The correction consists in a vertical and horizontal destriping, the radiometry values are expanded from the range [0,63] to the range [0,255]. No geometrical correction is applied on level 1. The pixel size of approximately 18 x 24.2 metres for raw data is newly dimensioned to 18 x 18 metres for System Corrected data using a cubic convolution algorithm. Disclaimer: Cloud coverage for JERS OPS products has not been computed using an algorithm. The cloud cover assignment was performed manually by operators at the acquisition stations. Due to missing attitude information, the Nadir looking band (band 3) and the corresponding forward looking band (band 4) are not well coregistered, resulting in some accuracy limitations. The quality control was not performed systematically for each frame. A subset of the entire JERS Optical dataset was selected and manually checked. As a result of this, users may occasionally encounter issues with some of the individual products.", "extent": {"spatial": {"bbox": [[-130, -90, 95, 90]]}, "temporal": {"interval": [["1992-08-13T00:00:00.000Z", "1998-10-08T23:59:59.999Z"]]}}, "instruments": ["OPS"], "keywords": ["568-km", "75-km", "agriculture", "earth-science->-agriculture", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-glaciers/ice-sheets", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-glaciers/ice-sheets", "glaciers-and-ice-sheets", "high-resolution---hr-(5---20)-m", "jers-1", "jers-1.ops.syc", "nir-(0.75---1.30)-\u00b5m", "ocean-waves", "ops", "ops-syc-1p", "photon/optical-detectors", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "JERS-1", "title": "JERS-1 OPS (Optical Sensor) Very Near Infrared Radiometer (VNIR) System Corrected Products level 1"}, "JERS-1.SAR.PRI": {"description": "The JSA_PRI_1P product is comparable to the ESA PRI/IMP images generated for Envisat ASAR and ERS SAR instruments. It is a ground range projected detected image in zero-Doppler SAR coordinates, with a 12.5 metre pixel spacing. It has four overlapping looks in Doppler covering a total bandwidth of 1000Hz, with each look covering a 300Hz bandwidth. Sidelobe reduction is applied to achieve a nominal PSLR of less than -21dB. The image is not geocoded, and terrain distortion (foreshortening and layover) has not been removed. Data acquired by ESA ground stations.", "extent": {"spatial": {"bbox": [[-95, -90, 130, 90]]}, "temporal": {"interval": [["1992-07-13T00:00:00.000Z", "1998-10-08T23:59:59.999Z"]]}}, "instruments": ["SAR"], "keywords": ["568-km", "75-km", "agriculture", "earth-science->-agriculture", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-glaciers/ice-sheets", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-glaciers/ice-sheets", "glaciers-and-ice-sheets", "high-resolution---hr-(5---20)-m", "imaging-radars", "jers-1", "jers-1.sar.pri", "jsa-pri-1p", "l-band-(19.4---76.9)-cm", "ocean-waves", "sar", "sun-synchronous", "vegetation"], "license": "other", "platform": "JERS-1", "title": "JERS-1 SAR Level 1 Precision Image"}, "JERS-1.SAR.SLC": {"description": "The JSA_SLC_1P product is comparable to the ESA SLC/IMS images generated for Envisat ASAR and ERS SAR instruments. It is a slant-range projected complex image in zero-Doppler SAR coordinates. The data is sampled in natural units of time in range and along track, with the range pixel spacing corresponding to the reciprocal of the platform ADC rate and the along track spacing to the reciprocal of the PRF. Data is processed to an unweighted Doppler bandwidth of 1000Hz, without sidelobe reduction. The product is suitable for interferometric, calibration and quality analysis applications. Data acquired by ESA ground stations", "extent": {"spatial": {"bbox": [[-95, -90, 130, 90]]}, "temporal": {"interval": [["1992-07-13T00:00:00.000Z", "1998-10-08T23:59:59.999Z"]]}}, "instruments": ["SAR"], "keywords": ["568-km", "75-km", "agriculture", "earth-science->-agriculture", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-glaciers/ice-sheets", "earth-science->-oceans->-ocean-waves", "earth-science->-terrestrial-hydrosphere->-glaciers/ice-sheets", "glaciers-and-ice-sheets", "high-resolution---hr-(5---20)-m", "imaging-radars", "jers-1", "jers-1.sar.slc", "jsa-slc-1p", "l-band-(19.4---76.9)-cm", "ocean-waves", "sar", "sun-synchronous", "vegetation"], "license": "other", "platform": "JERS-1", "title": "JERS-1 SAR Level 1 Single Look Complex Image"}, "KOMPSAT-2.ESA.archive": {"description": "Kompsat-2 ESA archive collection is composed by bundle (Panchromatic and Multispectral separated images) products from the Multi-Spectral Camera (MSC) onboard KOMPSAT-2 acquired from 2007 to 2014: 1m resolution for PAN, 4m resolution for MS. Spectral Bands: \u2022 Pan: 500 - 900 nm (locate, identify and measure surface features and objects primarily by their physical appearance) \u2022 MS1 (blue): 450 - 520 nm (mapping shallow water, differentiating soil from vegetation) \u2022 MS2 (green): 520 - 600 nm (differentiating vegetation by health) \u2022 MS3 (red): 630 - 690 nm (differentiating vegetation by species) \u2022 MS4 (near-infrared): 760 - 900 nm (mapping vegetation, mapping vegetation vigor/health, Differentiating vegetation by species)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2007-04-18T00:00:00.000Z", "2014-03-21T23:59:59.999Z"]]}}, "instruments": ["MSC"], "keywords": ["15-km", "685-km", "cameras", "earth-science->-human-dimensions->-economic-resources", "earth-science->-human-dimensions->-natural-hazards", "energy-and-natural-resources", "kompsat-2", "kompsat-2.esa.archive", "mapping-and-cartography", "msc", "msc-mul-1g", "msc-mul-1r", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "KOMPSAT-2", "title": "KOMPSAT-2 ESA archive"}, "L2SW_Open": {"description": "SMOS retrieved surface wind speed gridded maps (with a spatial sampling of 1/4 x 1/4 degrees) are available in NetCDF format.\r\n\r\nEach product contains parts of ascending and descending orbits and it is generated by Ifremer, starting from the SMOS L1B data products, in Near Real Time i.e. within 4 to 6 hours from sensing time.\r\n\r\nBefore using this dataset, please check the read-me-first note available in the Resources section below.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-05-01T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "302", "758-km", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring", "earth-science->-oceans->-ocean-circulation", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "l2sw-open", "marine-environment-monitoring", "miras", "ocean-circulation", "oceans", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS NRT L2 Swath Wind Speed"}, "L3SW_Open": {"description": "SMOS L3WS products are daily composite maps of the collected SMOS L2 swath wind products for a specific day, provided with the same grid than the Level 2 wind data (SMOS L2WS NRT) but separated into ascending and descending passes.\r\n\r\nThis product is available the day after sensing from Ifremer, in NetCDF format.\r\n\r\nBefore using this dataset, please check the read-me-first note available in the Resources section below.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-05-01T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "305", "758-km", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring", "earth-science->-oceans->-ocean-circulation", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "l3sw-open", "marine-environment-monitoring", "miras", "ocean-circulation", "oceans", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS L3 Daily Wind Speed"}, "L3_FT_Open": {"description": "The SMOS Level 3 Freeze and Thaw (F/T) product provides daily information on the soil state in the Northern Hemisphere based on SMOS observations and associated ancillary data.  Daily products, in NetCDF format, are generated by the Finnish Meteorological Institute (FMI) and are available from 2010 onwards.  The processing algorithm makes use of gridded Level 3 brightness temperatures provided by CATDS (https://www.catds.fr). The data is provided in the Equal-Area Scalable Earth Grid (EASE2-Grid), at 25 km x 25 km resolution.   For an optimal exploitation of this dataset, please refer to the Resources section below to access Product Specifications, read-me-first notes, etc.", "extent": {"spatial": {"bbox": [[-180, 0, 180, 85]]}, "temporal": {"interval": [["2010-06-01T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "300", "758-km", "earth-science->-cryosphere->-frozen-ground", "earth-science->-land-surface", "earth-science->-land-surface->-frozen-ground", "frozen-ground-and-permafrost", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "l3-ft-open", "land-surface", "miras", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS Soil Freeze and Thaw State"}, "L3_SIT_Open": {"description": "The SMOS Level 3 Sea Ice Thickness product, in NetCDF format, provides daily estimations of SMOS-retrieved sea ice thickness (and its uncertainty) at the edge of the Arctic Ocean during the October-April (winter) season, from year 2010 onwards. The sea ice thickness is retrieved from the SMOS L1C product, up to a depth of approximately 0.5-1 m, depending on the ice temperature and salinity. Daily maps, projected on polar stereographic grid of 12.5 km, are generated by the Alfred Wegener Institut (AWI). This product is complementary with sea ice thickness measurements from ESA&apos;s CryoSat and Copernicus Sentinel-3 missions.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2010-10-15T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "3.3", "758-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans->-sea-ice", "interferometric-radiometers", "l3-sit-open", "miras", "sea-ice", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS Level 3C Sea Ice Thickness"}, "L4WR_Open": {"description": "The SMOS WRF product is available in Near Real Time to support tropical cyclones (TC) forecasts. \r\nIt is generated within 4 to 6 hours from sensing from the SMOS L2 swath wind speed products, in the so-called \"Fix (F-deck)\" format compatible with the US Navy's ATCF (Automated Tropical Cyclone Forecasting) System.\r\n\r\nThe SMOS WRF \"fixes\" to the best-track forecasts contain: the SMOS 10-min maximum-sustained winds (in knots) and wind radii (in nautical miles) for the 34 kt (17 m/s), 50 kt (25 m/s) and 64 kt (33 m/s) winds per geographical storm quadrants, and for each SMOS pass intercepting a TC in all the active ocean basins.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-05-01T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "758-km", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring", "earth-science->-oceans->-ocean-circulation", "human-dimensions", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "l4wr-open", "marine-environment-monitoring", "miras", "natural-hazards-and-disaster-risk", "ocean-circulation", "oceans", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS Tropical Cyclone Wind Radii Fixes"}, "L4_SIT_Open": {"description": "The SMOS-CryoSat merged Sea Ice Thickness Level 4 product, in NetCDF format, is based on estimates from both the MIRAS and the SIRAL instruments, with a significant reduction in the relative uncertainty for the thickness of the thin ice.  A weekly averaged product is generated every day by the Alfred Wegener Institut (AWI), by merging the weekly AWI CryoSat-2 sea ice product and the daily SMOS sea ice thickness retrieval.  All grids are projected onto the 25 km EASE2 Grid, based on a polar aspect spherical Lambert azimuthal equal-area projection. The grid dimension is 5400 x 5400 km, equal to a 432 x 432 grid centered on the geographic Pole.  Coverage is limited to the October-April (winter) period for the Northern Hemisphere, due to the melting season, from year 2010 onwards.", "extent": {"spatial": {"bbox": [[-180, -16.6, 180, 90]]}, "temporal": {"interval": [["2010-11-15T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "204", "758-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans->-sea-ice", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "l4-sit-open", "miras", "sea-ice", "smos", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS-CryoSat L4 Sea Ice Thickness"}, "LANDSAT.ETM.GTC": {"description": "This dataset contains all the Landsat 7 Enhanced Thematic Mapper high-quality ortho-rectified L1T dataset (or L1Gt where not enough GCPs are available) over Kiruna, Maspalomas, Matera and Neustrelitz visibility masks. The Landsat 7 ETM+ scenes typically covers 185 x 170 km. A standard full scene is nominally centred on the intersection between a Path and Row (the actual image centre can vary by up to 100m). Each band requires 50MB (uncompressed), and Band 8 requires 200MB (panchromatic band with resolution of 15m opposed to 30m).", "extent": {"spatial": {"bbox": [[-60, -20, 60, 80]]}, "temporal": {"interval": [["1999-07-09T00:00:00.000Z", "2003-12-31T23:59:59.999Z"]]}}, "instruments": ["ETM"], "keywords": ["185-km", "3.08", "705-km", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "etm", "etm-gtc-1p", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "land-use-and-land-cover", "landsat-7", "landsat.etm.gtc", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-7", "title": "Landsat ETM+ ESA archive"}, "LANDSAT.TM.GTC": {"description": "This dataset contains all the Landsat 5 Thematic Mapper high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Matera, Kiruna and Maspalomas visibility masks, as well as campaign data over Malindi, Bishkek, Chetumal, Libreville and O'Higgins. The acquired Landsat TM scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 100m). A full image is composed of 6920 pixels x 5760 lines and each band requires 40 Mbytes of storage space (uncompressed) at 30m spatial resolution in the VIS, NIR and SWIR as well as 120m in the TIR spectral range.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1984-04-06T00:00:00.000Z", "2011-11-16T23:59:59.999Z"]]}}, "instruments": ["TM"], "keywords": ["185-km", "3.08", "705-km", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "imaging-spectrometers/radiometers", "land-use-and-land-cover", "landsat-5", "landsat.tm.gtc", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "tm", "tm--geo-1p", "tm--gtc-1p", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-5", "title": "Landsat TM ESA archive"}, "Landsat5TMEuropeandNorthAfricaCoverage198485": {"description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from April 1984 to December 1985. The available data products have a cloud cover percentage of less than 20%.\r\nThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "extent": {"spatial": {"bbox": [[-28, 20, 43, 73]]}, "temporal": {"interval": [["1984-04-07T00:00:00.000Z", "1985-12-03T23:59:59.999Z"]]}}, "instruments": ["TM"], "keywords": ["185-km", "3.08", "705-km", "biosphere", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "landsat-5", "landsat5tmeuropeandnorthafricacoverage198485", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "tm", "tm--gtc-1p", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-5", "title": "Landsat 5 TM Europe and North Africa Coverage 1984-85"}, "Landsat5TMEuropeandNorthAfricaCoverage198689": {"description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from January 1986 to November 1989. The available data products have a cloud cover percentage of less than 20%.\r\nThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "extent": {"spatial": {"bbox": [[-28, 20, 43, 73]]}, "temporal": {"interval": [["1986-01-08T00:00:00.000Z", "1989-11-30T23:59:59.999Z"]]}}, "instruments": ["TM"], "keywords": ["185-km", "3.08", "705-km", "biosphere", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "landsat-5", "landsat5tmeuropeandnorthafricacoverage198689", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "tm", "tm--gtc-1p", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-5", "title": "Landsat 5 TM Europe and North Africa Coverage 1986-89"}, "Landsat5TMEuropeandNorthAfricaCoverage199598": {"description": "This collections contains Landsat 5 Thematic Mapper (TM) imagery acquired over Europe and North Africa from January 1995 to December 1998. The available data products have a cloud cover percentage of less than 20%.\r\nThe acquired Landsat 5 TM scenes have a footprint of approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre may deviate by up to 100 m). The data are system corrected.", "extent": {"spatial": {"bbox": [[-28, 20, 43, 73]]}, "temporal": {"interval": [["1995-01-02T00:00:00.000Z", "1998-12-26T23:59:59.999Z"]]}}, "instruments": ["TM"], "keywords": ["185-km", "3.08", "705-km", "biosphere", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "landsat-5", "landsat5tmeuropeandnorthafricacoverage199598", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "tm", "tm--gtc-1p", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-5", "title": "Landsat 5 TM Europe and North Africa Coverage 1995-98"}, "LandsatETMCloudFree": {"description": "This dataset contains the cloud-free products from Landsat 7 Enhanced Thematic Mapper collection acquired over Europe, North Africa and middle East; for each scene only one product is selected, with the minimal cloud coverage. The Landsat 7 ETM+ scenes typically cover 185 x 170 km. A standard full scene is nominally centred on the intersection between a Path and Row (the actual image centre can vary by up to 100m). The data are system corrected.", "extent": {"spatial": {"bbox": [[-27, 19, 47, 73]]}, "temporal": {"interval": [["1999-07-10T00:00:00.000Z", "2003-05-23T23:59:59.999Z"]]}}, "instruments": ["ETM"], "keywords": ["185-km", "3.08", "705-km", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "etm", "etm-gtc-1p", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "land-use-and-land-cover", "landsat-7", "landsatetmcloudfree", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-7", "title": "Landsat 7 ETM+ European and Mediterranean Countries Cloud Free Collection"}, "LandsatTMCloudFree": {"description": "This dataset contains the cloud-free products from Landsat 5 Thematic Mapper collection acquired over Europe, North Africa and middle East; for each scene only one product is selected, with the minimal cloud coverage. The acquired Landsat TM scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 100 m). The data are system corrected.", "extent": {"spatial": {"bbox": [[-28, 20, 43, 73]]}, "temporal": {"interval": [["1986-09-18T00:00:00.000Z", "1995-09-24T23:59:59.999Z"]]}}, "instruments": ["MSS"], "keywords": ["185-km", "3.08", "705-km", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-land-surface->-surface-radiative-properties", "imaging-spectrometers/radiometers", "land-use-and-land-cover", "landsat-5", "landsattmcloudfree", "medium-resolution---mr-(20---500)-m", "mss", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "surface-radiative-properties", "swir-(1.3---3.0)-\u00b5m", "tir-(6.0---15.0)-\u00b5nm", "tm", "tm--gtc-1p", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-5", "title": "Landsat 5 TM European and Mediterranean Countries Cloud Free Collection"}, "Landsat_MSS_ESA_Archive": {"description": "This dataset contains all the Landsat 1 to Landsat 5 Multi Spectral Scanner (MSS) high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Kiruna (active from April to September only) and Maspalomas (on campaign basis) visibility masks. The acquired Landsat MSS scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 200m). The altitude changed from 917 Km to 705 km and therefore two World Reference Systems (WRS) were. A full image is composed of 3460 pixels x 2880 lines with a pixel size of 60m. Level 1 Geometrically and terrain corrected GTC products (L1T) are available: it is the most accurate level of processing as it incorporates Ground Control Points (GCPs) and a Digital Elevation Model (DEM) to provide systematic geometric and topographic accuracy, with geodetic accuracy dependent on the number, spatial distribution and accuracy of the GCPs over the scene extent, and the resolution of the DEM used.", "extent": {"spatial": {"bbox": [[-22, -24, 44, 71]]}, "temporal": {"interval": [["1975-04-21T00:00:00.000Z", "1993-12-31T23:59:59.999Z"]]}}, "instruments": ["MSS", "MSS", "MSS", "MSS", "MSS"], "keywords": ["185-km", "3.08", "705-km-(landsat-4-and-landsat-5)", "917-km-(landsat-1-to-landsat-3)", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "imaging-spectrometers/radiometers", "land-use-and-land-cover", "landsat-1", "landsat-2", "landsat-3", "landsat-4", "landsat-5", "landsat-mss-esa-archive", "medium-resolution---mr-(20---500)-m", "mss", "mss-geo-1p", "mss-gtc-1p", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-1,Landsat-2,Landsat-3,Landsat-4,Landsat-5", "title": "Landsat MSS ESA Archive"}, "Landsat_RBV": {"description": "This dataset contains Landsat 3 Return Beam Vidicon (RBV) products, acquired by ESA by the Fucino ground station over its visibility mask. The data (673 scenes) are the result of the digitalization of the original 70 millimetre (mm) black and white film rolls.\r\nThe RBV instrument was mounted on board the Landsat 1 to 3 satellites between 1972 and 1983, with 80 meter resolution. Three independent co-aligned television cameras, one for each spectral band (band 1: blue-green, band 2: yellow-red, band 3: NIR), constituted this instrument. \r\nThe RBV system was redesigned for Landsat 3 to use two cameras operating in one broad spectral band (green to near-infrared; 0.505\u20130.750 \u00b5m), mounted side-by-side, with panchromatic spectral response and higher spatial resolution than on Landsat-1 and Landsat-2. Each of the cameras produced a swath of about 90 km (for a total swath of 180 km), with a spatial resolution of 40 m.", "extent": {"spatial": {"bbox": [[20, -90, 50, 75]]}, "temporal": {"interval": [["1978-11-01T00:00:00.000Z", "2018-08-01T23:59:59.999Z"]]}}, "instruments": ["RBV"], "keywords": ["180-km", "917-km", "cameras", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface->-land-use/land-cover", "land-use-and-land-cover", "landsat-3", "landsat-rbv", "medium-resolution---mr-(20---500)-m", "rbv", "rbv-pan-1p", "sun-synchronous", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Landsat-3", "title": "Landsat RBV"}, "MER.RR__1P": {"description": "The MERIS Level 1 Reduced Resolution (RR) product contains the Top of Atmosphere (TOA) upwelling spectral radiance measures at reduced resolution. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS RR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 measurements data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values &quot;Band wavelength&quot; and &quot;Bandwidth&quot; provided in the Manifest file of the Level 1 products are the averaged band-pass of each pixel over the instrument field of view. The Auxiliary data used are listed in the Manifest file associated to each product. MERIS was operating continuously on the day side of the Envisat orbit (descending track). RR data was acquired over 43.5 minutes in each orbit, i.e. 80% of the descending track.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-04-29T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MERIS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-winds", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-winds", "envisat", "imaging-spectrometers/radiometers", "mer.rr--1p", "meris", "meris-rr---1040-m-across-track-/-1160-m-along-track", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat MERIS Reduced Resolution - Level 1 [MER_RR__1P/ME_1_RRG]"}, "MER.RR__2P": {"description": "MERIS RR Level 2 is a Reduced Resolution (RR) Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS RR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file, which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing mainly: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters. The Auxiliary data used are listed in the Manifest file associated to each product. MERIS was operating continuously on the day side of the Envisat orbit (descending track). RR data was acquired over 43.5 minutes in each orbit, i.e. 80% of the descending track.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-04-29T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MERIS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-winds", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-winds", "envisat", "imaging-spectrometers/radiometers", "mer.rr--2p", "meris", "meris-rr---1040-m-across-track-/-1160-m-along-track", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat MERIS Reduced Resolution Geophysical Product - Level 2 [MER_RR__2P]"}, "MER_FRS_1P": {"description": "The MERIS Level 1 Full Resolution (FR) product contains the Top of Atmosphere (TOA) upwelling spectral radiance measures. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS FR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The 15 sun spectral flux values provided in the instrument data file of the Level 1 products are the in-band reference irradiances adjusted for the Earth-sun distance at the time of measurement. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values &quot;Band wavelength&quot; and &quot;Bandwidth&quot; provided in the Manifest file of the Level 1b products are the averaged band-pass of each pixel over the instrument field of view. Auxiliary data are also listed in the Manifest file associated to each product. The Level 1 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0 (for a maximum of 20 minutes). Thus the estimated size of the Level 1 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-05-17T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MERIS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-winds", "clouds", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-winds", "earth-science->-atmosphere->-clouds", "earth-science->-atmosphere->-precipitation", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans", "envisat", "imaging-spectrometers/radiometers", "land-surface", "mer-frs-1p", "meris", "meris-fr---260-m-across-track-/-290-m-along-track", "oceans", "precipitation", "sun-synchronous", "topography"], "license": "other", "platform": "Envisat", "title": "Envisat MERIS Full Resolution - Level 1 [MER_FRS_1P/ME_1_FRG]"}, "MER_FRS_2P": {"description": "MERIS FR Level 2 is a Full-Resolution Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS FR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters and annotation. The Auxiliary data used are listed in the Manifest file associated to each product. The Level 2 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0/Level 1. Thus the estimated size of the Level 2 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-05-17T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["MERIS"], "keywords": ["5---1150-km", "800-km", "atmosphere", "atmospheric-winds", "clouds", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-winds", "earth-science->-atmosphere->-clouds", "earth-science->-atmosphere->-precipitation", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans", "envisat", "imaging-spectrometers/radiometers", "land-surface", "mer-frs-2p", "meris", "meris-fr---260-m-across-track-/-290-m-along-track", "oceans", "precipitation", "sun-synchronous", "topography"], "license": "other", "platform": "Envisat", "title": "Envisat MERIS Full Resolution - Level 2 [MER_FRS_2P/ME_2_FRG]"}, "NRT_Open": {"description": "The SMOS Near Real Time products include Level 1 geo-located brightness temperature and Level 2 geo-located soil moisture estimation. The SMOS NRT L1 Light BUFR product contains brightness temperature geo-located on a reduced Gaussian grid (T511/N256), only for \"land\" pixels but keeping the full angular resolution. The pixels are consolidated in a full orbit dump segment (i.e. around 100 minutes of sensing time) with a maximum size of about 30MB per orbit. Spatial resolution is in the range of 30-50 km. This product is distributed in BUFR format. The SMOS NRT L2 Soil Moisture Neural Network (NN) product provides NRT soil moisture data based on the statistical coefficients estimated by a neural network. It is provided in the SMOS DGG grid and only at the satellite track. It also provides an estimation of the uncertainty of the estimated soil moisture product, and the probability that a soil moisture value is contaminated by Radio Frequency Interference (RFI). This product is distributed in NetCDF format. The L2 data product is also distributed via the EUMETCast Europe Service (DVB), upon registration on the EUMETSAT Earth Observation Portal (https://eoportal.eumetsat.int/userMgmt/gateway.faces). The Ku-band DVB reception station must be situated within the service coverage in Europe. SMOS NRT data is also regularly delivered to the UK Met-Office, then made available to operational agencies and research and development institutes via the WMO GTS Network. For an optimal exploitation of the SMOS NRT products please consult the read-me-first notes available in the Resources section below.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-05-09T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "758-km", "earth-science->-agriculture->-soils", "earth-science->-agriculture->-soils->-soil-moisture/water-content", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "land-surface", "miras", "nrt-open", "smos", "soil-moisture", "soils", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS NRT Data Products"}, "OceanSat-2.NRT.data": {"description": "ESA, in collaboration with GAF AG, acquired and processed every day OceanSat-2 passes over Neutrelitz reception station from January 2016 to November 2021. All passes were systematically processed to levels 1B, 2B and 2C, and available to users in NRT (< 3 hours). Products are available in: \u2022 Level 1B: Geophysical Data containing Radiance Data for all 8 Bands of OCM-2 \u2022 Level 2B: Geophysical Data L2B for given Geo physical parameter. Geo physical parameters: Chlorophyll, Aerosol Depth, Different Attenuation, Total Suspended Sediments \u2022 Level 2C: Georeferenced Radiance Data for given geo physical parameter. Geo physical parameters: Chlorophyll, Aerosol Depth, Different Attenuation, Total Suspended Sediments", "extent": {"spatial": {"bbox": [[-20, -30, 41, 70]]}, "temporal": {"interval": [["2015-10-27T00:00:00.000Z", "2021-11-07T23:59:59.999Z"]]}}, "instruments": ["OCM-2"], "keywords": ["1420-km", "720-km", "coastal-processes", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-terrestrial-hydrosphere->-snow/ice", "earth-science->-terrestrial-hydrosphere->-surface-water", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "oceans", "oceansat-2", "oceansat-2.nrt.data", "ocm-2", "ocm2-la-1b", "ocm2-la-2b", "ocm2-la-2c", "photon/optical-detectors", "snow-and-ice", "sun-synchronous", "surface-water", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "OceanSat-2", "title": "OceanSat-2 NRT data"}, "PAZ.ESA.archive": {"description": "The PAZ ESA archive collection consists of PAZ Level 1 data previously requested by ESA supported projects over their areas of interest around the world and, as a consequence, the products are scattered and dispersed worldwide and in different time windows. The dataset regularly grows as ESA collects new products over the years. Available modes are: \u2022\tStripMap mode (SM): SSD less than 3m for a scene 30km x 50km in single polarization or 15km x 50km in dual polarisation \u2022\tScanSAR mode (SC): the scene is 100 x 150 km2, SSD less than 18m in signle pol only \u2022\tWide ScanSAR mode (WS): single polarisation only, with SS less than 40m and scene size of 270 x 200 km2 \u2022\tSpotlight modes (SL): SSD less than 2m for a scene 10km x 10km, both single and dual polarization are available \u2022\tHigh Resolution Spotlight mode (HS): in both single and dual polarisation, the scene is 10x5 km2, SSD less than 1m \u2022\tStaring Spotlight mode (ST): SSD is 25cm, the scene size is 4 x 4 km2, in single polarisation only. The available geometric projections are: \u2022\tSingle Look Slant Range Complex (SSC): single look product, no geocoding, no radiometric artifact included, the pixel spacing is equidistant in azimuth and in ground range \u2022\tMulti Look Ground Range Detected (MGD): detected multi look product, simple polynomial slant-to-ground projection is performed in range, no image rotation to a map coordinate system is performed \u2022\tGeocoded Ellipsoid Corrected (GEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid with no terrain corrections \u2022\tEnhanced Ellipsoid Corrected (EEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid, the image distortions caused by varying terrain height are corrected using a DEM The following table summarises the offered product types EO-SIP product type\tOperation Mode\tGeometric Projection PSP_SM_SSC\tStripmap (SM)\tSingle Look Slant Range Complex (SSC) PSP_SM_MGD\tStripmap (SM)\tMulti Look Ground Range Detected (MGD) PSP_SM_GEC\tStripmap (SM)\tGeocoded Ellipsoid Corrected (GEC) PSP_SM_EEC\tStripmap (SM)\tEnhanced Ellipsoid Corrected (EEC) PSP_SC_MGD\tScanSAR (SC)\tSingle Look Slant Range Complex (SSC) PSP_SC_GEC\tScanSAR (SC)\tMulti Look Ground Range Detected (MGD) PSP_SC_EEC\tScanSAR (SC)\tGeocoded Ellipsoid Corrected (GEC) PSP_SC_SSC\tScanSAR (SC)\tEnhanced Ellipsoid Corrected (EEC) PSP_SL_SSC\tSpotlight (SL)\tSingle Look Slant Range Complex (SSC) PSP_SL_MGD\tSpotlight (SL)\tMulti Look Ground Range Detected (MGD) PSP_SL_GEC\tSpotlight (SL)\tGeocoded Ellipsoid Corrected (GEC) PSP_SL_EEC\tSpotlight (SL)\tEnhanced Ellipsoid Corrected (EEC) PSP_HS_SSC\tHigh Resolution Spotlight (HS)\tSingle Look Slant Range Complex (SSC) PSP_HS_MGD\tHigh Resolution Spotlight (HS)\tMulti Look Ground Range Detected (MGD) PSP_HS_GEC\tHigh Resolution Spotlight (HS)\tGeocoded Ellipsoid Corrected (GEC) PSP_HS_EEC\tHigh Resolution Spotlight (HS)\tEnhanced Ellipsoid Corrected (EEC) PSP_ST_SSC\tStaring Spotlight (ST)\tSingle Look Slant Range Complex (SSC) PSP_ST_MGD\tStaring Spotlight (ST)\tMulti Look Ground Range Detected (MGD) PSP_ST_GEC\tStaring Spotlight (ST)\tGeocoded Ellipsoid Corrected (GEC) PSP_ST_EEC\tStaring Spotlight (ST)\tEnhanced Ellipsoid Corrected (EEC) PSP_WS_SSC\tWide ScanSAR (WS)\tSingle Look Slant Range Complex (SSC) PSP_WS_MGD\tWide ScanSAR (WS)\tMulti Look Ground Range Detected (MGD) PSP_WS_GEC\tWide ScanSAR (WS)\tGeocoded Ellipsoid Corrected (GEC) PSP_WS_EEC\tWide ScanSAR (WS)\tEnhanced Ellipsoid Corrected (EEC)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-09-06T00:00:00.000Z", null]]}}, "instruments": ["PAZ-SAR"], "keywords": ["100x150-km-scansar", "10x10-km-spotlight", "10x5-km-hr-spotlight", "15/30-x50-km-stripmap", "514-km", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "high-resolution---hr-(5---20)-m", "imaging-radars", "natural-hazards-and-disaster-risk", "oceans", "paz", "paz-sar", "paz.esa.archive", "psp-hs-eec", "psp-hs-gec", "psp-hs-mgd", "psp-hs-ssc", "psp-sc-eec", "psp-sc-gec", "psp-sc-mgd", "psp-sc-ssc", "psp-sl-eec", "psp-sl-gec", "psp-sl-mgd", "psp-sl-ssc", "psp-sm-eec", "psp-sm-gec", "psp-sm-mgd", "psp-sm-ssc", "psp-st-eec", "psp-st-gec", "psp-st-mgd", "psp-st-ssc", "psp-ws-eec", "psp-ws-gec", "psp-ws-mgd", "psp-ws-ssc", "soils", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "x-band-(2.8---5.2)-cm"], "license": "other", "platform": "PAZ", "title": "PAZ ESA archive"}, "PROBA.CHRIS.1A": {"description": "CHRIS acquires a set of up to five images of each target during each acquisition sequence, these images are acquired when Proba-1 is pointing at distinct angles with respect to the target. CHRIS Level 1A products (supplied in HDF data files, version 4.1r3) include five formal CHRIS imaging modes, classified as modes 1 to 5: \u2022 MODE 1: Full swath width, 62 spectral bands, 773nm / 1036nm, nadir ground sampling distance 34m @ 556km \u2022 MODE 2 WATER BANDS: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 3 LAND CHANNELS: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 4 CHLOROPHYL BAND SET: Full swath width, 18 spectral bands, nadir ground sampling distance 17m @ 556km \u2022 MODE 5 LAND CHANNELS: Half swath width, 37 spectral bands, nadir ground sampling distance 17m @ 556km All Proba-1 passes are systematically acquired according to the current acquisition plan, CHRIS data are processed every day to Level 1A and made available to ESA users. Observation over a new specific area can be performed by submitting the request to add a new site to the acquisition plan. Valuable indication whether the acquisition was successfully, cloudy, failed or programmed is reported in the _$$Proba-CHRIS Actual Acquisitions$$ http://www.rsacl.co.uk/chris/excel/active/", "extent": {"spatial": {"bbox": [[-180, -56, 180, 75]]}, "temporal": {"interval": [["2002-05-14T00:00:00.000Z", "2022-12-22T23:59:59.999Z"]]}}, "instruments": ["CHRIS"], "keywords": ["14-km", "615-km", "chr-mo1-1p", "chr-mo2-1p", "chr-mo3-1p", "chr-mo4-1p", "chr-mo5-1p", "chris", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-oceans", "earth-science->-terrestrial-hydrosphere->-surface-water", "forestry", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "medium-resolution---mr-(20---500)-m", "nir-(0.75---1.30)-\u00b5m", "oceans", "proba-1", "proba.chris.1a", "sun-synchronous", "surface-water", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "PROBA-1", "title": "Proba CHRIS Level 1A"}, "PROBA.HRC.1A": {"description": "The HRC Level 1A product is an image images with a pixel resolution of 8m. The data are grey scale images, an image contains 1026 x 1026 pixels and covers an area of 25 km2. HRC data is supplied in BMP format. All Proba-1 passes are systematically acquired according to the current acquisition plan, HRC data are processed every day to Level 1A and made available to ESA users.", "extent": {"spatial": {"bbox": [[-180, -56, 180, 75]]}, "temporal": {"interval": [["2002-10-10T00:00:00.000Z", null]]}}, "instruments": ["HRC"], "keywords": ["4-km", "615-km", "cameras", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-oceans", "earth-science->-terrestrial-hydrosphere->-surface-water", "forestry", "high-resolution---hr-(5---20)-m", "hrc", "hrc-hrc-1p", "oceans", "proba-1", "proba.hrc.1a", "sun-synchronous", "surface-water", "vegetation", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "PROBA-1", "title": "Proba HRC"}, "PlanetScopeESAarchive": {"description": "The PlanetScope ESA archive collection consists of PlanetScope products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products.\r\n\r\nThree product lines for PlanetScope imagery are offered, for all of them the Ground Sampling Distance at nadir is 3.7 m (at reference altitude 475 km).\r\n\r\nEO-SIP Product Type\tProduct description\tProcessing Level\r\nPSC_DEF_S3\t3 bands \u2013 Analytic and Visual - Basic and Ortho Scene\tlevel 1B and 3B\r\nPSC_DEF_S4\t4 bands \u2013 Analytic and Visual - Basic and Ortho Scene\tlevel 1B and 3B\r\nPSC_DEF_OT\t3 bands, 4 bands and 5 bands \u2013 Analytic and Visual - Ortho Tile\tlevel 3A\r\n \r\n\r\nThe Basic Scene product is a single-frame scaled Top of Atmosphere Radiance (at sensor) and sensor-corrected product. The product is not orthorectified or corrected for terrain distortions, radiometric and sensor corrections are applied to the data.\r\nThe Ortho Scenes product is a single-frame scaled Top of Atmosphere Radiance (at sensor) or Surface Reflectance image product. The product is radiometrically, sensor and geometrically corrected and is projected to a cartographic map (UTM/WGS84).\r\nThe Ortho Tiles are multiple orthorectified scenes in a single strip that have been merged and then divided according to a defined grid. Radiometric and sensor corrections are applied, the imagery is orthorectified and projected to a UTM projection.\r\nSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/PlanetScope available on the Third Party Missions Dissemination Service.\r\n\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -84, 180, 84]]}, "temporal": {"interval": [["2016-08-08T00:00:00.000Z", null]]}}, "instruments": ["PlanetScope Camera"], "keywords": ["25-km", "475-km", "diseases-and-pests", "earth-science->-agriculture->-agricultural-plant-science->-plant-diseases/disorders/pests", "earth-science->-agriculture->-agricultural-plant-science->-weeds", "imaging-spectrometers/radiometers", "invasive-species", "nir-(0.75---1.30)-\u00b5m", "noxious-plants-or-invasive-plants", "planetscope", "planetscope-camera", "planetscopeesaarchive", "psc-3ao-3b", "psc-3vo-3b", "psc-4ab-1b", "psc-4ao-3b", "psc-4sr-3b", "psc-8ab-1b", "psc-8ao-3b", "psc-8sr-3b", "psc-def-ot", "psc-def-s3", "psc-def-s4", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "PlanetScope", "title": "PlanetScope ESA archive"}, "Pleiades.ESA.archive": {"description": "The Pl\u00e9iades ESA archive is a dataset of Pl\u00e9iades-1A and 1B products that ESA collected over the years. The dataset regularly grows as ESA collects new Pl\u00e9iades products.\r\n\r\nPl\u00e9iades Primary and Ortho products can be available in the following modes:\r\n\r\n \u2022 Panchromatic image at 0.5 m resolution\r\n \u2022 Pansharpened colour image at 0.5 m resolution\r\n \u2022 Multispectral image in 4 spectral bands at 2 m resolution\r\n \u2022 Bundle (0.5 m panchromatic image + 2 m multispectral image)\r\n\r\nSpatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service. \r\n\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2011-01-01T00:00:00.000Z", null]]}}, "instruments": ["HiRI"], "keywords": ["20-km", "694-km", "earth-science->-human-dimensions->-human-settlements", "earth-science->-human-dimensions->-natural-hazards", "hir-ms--1a", "hir-ms--3-", "hir-p---1a", "hir-p---3-", "hir-p-s-1a", "hir-p-s-2-", "hir-p-s-3-", "hir-pms-1a", "hir-pms-2-", "hir-pms-3-", "hiri", "human-settlements", "imaging-spectrometers/radiometers", "mapping-and-cartography", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "pleiades", "pleiades.esa.archive", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Pleiades", "title": "Pleiades ESA archive"}, "PleiadesNeoESAArchive10": {"description": "The Pleiades Neo ESA archive collection is comprised of Pleiades Neo products that ESA has collected over the years. The dataset regularly grows as ESA collects new Pleiades Neo products.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-10-26T00:00:00.000Z", null]]}}, "instruments": ["PNEO"], "keywords": ["14-km-at-nadir", "620-km", "agriculture", "biosphere", "earth-science->-agriculture", "earth-science->-biosphere", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-economic-resources", "earth-science->-land-surface", "energy-and-natural-resources", "forestry", "human-dimensions", "imaging-spectrometers/radiometers", "land-surface", "neo-ms--1a", "neo-ms--2-", "neo-ms--3-", "neo-p---1a", "neo-p---2-", "neo-p---3-", "neo-p-s-1a", "neo-p-s-2-", "neo-p-s-3-", "neo-pms-1a", "neo-pms-2-", "neo-pms-3-", "nir-(0.75---1.30)-\u00b5m", "pleiades-neo", "pleiadesneoesaarchive10", "pneo", "security", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "Pleiades Neo", "title": "Pleiades Neo ESA Archive"}, "QuickBird-2.ESA.archive": {"description": "The QuickBird-2 archive collection consists of QuickBird-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Panchromatic (up to 61 cm resolution) and 4-Bands (up to nominal value of 2.44m resolution, reduced to 1.63m when at the end of the mission the orbit altitude was lowered to 300km) products are available; the 4-Bands includes various options such as Multispectral (separate channel for BLUE, GREEN, RED, NIR1), Pan-sharpened (BLUE, GREEN, RED, NIR1), Bundle (separate bands for PAN, BLUE, GREEN, RED, NIR1), Natural Color (pan-sharpened BLUE, GREEN, RED), Colored Infrared (pan-sharpened GREEN, RED, NIR1), Natural Colour / Coloured Infrared (3-Band pan-sharpened) The processing levels are: \u2022 STANDARD (2A): normalized for topographic relief \u2022 VIEW READY STANDARD (OR2A): ready for orthorectification \u2022 VIEW READY STEREO: collected in-track for stereo viewing and manipulation \u2022 MAP-READY (ORTHO) 1:12.000 Orthorectified: additional processing unnecessary \u2022 MAP-READY (ORTHO) 1:15.000 Orthorectified: additional processing unnecessary", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-02-11T00:00:00.000Z", "2012-05-25T23:59:59.999Z"]]}}, "instruments": ["BGI"], "keywords": ["16.5-km", "450-km-(300-km-at-end-of-mission)", "bgi", "bgi-4b--2a", "bgi-4b--mp", "bgi-pan-2a", "bgi-pan-mp", "biosphere", "cameras", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "high-resolution---hr-(5---20)-m", "nir-(0.75---1.30)-\u00b5m", "quickbird-2", "quickbird-2.esa.archive", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "QuickBird-2", "title": "QuickBird-2 ESA archive"}, "RADARSAT1ESAArchive10": {"description": "ESA archive of RADARSAT-1 data products", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1996-12-15T00:00:00.000Z", "2008-10-13T23:59:59.999Z"]]}}, "instruments": ["SAR"], "keywords": ["50-\u2013-500-km", "783-821-km", "biosphere", "c-band-(5.2---7.7)-cm", "coastal-processes", "cryosphere", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "high-resolution---hr-(5---20)-m", "imaging-radars", "l1", "l2", "land-surface", "medium-resolution---mr-(20---500)-m", "oceans", "radarsat-1", "radarsat1esaarchive10", "sar", "sar-el-sgf", "sar-fx-sgf", "sar-fx-slc", "sar-sn-scn", "sar-sw-scw", "sar-sx-sgf", "sar-sx-slc", "sar-sx-ssg", "sar-wx-sgf", "sar-wx-slc", "sea-ice", "sun-synchronous", "terrestrial-hydrosphere", "vegetation"], "license": "other", "platform": "RADARSAT-1", "processing:level": "L1,L2", "title": "RADARSAT-1 ESA Archive"}, "Radarsat-2": {"description": "The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. BEAM MODE: Standard PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 or 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 9.0 or 13.5 x 7.7 (SLC), 26.8 - 17.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 100 x 100 Range of Angle of Incidence (deg): 20 - 52 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. BEAM MODE: Wide PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 10 x 10 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 40.0 - 19.2 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 150 x 150 Range of Angle of Incidence (deg): 20 - 45 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: Fine PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 4.7 x 5.1 (SLC), 3.13 x 3.13 (SGX), 6.25 x 6.25 (SSG, SPG) Resolution - Range x Azimuth (m): 5.2 x 7.7 (SLC), 10.4 - 6.8 x 7.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 50 x 50 Range of Angle of Incidence (deg): 30 - 50 No. of Looks - Range x Azimuth: 1 x 1 (SLC,SGX, SGF, SSG, SPG) Polarisations - Options: \u2022 Single: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. BEAM MODE: Extended Low PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 x 5.1 (SLC), 10.0 x 10.0 (SGX), 12.5 x 12.5 (SSG, SPG) Nominal Resolution - Range x Azimuth (m): 9.0 x 7.7 (SLC), 52.7 - 23.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 170 x 170 Range of Angle of Incidence (deg): 10 - 23 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. BEAM MODE: Extended High PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 18.2 - 15.9 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 75 x 75 Range of Angle of Incidence (deg): 49 - 60 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: ScanSAR Narrow PRODUCT: SCN, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 25 x 25 Nominal Resolution - Range x Azimuth (m):81-38 x 40-70 Nominal Scene Size - Range x Azimuth (km): 300 x 300 Range of Angle of Incidence (deg): 20 - 46 No. of Looks - Range x Azimuth: 2 x 2 Polarisations - Options: \u2022 Single Co or Cross: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. Polarisation can be single and dual. BEAM MODE: ScanSAR Wide PRODUCT: SCW, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 50 x 50 Resolution - Range x Azimuth (m): 163.0 - 73 x 78-106 Nominal Scene Size - Range x Azimuth (km): 500 x 500 Range of Angle of Incidence (deg): 20 - 49 No. of Looks - Range x Azimuth: 4 x 2 Polarisations - Options: \u2022 Single Co or Cross: HH or VV or HV or VH \u2022 Dual: HH + HV or VV + VH These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2008-07-27T00:00:00.000Z", "2021-04-11T23:59:59.999Z"]]}}, "instruments": ["SAR"], "keywords": ["100-km-for-standard", "170-km-for-extended-low-incidence", "300-km-for-scansar-narrow", "50-km-for-fine-resolution", "50-km-for-wide-swath", "500-km-for-scansar-wide", "75-km-for-extended-high-incidence", "798-km", "biosphere", "c-band-(5.2---7.7)-cm", "coastal-processes", "cryosphere", "earth-science->-biosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-land-surface", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere", "imaging-radars", "land-surface", "medium-resolution---mr-(20---500)-m", "oceans", "radarsat-1", "radarsat-2", "sar", "sea-ice", "sun-synchronous", "terrestrial-hydrosphere", "vegetation", "very-high-resolution---vhr-(0---5)-m"], "license": "other", "platform": "RADARSAT-1", "title": "RADARSAT-2 ESA Archive"}, "RapidEye.ESA.archive": {"description": "The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products.", "extent": {"spatial": {"bbox": [[-180, -84, 180, 84]]}, "temporal": {"interval": [["2009-02-22T00:00:00.000Z", null]]}}, "instruments": ["MSI"], "keywords": ["630-km", "77-km", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "land-surface", "mapping-and-cartography", "msi", "msi-img-1b", "msi-img-3a", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "rapideye", "rapideye.esa.archive", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "RapidEye", "title": "RapidEye ESA archive"}, "RapidEye.South.America": {"description": "ESA, in collaboration with BlackBridge, has collected this RapidEye dataset of level 3A tiles covering more than 6 million km2 of South American countries: Paraguay, Ecuador, Chile, Bolivia, Peru, Uruguay and Argentina. The area is fully covered with low cloud coverage", "extent": {"spatial": {"bbox": [[-81, -41, 54, 1]]}, "temporal": {"interval": [["2012-07-12T00:00:00.000Z", "2015-12-13T23:59:59.999Z"]]}}, "instruments": ["MSI"], "keywords": ["630-km", "77-km", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "imaging-spectrometers/radiometers", "land-surface", "mapping-and-cartography", "msi", "msi-img-3a", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "rapideye", "rapideye.south.america", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "RapidEye", "title": "RapidEye South America"}, "RapidEye.time.series.for.Sentinel-2": {"description": "The European Space Agency, in collaboration with BlackBridge collected 2 time series datasets with a 5 day revisit at high resolution: \u2022 February to June 2013 over 14 selected sites around the world \u2022 April to September 2015 over 10 selected sites around the world The RapidEye Earth Imaging System provides data at 5 m spatial resolution (multispectral L3A orthorectified). The products are radiometrically and sensor corrected similar to the 1B Basic product, but have geometric corrections applied to the data during orthorectification using DEMs and GCPs. The product accuracy depends on the quality of the ground control and DEMs used. The imagery is delivered in GeoTIFF format with a pixel spacing of 5 metres. The dataset is composed of data over: \u2022 14 selected sites in 2013: Argentina, Belgium, Chesapeake Bay, China, Congo, Egypt, Ethiopia, Gabon, Jordan, Korea, Morocco, Paraguay, South Africa and Ukraine. \u2022 10 selected sites in 2015: Limburgerhof, Railroad Valley, Libya4, Algeria4, Figueres, Libya1, Mauritania1, Barrax, Esrin, Uyuni Salt Lake.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2013-02-06T00:00:00.000Z", "2015-08-15T23:59:59.999Z"]]}}, "instruments": ["MSI"], "keywords": ["630-km", "77-km", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "land-surface", "mapping-and-cartography", "msi", "msi-img-3a", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "rapideye", "rapideye.time.series.for.sentinel-2", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "RapidEye", "title": "RapidEye time series for Sentinel-2"}, "SAR_IMM_1P": {"description": "This ERS Medium Resolution strip-line product is generated from the Image Mode Level 0 Product. Strip-line image products contain image data for an entire segment, up to a maximum size of 10 minutes per product for IM mode. The processor concatenates together several sub-images called &quot;slices&quot; that were processed separately on a dataset-by-dataset basis in order to form the entire strip-line image. The product is processed to an approximately 150 m x 150 m resolution and has a radiometric resolution that is good enough for ice applications. This product has a lower spatial resolution than the SAR_IMP_1P and SAR_IMS_1P products. The SAR IM L0 full mission data archive has been bulk processed to Level 1 (SAR_IMM_1P) in Envisat format with the PF-ERS processor version 6.01. Product Characteristics: - Pixel size: 5 m (ground range \u2013 across track) x 75 m (azimuth \u2013 along track) - Scene area: 100 km (range) x at least 102.5 km - Scene Size: 1300 pixels (range) x at least 1350 lines (azimuth) - Pixel depth: 16 bits unsigned integer - Total product volume: at least 3.5 MB - Projection: Ground-range - Number of looks: 8 (azimuth) x 7 (range)", "extent": {"spatial": {"bbox": [[-180, -82, 180, 82]]}, "temporal": {"interval": [["1991-07-27T00:00:00.000Z", "2011-07-04T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR"], "keywords": ["5-km", "782-to-785-km", "ami/sar", "cryosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere->-snow/ice", "ers-1", "ers-2", "imaging-radars", "land-surface", "pf-ers-/-envisat-format", "sar-imm-1p", "sea-ice", "snow-and-ice", "sun-synchronous", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS-1/2 SAR IM Medium Resolution L1 [SAR_IMM_1P]"}, "SAR_IMP_1P": {"description": "The SAR Precision product is a multi-look (speckle-reduced), ground range image acquired in Image Mode. This product type is most applicable to users interested in remote sensing applications, but is also suitable for calibration purposes. The products are calibrated and corrected for the SAR antenna pattern and range-spreading loss. Radar backscatter can be derived from the products for geophysical modelling, but no correction is applied for terrain-induced radiometric effects. The images are not geocoded, and terrain distortion (foreshortening and layover) has not been removed. The numbering sequence relates to the satellite position and therefore differs between Ascending and Descending scenes. Product characteristics: - Pixel size: 12.5 m (range - across track) x 12.5 m (azimuth - along track) - Scene area: 100 km (range) x at least 102.5 km (azimuth) - Scene size: 8000 pixels range x at least 8200 lines (azimuth) - Pixel depth: 16 bits unsigned integer - Total product volume: 125 MBs - Projection: Ground-range - Number of looks: 3", "extent": {"spatial": {"bbox": [[-180, -82, 180, 82]]}, "temporal": {"interval": [["1991-07-27T00:00:00.000Z", "2011-07-04T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR"], "keywords": ["5-km", "782-to-785-km", "ami/sar", "cryosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere->-snow/ice", "ers-1", "ers-2", "imaging-radars", "land-surface", "sar-imp-1p", "sea-ice", "snow-and-ice", "sun-synchronous", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS-1/2 SAR IM Precision L1 [SAR_IMP_1P]"}, "SAR_IMS_1P": {"description": "The SAR SLC product is a single look complex acquired in Image Mode. It is a digital image, with slant range and phase preserved, generated from raw SAR data using up-to-date auxiliary parameters. The products are intended for use in SAR quality assessment, calibration and interferometric applications. A minimum number of corrections and interpolations are performed on the data. Absolute calibration parameters (when available) are provided in the product annotation. Product characteristics: - Pixel size: 8 m (range - across track) x 4 m (azimuth - along track \u2013 varying slightly depending on acquisition Pulse Repetition Frequency) - Scene area: 100 km (range) x at least 102.5 km (azimuth) - Scene size: 5000 samples (range) x at least 30000 lines (azimuth) - Pixel depth: 32 bits signed integer (16 bits I, 16 bits Q) - Total product volume: 575 MB - Projection: Slant range - Number of looks: 1", "extent": {"spatial": {"bbox": [[-180, -82, 180, 82]]}, "temporal": {"interval": [["1991-07-27T00:00:00.000Z", "2011-07-04T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR"], "keywords": ["5-km", "782-to-785-km", "ami/sar", "cryosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere->-snow/ice", "ers-1", "ers-2", "imaging-radars", "land-surface", "sar-ims-1p", "sea-ice", "snow-and-ice", "sun-synchronous", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS-1/2 SAR IM Single Look Complex L1 [SAR_IMS_1P]"}, "SAR_IM_0P": {"description": "This SAR Level 0 product is acquired in Image Mode. The products consist of the SAR telemetry data and are supplied as standard scenes. It also contains all the required auxiliary data necessary for data processing. The product serves two main purposes: For testing ERS SAR processors independently from the HDDR system For users interested in full SAR data processing. Product characteristics: - Scene area: 100 km (range - across track) x full segment length (azimuth - along track) - Scene size: 5616 samples (range) x full segment length (azimuth) - Pixel depth: 10 bits signed integer (5 bits I, 5 bits Q) - Projection: Slant range", "extent": {"spatial": {"bbox": [[-180, -82, 180, 82]]}, "temporal": {"interval": [["1991-07-27T00:00:00.000Z", "2011-07-04T23:59:59.999Z"]]}}, "instruments": ["AMI/SAR", "AMI/SAR"], "keywords": ["5-km", "782-to-785-km", "ami/sar", "cryosphere", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-cryosphere->-snow/ice", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "earth-science->-oceans->-sea-ice", "earth-science->-terrestrial-hydrosphere->-snow/ice", "ers-1", "ers-2", "imaging-radars", "land-surface", "sar-im-0p", "sea-ice", "snow-and-ice", "sun-synchronous", "topography", "vegetation"], "license": "other", "platform": "ERS-1,ERS-2", "title": "ERS-1/2 SAR IM L0 [SAR_IM__0P]"}, "SCIAMACHYLevel2LimbOzone": {"description": "This Envisat SCIAMACHY Ozone stratospheric profiles dataset has been extracted from the previous baseline (v6.01) of the SCIAMACHY Level 2 data. The dataset is generated in the framework of the full mission reprocessing campaign completed in 2023 under the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ .\r\nFor optimal results, users are strongly encouraged to make use of these specific ozone limb profiles rather than the ones contained in the _$$SCIAMACHY Level 2 dataset version 7.1$$ https://earth.esa.int/eogateway/catalog/envisat-sciamachy-total-column-densities-and-stratospheric-profiles-sci_ol__2p- .\r\n\r\nThe new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. \r\nPanoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions .\r\n\r\nPlease refer to the _$$README$$ https://earth.esa.int/eogateway/documents/20142/37627/ENVI-GSOP-EOGD-QD-16-0132.pdf file (L2 v6.01) for essential guidance before using the data.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-08-02T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["SCIAMACHY"], "keywords": ["5---1150-km", "atmosphere", "atmospheric-chemistry", "atmospheric-indicators", "atmospheric-radiation", "atmospheric-temperature", "climate", "earth-science->-atmosphere", "earth-science->-atmosphere->-atmospheric-chemistry", "earth-science->-atmosphere->-atmospheric-radiation", "earth-science->-atmosphere->-atmospheric-temperature", "earth-science->-climate-indicators", "earth-science->-oceans", "envisat", "oceans", "sciamachy", "sciamachylevel2limbozone", "spectrometers", "sun-synchronous"], "license": "other", "platform": "Envisat", "title": "Envisat SCIAMACHY Level 2 - Limb Ozone [SCI_LIMBO3]"}, "SMOS_Open_V7": {"description": "Level 1 SMOS data products are designed for scientific and operational users who need to work with calibrated MIRAS instrument measurements, while Level 2 SMOS data products are designed for scientific and operational users who need to work with geo-located soil moisture and sea surface salinity estimation as retrieved from Level 1 dataset. Products from the operational pipeline in the SMOS Data Processing Ground Segment (DPGS) https://earth.esa.int/eogateway/missions/smos/description, located at the European Space Astronomy Centre (ESAC), have File Class \"\"OPER\"\", while reprocessed data is tagged as \"\"REPR\"\". For an optimal exploitation of the current SMOS L1 and L2 data set please consult the read-me-first notes. The Level 1A product comprises all calibrated visibilities between receivers (i.e. the interferometric measurements from the sensor including the redundant visibilities), combined per integration time of 1.2s (snapshot). The snapshots are consolidated in a pole-to-pole product file (50 minutes of sensing time) with a maximum size of about 215MB per half orbit (29 half orbits per day). The Level 1B product comprises the result of the image reconstruction algorithm applied to the L1A data. As a result, the reconstructed image at L1B is simply the difference between the sensed scene by the sensor and the artificial scene. The brightness temperature image is available in its Fourier component in the antenna polarisation reference frame top of the atmosphere. Images are combined per integration time of 1.2 seconds (snapshot). The removal of foreign sources (Galactic, Direct Sun, Moon) is also included in the reconstruction. Snapshot consolidation is as per L1A, with a maximum product size of about 115MB per half orbit. ESA provides the Artificial Scene Library (ASL) to add the artificial scene in L1B for any user that wants to start from L1B products and derive the sensed scene. The Level 1C product contains multi-angular brightness temperatures in antenna frame (X-pol, Y-pol, T3 and T4) at the top of the atmosphere, geo-located in an equal-area grid system (ISEA 4H9 - Icosahedral Snyder Equal Area projection). Two L1C products are available: Land for soil moisture retrieval and Sea for sea surface salinity retrieval. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 350MB per half orbit (29 half orbits per day). Spatial resolution is in the range of 30-50 km. For each L1C product there is also a corresponding Browse product containing brightness temperatures interpolated for an incidence angle of 42.5\u00b0. The Level 2 Soil Moisture (SM) product comprises soil moisture measurements geo-located in an equal-area grid system ISEA 4H9. The product contains not only the retrieved soil moisture, but also a series of ancillary data derived from the processing (nadir optical thickness, surface temperature, roughness parameter, dielectric constant and brightness temperature retrieved at top of atmosphere and on the surface) with the corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 7MB (25MB uncompressed data) per half orbit (29 half orbits per day). The Level 2 Ocean Salinity (OS) product comprises sea surface salinity measurements geo-located in an equal-area grid system ISEA 4H9. The product contains one single swath-based sea surface salinity retrieved with and without Land-Sea contamination correction, SSS anomaly based on WOA-2009 referred to Land-Sea corrected sea surface salinity, brightness temperature at the top of the atmosphere and at the sea surface with their corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 10MB (25MB uncompressed data) per half orbit (29 half orbits per day). The following Science data products, belonging to the latest processing baseline, are openly available to all users: MIR_SC_F1B/MIR_SC_D1B: Level 1B product, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SCLF1C/MIR_SCLD1C: Level 1C product over Land, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SCSF1C/MIR_SCSD1C: Level 1C product over Sea, FULL/DUAL polarisation mode, in Earth Explorer format MIR_BWLF1C/MIR_BWLD1C: Level 1C Browse product over Land, FULL/DUAL polarisation mode, in Earth Explorer format MIR_BWSF1C/MIR_BWSD1C: Level 1C Browse product over Sea, FULL/DUAL polarisation mode, in Earth Explorer format MIR_SMUDP2: Level 2 Soil Moisture product, in Earth Explorer and NetCDF format MIR_OSUDP2: Level 2 Sea Surface Salinity product, in Earth Explorer and NetCDF format Access to the following Science data products is restricted to SMOS CalVal users: MIR_SC_F1A/MIR_SC_D1A: Level 1A product, FULL/DUAL polarisation mode, in Earth Explorer format. For an optimal exploitation of the current SMOS L1 and L2 data set please consult the read-me-first notes available in the Resources section below.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2010-01-12T00:00:00.000Z", null]]}}, "instruments": ["MIRAS"], "keywords": ["1000-km", "758-km", "7xx", "earth-science->-agriculture->-soils", "earth-science->-agriculture->-soils->-soil-moisture/water-content", "earth-science->-land-surface", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-salinity/density", "interferometric-radiometers", "l-band-(19.4---76.9)-cm", "land-surface", "mir-bwlf1c/mir-bwld1c", "mir-bwsf1c/mir-bwsd1c", "mir-osudp2", "mir-sc-f1b/mir-sc-d1b", "mir-sclf1c/mir-scld1c", "mir-scsf1c/mir-scsd1c", "mir-smudp2", "miras", "oceans", "salinity-and-density", "smos", "smos-open-v7", "soil-moisture", "soils", "sun-synchronous"], "license": "other", "platform": "SMOS", "title": "SMOS L1 and L2 Science data"}, "SPOT-6.and.7.ESA.archive": {"description": "The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products.\r\n\r\nSPOT 6 and 7 Primary and Ortho products can be available in the following modes:\r\n\r\nPanchromatic image at 1.5m resolution\r\nPansharpened colour image at 1.5m resolution\r\nMultispectral image in 4 spectral bands at 6m resolution\r\nBundle (1.5m panchromatic image + 6m multispectral image)\r\nSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. \r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2012-10-01T00:00:00.000Z", null]]}}, "instruments": ["NAOMI", "NAOMI"], "keywords": ["60-km", "694-km", "afforestation/reforestation", "agriculture", "atmosphere", "cameras", "crops-and-yields", "earth-science->-agriculture", "earth-science->-agriculture->-agricultural-plant-science->-crop/plant-yields", "earth-science->-agriculture->-forest-science->-afforestation/reforestation", "earth-science->-agriculture->-forest-science->-forest-fire-science", "earth-science->-atmosphere", "earth-science->-atmosphere->-weather-events", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "forest-fires", "high-resolution---hr-(5---20)-m", "land-surface", "nao-ms--1a", "nao-ms--3-", "nao-p---1a", "nao-p---3-", "nao-p-s-1a", "nao-p-s-2-", "nao-p-s-3-", "nao-pms-1a", "nao-pms-2-", "nao-pms-3-", "naomi", "nir-(0.75---1.30)-\u00b5m", "spot-6", "spot-6.and.7.esa.archive", "spot-7", "sun-synchronous", "topography", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m", "weather-events"], "license": "other", "platform": "SPOT 6,SPOT 7", "title": "SPOT-6 and 7 ESA archive"}, "SPOT1-5": {"description": "The ESA SPOT1-5 collection is a dataset of SPOT-1 to 5 Panchromatic and Multispectral products that ESA collected over the years. The HRV(IR) sensor onboard SPOT 1-4 provides data at 10 m spatial resolution Panchromatic mode (-1 band) and 20 m (Multispectral mode -3 or 4 bands). The HRG sensor on board of SPOT-5 provides spatial resolution of the imagery to < 3 m in the panchromatic band and to 10 m in the multispectral mode (3 bands). The SWIR band imagery remains at 20 m. The dataset mainly focuses on European and African sites but some American, Asian and Greenland areas are also covered.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1986-04-01T00:00:00.000Z", "2015-09-15T23:59:59.999Z"]]}}, "instruments": ["HRV", "HRV", "HRV", "HRVIR", "HRG"], "keywords": ["60-km", "832-km", "afforestation/reforestation", "agriculture", "atmosphere", "cameras", "crops-and-yields", "earth-science->-agriculture", "earth-science->-agriculture->-agricultural-plant-science->-crop/plant-yields", "earth-science->-agriculture->-forest-science->-afforestation/reforestation", "earth-science->-agriculture->-forest-science->-forest-fire-science", "earth-science->-atmosphere", "earth-science->-atmosphere->-weather-events", "earth-science->-land-surface", "earth-science->-land-surface->-topography", "forest-fires", "high-resolution---hr-(5---20)-m", "hrg", "hrg--a---3", "hrg--a--1a", "hrg--a--1b", "hrg--a--2a", "hrg--b--2a", "hrg--j---3", "hrg--j--1a", "hrg--j--1b", "hrg--j--2a", "hrg--ps--3", "hrg--ps-1a", "hrg--ps-1b", "hrg--ps-2a", "hri--i---3", "hri--i--1a", "hri--i--1b", "hri--i--2a", "hri--m---3", "hri--m--1a", "hri--m--1b", "hri--m--2a", "hri--x---3", "hri--x--1a", "hri--x--2a", "hrs", "hrv", "hrv--p---3", "hrv--p--1a", "hrv--p--1b", "hrv--p--2a", "hrv--x---3", "hrv--x--1a", "hrv--x--1b", "hrv--x--2a", "hrvir", "imaging-spectrometers/radiometers", "land-surface", "nir-(0.75---1.30)-\u00b5m", "spot-1", "spot-2", "spot-3", "spot-4", "spot-5", "spot1-5", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "topography", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m", "weather-events"], "license": "other", "platform": "SPOT 1,SPOT 2,SPOT 3,SPOT 4,SPOT 5", "title": "SPOT1-5 ESA archive"}, "SPOT4-5_Take5.ESAarchive": {"description": "At the end of SPOT-4 life, the Take5 experiment was launched and the satellite was moved to a lower orbit to obtain a 5 day repeat cycle, same repetition of Sentinel-2. Thanks to this orbit, from 1st of Feb to 19th of June 2013 a time series of images acquired every 5 days with constant angle and over 45 different sites were observed. In analogy to the previous SPOT-4 Take-5 experiment, also SPOT-5 was placed in a 5 days cycle orbit and 145 selected sites were acquired every 5 days under constant angles from 8th of April to 31st of August 2015. With a resolution of 10 m, the following processing levels are available: Level 1A: reflectance at the top of atmosphere (TOA), not orthorectified products Level 1C: data orthorectified reflectance at the top of atmosphere (TOA) Level 2A: data orthorectified surface reflectance after atmospheric correction (BOA), along with clouds mask and their shadow, and mask of water and snow.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2013-01-31T00:00:00.000Z", "2015-09-15T23:59:59.999Z"]]}}, "instruments": ["HRVIR", "HRS"], "keywords": ["60-km", "832-km", "afforestation/reforestation", "agriculture", "cameras", "crops-and-yields", "earth-science->-agriculture", "earth-science->-agriculture->-agricultural-plant-science->-crop/plant-yields", "earth-science->-agriculture->-forest-science->-afforestation/reforestation", "earth-science->-agriculture->-forest-science->-forest-fire-science", "earth-science->-land-surface", "forest-fires", "hrg-xs--1a", "hrg-xs--1c", "hrg-xs--2a", "hri-xs--1c", "hri-xs--2a", "hrs", "hrvir", "imaging-spectrometers/radiometers", "land-surface", "nir-(0.75---1.30)-\u00b5m", "spot-4", "spot-5", "spot4-5-take5.esaarchive", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "SPOT 4,SPOT 5", "title": "SPOT 4-5 Take5 ESA archive"}, "SeaSat.ESA.archive": {"description": "This collection gives access to the complete SEASAT dataset acquired by ESA and mainly covers Europe. The dataset comprises some of the first ever SAR data recorded for scientific purposes, reprocessed with the most recent processor. The Level-1 products are available as: \u2022\tSAR Ellipsoid Geocoded Precision Image \u2022\tSAR Precision Image \u2022\tSAR Single Look Complex Image European Space Agency, Seasat SAR Precision Image. Version 1.0. https://doi.org/10.5270/SE1-99j66hv European Space Agency, Seasat SAR Single Look Complex. Version 1.0. https://doi.org/10.5270/SE1-4uij92n European Space Agency, Seasat SAR Ellipsoid Geocoded Precision Image . Version 1.0. https://doi.org/10.5270/SE1-ungwqxv", "extent": {"spatial": {"bbox": [[-125, -10, 20, 70]]}, "temporal": {"interval": [["1978-07-13T00:00:00.000Z", "1978-10-10T23:59:59.999Z"]]}}, "instruments": ["SAR"], "keywords": ["1.3.6", "100-km", "800-km", "atmospheric-water-vapour", "earth-science->-atmosphere->-atmospheric-water-vapor", "earth-science->-cryosphere->-sea-ice", "earth-science->-land-surface->-geomorphic-landforms/processes->-tectonic-processes", "earth-science->-land-surface->-topography", "earth-science->-oceans", "earth-science->-oceans->-ocean-circulation", "earth-science->-oceans->-ocean-waves", "earth-science->-oceans->-sea-ice", "earth-science->-oceans->-sea-surface-topography", "earth-science->-solid-earth->-geomorphic-landforms/processes", "geomorphic-landforms-and-processes", "imaging-radars", "inclined", "l-band-(19.4---76.9)-cm", "medium-resolution---mr-(20---500)-m", "non-sun-synchronous", "ocean-circulation", "ocean-waves", "oceans", "sar", "sea-gec-1p", "sea-ice", "sea-pri-1p", "sea-slc-1p", "sea-surface-topography", "seasat", "seasat.esa.archive", "topography"], "license": "other", "platform": "Seasat", "title": "SeaSat ESA archive"}, "SkySatESAarchive": {"description": "The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products.\r\n\r\nTwo different product types are offered, Ground Sampling Distance at nadir up to 65 cm for panchromatic and up to 0.8m for multi-spectral.\r\n\r\nEO-SIP Product Type\tProduct Description\tContent\r\nSSC_DEF_SC\tBasic and Ortho scene\t\r\nLevel 1B 4-bands Analytic /DN Basic scene\r\nLevel 1B 4-bands Panchromatic /DN Basic scene\r\nLevel 1A 1-band Panchromatic DN Pre Sup resolution Basic scene\r\nLevel 3B 3-bands Visual Ortho Scene\r\nLevel 3B 4-bands Pansharpened Multispectral Ortho Scene\r\nLevel 3B 4-bands Analytic/DN/SR Ortho Scene\r\nLevel 3B 1-band Panchromatic /DN Ortho Scene\r\nSSC_DEF_CO\tOrtho Collect\t\r\nVisual 3-band Pansharpened Image\r\nMultispectral 4-band Pansharpened Image\r\nMultispectral 4-band Analytic/DN/SR Image (B, G, R, N)\r\n1-band Panchromatic Image\r\n \r\n\r\nThe Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process:\r\nAnalytic - unorthorectified, radiometrically corrected, multispectral BGRN\r\nAnalytic DN - unorthorectified, multispectral BGRN\r\nPanchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN)\r\nPanchromatic DN - unorthorectified, panchromatic (PAN)\r\nL1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN)\r\nThe Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection:\r\nVisual - orthorectified, pansharpened, and colour-corrected (using a colour curve) 3-band RGB Imagery\r\nPansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery\r\nAnalytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product.\r\nAnalytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance.\r\nAnalytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts\r\nPanchromatic - orthorectified, radiometrically correct, panchromatic (PAN)\r\nPanchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product\r\nThe Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes.\r\nSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service.\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -84, 180, 84]]}, "temporal": {"interval": [["2016-02-29T00:00:00.000Z", null]]}}, "instruments": ["SkySat Camera"], "keywords": ["475-575-km", "6.6-km-at-nadir-for-skysat-3-to--13", "8-km-at-nadir-for-skysat-1-to--2", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "imaging-spectrometers/radiometers", "land-surface", "mapping-and-cartography", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "skysat", "skysat-camera", "skysatesaarchive", "ssc-def-co", "ssc-def-sc", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "SkySat", "title": "Skysat ESA archive"}, "TDPforAtmosphere": {"description": "This is the Atmospheric Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing Total Column Water Vapour (TCWV), Cloud Liquid Water Path (LWP), Atmospheric Attenuation of the altimeter backscattering coefficient at Ku-band (AttKu), and Wet Tropospheric Correction (WTC), retrieved from observations of the Microwave Radiometer (MWR) instruments flown on-board the ERS-1, and ERS-2, and Envisat satellites.\r\n\r\nCompared to existing datasets, the Atmospheric TDP demonstrates notable improvements in several aspects:\r\n\r\nImproved temporal coverage, especially for ERS-2\r\nImproved L0 -&gt; 1 processing\r\nTwo different corrections are provided based on a neural network retrieval or on a 1D-VAR approach\r\n\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "atmosphere", "earth-science->-atmosphere", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "ra", "ra-2", "radar-altimeters", "sun-synchronous", "tdpforatmosphere"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Atmospheric Thematic Data Product [MWR_TDPATM]"}, "TDPforInlandWater": {"description": "This is the Inland Waters Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing improved Water Surface Height (WSH) data record from the ERS-1, ERS-2 and Envisat missions estimated using the ICE1 retracking range for its better performance on the hydro targets.\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-04T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "earth-science->-terrestrial-hydrosphere", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "ra", "ra-2", "radar-altimeters", "sun-synchronous", "tdpforinlandwater", "terrestrial-hydrosphere"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Inland Waters Thematic Data Product [ALT_TDP_IW]"}, "TDPforLandice": {"description": "This is the Land Ice Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing estimates of ice sheet surface elevation and associated uncertainties.\r\nThe collection covers data for three different missions: ERS-1, ERS-2 and Envisat, and based on Level 1 data coming from previous reprocessing (ERS REAPER and the Envisat V3.0) but taking into account the improvements made at Level 0/Level 1 in the frame of FDR4ALT (_$$ALT FDR$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry).\r\nThe Land Ice TDP focuses specifically on the ice sheets of Greenland and Antarctica, providing these data in different files.\r\nFor many aspects, the Land Ice Level 2 and Level 2+ processing is very innovative:\r\n\r\nImproved relocation approach correcting for topographic effects within the beam footprint to identify the Point of Closest Approach \r\nHomogeneous timeseries of surface elevation measurements at regular along-track reference nodes.\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-03-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-snow/ice", "earth-science->-terrestrial-hydrosphere", "earth-science->-terrestrial-hydrosphere->-snow/ice", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "ra", "ra-2", "radar-altimeters", "snow-and-ice", "sun-synchronous", "tdpforlandice", "terrestrial-hydrosphere"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Land Ice Thematic Data Product [ALT_TDP_LI]"}, "TDPforOceanCoastalTopography": {"description": "This is the Ocean and Coastal Topography Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing improved sea surface height anomaly data records both at 1 Hz and 20 Hz resolution to address climate and/or coastal areas studies. The collection covers data for the ERS-1, ERS-2 and Envisat missions. Note that a dedicated processing to coastal zones has been applied for coastal distances below 200 km.\r\nCompared to existing datasets, the Ocean and Coastal Topography TDP demonstrates notable improvements in several aspects:\r\n\r\nUp-to-date orbit and geophysical corrections applied\r\nAdaptive retracker for Envisat\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "earth-science->-oceans", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "oceans", "ra", "ra-2", "radar-altimeters", "sun-synchronous", "tdpforoceancoastaltopography"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Ocean and Coastal Topography Thematic Data Product [ALT_TDP_OC]"}, "TDPforOceanWaves": {"description": "This is the Ocean Waves Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing Significant Wave Height estimates for the ERS-1, ERS-2 and Envisat missions.\r\nCompared to existing datasets, the Ocean Waves TDP demonstrates notable improvements in several aspects:\r\n\r\nGreat improvements for Envisat due to noise reduction from Adaptive retracker and High-Frequency Adjustment (HFA)\r\nAll variables are given at 5 Hz\r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA", "RA", "RA-2"], "keywords": ["5---1150-km", "800-km", "earth-science->-oceans", "earth-science->-oceans->-ocean-waves", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "ocean-waves", "oceans", "ra", "ra-2", "radar-altimeters", "sun-synchronous", "tdpforoceanwaves"], "license": "other", "platform": "ERS-1,ERS-2,Envisat", "title": "Ocean Waves Thematic Data Product [ALT_TDP_WA]"}, "TDPforSeaice": {"description": "This is the Sea Ice Thematic Data Product (TDP) V1 resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ and containing the sea ice related geophysical parameters, along with associated uncertainties: snow depth, radar and sea-ice freeboard, sea ice thickness and concentration.\r\nThe collection covers data for the ERS-1, ERS-2 and Envisat missions, and bases on Level 1 data coming from previous reprocessing (ERS REAPER and the Envisat V3.0) but taking into account the improvements made at Level 0/Level 1 in the frame of FDR4ALT (_$$ALT FDR$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry).\r\nThe Sea Ice TDP provides data from the northern or southern hemisphere in two files corresponding to the Arctic and Antarctic regions respectively for the winter periods only, i.e., October to June for the Arctic, and May to November for the Antarctic.\r\nFor many aspects, the Sea Ice TDP is very innovative: \r\n\r\nFirst time series of sea-ice thickness estimates for ERS\r\nHomogeneous calibration, allowing the first Arctic radar freeboard time series from ERS-1 (1991) to CryoSat-2 (2021)\r\nUncertainties estimated along-track with a bottom-up approach based on dominant sources \r\nERS pulse blurring error corrected using literature procedure [Peacock, 2004] \r\nThe FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used.\r\nInformation for expert altimetry users is also available in a dedicated NetCDF group within the products.\r\nPlease consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data.\r\nThe FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1991-08-03T00:00:00.000Z", "2012-04-08T23:59:59.999Z"]]}}, "instruments": ["RA-2", "RA", "RA"], "keywords": ["5---1150-km", "800-km", "cryosphere", "earth-science->-cryosphere", "earth-science->-cryosphere->-sea-ice", "earth-science->-oceans", "earth-science->-oceans->-sea-ice", "envisat", "ers-1", "ers-2", "imaging-spectrometers/radiometers", "mwr", "oceans", "ra", "ra-2", "radar-altimeters", "sea-ice", "sun-synchronous", "tdpforseaice"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "Sea Ice Thematic Data Product [ALT_TDP_SI]"}, "TerraSAR-X": {"description": "The TerraSAR-X ESA archive collection consists of TerraSAR-X and TanDEM-X products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. TerraSAR-X/TanDEM-X Image Products can be acquired in 6 image modes with flexible resolutions (from 0.25m to 40m) and scene sizes. Thanks to different polarimetric combinations and processing levels the delivered imagery can be tailored specifically to meet the requirements of the application. The following list delineates the characteristics of the SAR imaging modes that are disseminated under ESA Third Party Missions (TPM). \u2022 StripMap (SM): Resolution 3 m, Scene size 30x50 km2 (up to 30x1650 km2) \u2022 SpotLight (SL): Resolution 2 m, Scene size 10x10 km2 \u2022 Staring SpotLight (ST): Resolution 0.25m, Scene size 4x3.7 km2 \u2022 High Resolution SpotLight (HS): Resolution 1 m, Scene size 10x5 km2 \u2022 ScanSAR (SC): Resolution 18 m, Scene size 100x150 km2 (up to 100x1650 km2) \u2022 Wide ScanSAR (WS): Resolution 40 m, Scene size 270x200 km2 (up to 270x1500 km2) The following list briefly delineates the available processing levels for the TerraSAR-X dataset: \u2022 SSC (Single Look Slant Range Complex) in DLR-defined COSAR binary format \u2022 MGD (Multi Look Ground Range Detected) in GeoTiff format \u2022 GEC (Geocoded Ellipsoid Corrected) in GeoTiff format \u2022 EEC (Enhanced Ellipsoid Corrected in GeoTiff format", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2007-07-01T00:00:00.000Z", null]]}}, "instruments": ["TSX-1"], "keywords": ["10-km-for-spotlight", "100-km-for-scansar", "30-km-for-stripmap", "514-km", "coastal-processes", "earth-science->-agriculture->-soils", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface->-geomorphic-landforms/processes->-coastal-processes", "earth-science->-land-surface->-soils", "earth-science->-oceans", "earth-science->-oceans->-marine-environment-monitoring->-marine-obstructions", "high-resolution---hr-(5---20)-m", "imaging-radars", "l1", "natural-hazards-and-disaster-risk", "oceans", "sar-hs-eec", "sar-hs-gec", "sar-hs-mgd", "sar-hs-ssc", "sar-sc-eec", "sar-sc-gec", "sar-sc-mgd", "sar-sc-ssc", "sar-sl-eec", "sar-sl-gec", "sar-sl-mgd", "sar-sl-ssc", "sar-sm-eec", "sar-sm-gec", "sar-sm-mgd", "sar-sm-ssc", "sar-st-eec", "sar-st-gec", "sar-st-mgd", "sar-st-ssc", "sar-ws-eec", "sar-ws-gec", "sar-ws-mgd", "sar-ws-ssc", "soils", "sun-synchronous", "terrasar-x", "tsx-1", "vegetation", "very-high-resolution---vhr-(0---5)-m", "x-band-(2.8---5.2)-cm"], "license": "other", "platform": "TerraSAR-X", "processing:level": "L1", "title": "TerraSAR-X ESA archive"}, "TropForest": {"description": "The objective of the ESA TropForest project was to create a harmonised geo-database of ready-to-use satellite imagery to support 2010 global forest assessment performed by the Joint Research Centre (JRC) of the European Commission and by the Food and Agriculture Organization (FAO). Assessments for year 2010 were essential for building realistic deforestation benchmark rates at global to regional levels. To reach this objective, the project aimed to create a harmonised ortho-rectified/pre-processed imagery geo-database based on satellite data acquisitions (ALOS AVNIR-2, GEOSAT-1 SLIM6, KOMPSAT-2 MSC) performed during year 2009 and 2010, for the Tropical Latin America (excluding Mexico) and for the Tropical South and Southeast Asia (excluding China), resulting in 1971 sites located at 1 deg x 1 deg geographical lat/long intersections. The project finally delivered 1866 sites (94.7% of target) due to cloud coverages too high for missing sites", "extent": {"spatial": {"bbox": [[-100, -50, 160, 40]]}, "temporal": {"interval": [["2009-01-27T00:00:00.000Z", "2011-08-09T23:59:59.999Z"]]}}, "instruments": ["AVNIR-2", "SLIM6", "MSC"], "keywords": ["agriculture", "al1-av2-2f", "alos-1", "avnir-2", "cameras", "de1-625-km;-ko2-15-km;-al1-70-km", "de1-663-km;-ko2-685-km;-al1-692-km", "de1-sl6-2f", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-land-surface", "forestry", "geosat-1", "high-resolution---hr-(5---20)-m", "imaging-spectrometers/radiometers", "ko2-msc-2f", "kompsat-2", "land-surface", "msc", "nir-(0.75---1.30)-\u00b5m", "slim6", "sun-synchronous", "tropforest", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ALOS-1,GEOSAT-1,KOMPSAT-2", "title": "TropForest- ALOS, GEOSAT-1 & KOMPSAT-2 optical coverages over tropical forests"}, "VT_GOCE_Data": {"description": "This collection contains the VT GOCE software and associated data set needed to run the software that is used for GOCE data visualisation.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-09-01T00:00:00.000Z", "2012-07-31T23:59:59.999Z"]]}}, "instruments": ["STR"], "keywords": ["250-km", "accelerometers", "drifting", "earth-science->-solid-earth", "earth-science->-solid-earth->-geodetics", "egg", "geodetics", "goce", "gps", "near-polar", "radar-altimeters", "solid-earth", "ssti", "str", "vt-goce-data"], "license": "other", "platform": "GOCE", "title": "VT GOCE Data"}, "WorldView-2.European.Cities": {"description": "ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015.", "extent": {"spatial": {"bbox": [[-19, -26, 35, 66]]}, "temporal": {"interval": [["2010-07-20T00:00:00.000Z", "2015-07-19T23:59:59.999Z"]]}}, "instruments": ["WV110"], "keywords": ["16.4-km", "770-km", "agriculture", "cameras", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-human-dimensions->-human-settlements", "earth-science->-land-surface", "forestry", "human-settlements", "land-surface", "nir-(0.75---1.30)-\u00b5m", "sun-synchronous", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m", "worldview-2", "worldview-2.european.cities", "wv-110--2a", "wv110"], "license": "other", "platform": "WorldView-2", "title": "WorldView-2 European Cities"}, "WorldView.ESA.archive": {"description": "The WorldView ESA archive is composed of products acquired by WorldView-1, -2, -3 and -4 satellites and requested by ESA supported projects over their areas of interest around the world\r\n\r\nPanchromatic, 4-Bands, 8-Bands and SWIR products are part of the offer, with the resolution at Nadir depicted in the table.\r\n\r\nBand Combination\tMission\tGSD Resolution at Nadir\tGSD Resolution (20\u00b0 off nadir)\r\nPanchromatic\tWV-1\t50 cm\t55 cm\r\nWV-2\t46 cm\t52 cm\r\nWV-3\t31 cm\t34 cm\r\nWV-4\t31 cm\t34 cm\r\n4-Bands\tWV-2\t1.84 m\t2.4 m\r\nWV-3\t1.24 m\t1.38 m\r\nWV-4\t1.24 m\t1.38 m\r\n8-Bands\tWV-2\t1.84 m\t2.4 m\r\nWV-3\t1.24 m\t1.38 m\r\nSWIR\tWV-3\t3.70 m\t4.10 m\r\n\r\nThe 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR). The 8-Bands being an option from Multispectral (COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2) and Bundle (PAN, COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2).\r\nThe processing levels are:\r\n\r\nStandard (2A): normalised for topographic relief\r\nView Ready Standard: ready for orthorectification (RPB files embedded)\r\nView Ready Stereo: collected in-track for stereo viewing and manipulation (not available for SWIR)\r\nMap Scale (Ortho) 1:12,000 Orthorectified: additional processing unnecessary\r\nSpatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/WorldView/ available on the Third Party Missions Dissemination Service.\r\nThe following table summarises the offered product types\r\n\r\nEO-SIP Product Type\tBand Combination\tProcessing Level\tMissions\r\nWV6_PAN_2A\tPanchromatic (PAN)\tStandard/View Ready Standard\tWorldView-1 and 4\r\nWV6_PAN_OR\tPanchromatic (PAN)\tView Ready Stereo\tWorldView-1 and 4\r\nWV6_PAN_MP\tPanchromatic (PAN)\tMap Scale Ortho\tWorldView-1 and 4\r\nWV1_PAN__2A\tPanchromatic (PAN)\tStandard/View Ready Standard\tWorldView-2 and 3\r\nWV1_PAN__OR\tPanchromatic (PAN)\tView Ready Stereo\tWorldView-2 and 3\r\nWV1_PAN__MP\tPanchromatic (PAN)\tMap Scale Ortho\tWorldView-2 and 3\r\nWV1_4B__2A\t4-Band (4B)\tStandard/View Ready Standard\tWorldView-2, 3 and 4\r\nWV1_4B__OR\t4-Band (4B)\tView Ready Stereo\tWorldView-2, 3 and 4\r\nWV1_4B__MP\t4-Band (4B)\tMap Scale Ortho\tWorldView-2, 3 and 4\r\nWV1_8B_2A\t8-Band (8B)\tStandard/View Ready Standard\tWorldView-2 and 3\r\nWV1_8B_OR\t8-Band (8B)\tView Ready Stereo\tWorldView-2 and 3\r\nWV1_8B_MP\t8-Band (8B)\tMap Scale Ortho\tWorldView-2 and 3\r\nWV1_S8B__2A\tSWIR\tStandard/View Ready Standard\tWorldView-3\r\nWV1_S8B__MP\tSWIR\tMap Scale Ortho\tWorldView-3\r\n\r\nAs per ESA policy, very high-resolution imagery of conflict areas cannot be provided.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2009-02-07T00:00:00.000Z", "2025-12-03T23:59:59.999Z"]]}}, "instruments": ["WV60", "WV110", "WV110", "SpaceView-110"], "keywords": ["agriculture", "cameras", "earth-science->-agriculture", "earth-science->-biosphere->-ecosystems->-terrestrial-ecosystems->-forests", "earth-science->-biosphere->-vegetation", "earth-science->-cryosphere->-snow/ice", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-oceans", "earth-science->-terrestrial-hydrosphere->-snow/ice", "forestry", "imaging-spectrometers/radiometers", "l2", "l3", "land-surface", "natural-hazards-and-disaster-risk", "nir-(0.75---1.30)-\u00b5m", "oceans", "snow-and-ice", "spaceview-110", "sun-synchronous", "swir-(1.3---3.0)-\u00b5m", "vegetation", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m", "worldview-1", "worldview-2", "worldview-3", "worldview-4", "worldview-legion", "worldview.esa.archive", "wv-1:-17.6-km-wv-2:-16.4-km-wv-3:-13.1-km-wv-4:-13.1-km-wv-l:-10-km", "wv-1:-496-km-wv-2:-770-km-wv-3:-617-km-wv-4:-617-km-wv-l:-518-km", "wv1-4b--2a", "wv1-4b--mp", "wv1-4b--or", "wv1-8b--2a", "wv1-8b--mp", "wv1-8b--or", "wv1-pan-2a", "wv1-pan-mp", "wv1-pan-or", "wv110", "wv6-pan-2a", "wv6-pan-or", "wv60"], "license": "other", "platform": "WorldView-1,WorldView-2,WorldView-3,WorldView-4,WorldView Legion", "processing:level": "L2,L3", "title": "WorldView ESA archive"}, "alos-prism-l1c": {"description": "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East. The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: \u2022\tTime window: from 2006-08-01 to 2011-03-31 \u2022\tOrbits: from 2768 to 27604 \u2022\tPath (corresponds to JAXA track number): from 1 to 665 \u2022\tRow (corresponds to JAXA scene centre frame number): from 310 to 6790. The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors). The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width. The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2006-08-01T00:00:00.000Z", "2011-03-31T23:59:59.999Z"]]}}, "instruments": ["PRISM"], "keywords": ["35-km", "691.65-km", "alos-1", "alos-prism-l1c", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "human-dimensions", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "mapping-and-cartography", "natural-hazards-and-disaster-risk", "prism", "psm-0b1-1c", "sun-synchronous", "very-high-resolution---vhr-(0---5)-m", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "ALOS-1", "title": "ALOS PRISM L1C"}, "alos.prism.l1c.european.coverage.cloud.free": {"description": "This collection is composed of a subset of ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C products from the _$$ALOS PRISM L1C collection$$ https://earth.esa.int/eogateway/catalog/alos-prism-l1c (DOI: 10.57780/AL1-ff3877f) which have been chosen so as to provide a cloud-free coverage over Europe. 70% of the scenes contained within the collection have a cloud cover percentage of 0%, while the remaining 30% of the scenes have a cloud cover percentage of no more than 20%.\r\nThe collection is composed of PSM_OB1_1C EO-SIP products, with the PRISM sensor operating in OB1 mode with three views (Nadir, Forward and Backward) at 35 km width.", "extent": {"spatial": {"bbox": [[-25, 27, 46, 72]]}, "temporal": {"interval": [["2007-03-26T00:00:00.000Z", "2011-03-31T23:59:59.999Z"]]}}, "instruments": ["PRISM"], "keywords": ["35-km", "691.65-km", "alos-1", "alos.prism.l1c.european.coverage.cloud.free", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "human-dimensions", "imaging-spectrometers/radiometers", "land-surface", "land-use-and-land-cover", "mapping-and-cartography", "natural-hazards-and-disaster-risk", "prism", "psm-ob1-1c", "sun-synchronous", "very-high-resolution---vhr-(0---5m)", "vis-(0.40---0.75-\u00e2\u00b5m)"], "license": "other", "platform": "ALOS-1", "title": "ALOS PRISM L1C European Coverage Cloud Free"}, "kompsat.1.coverage.of.50.european.cities": {"description": "Available as a single coverage collection of data over 50 European Cities acquired by KOMPSAT-1\u2019s Electro-Optical Camera (EOC) geolocated and orthorectified. The dataset is composed by PAN imagery at 6.6 m GSD, in GeoTIFF orthorectified format.", "extent": {"spatial": {"bbox": [[-19, -26, 35, 66]]}, "temporal": {"interval": [["2000-03-06T00:00:00.000Z", "2004-08-06T23:59:59.999Z"]]}}, "instruments": ["EOC"], "keywords": ["17-km", "685-km", "cameras", "earth-science->-human-dimensions", "earth-science->-human-dimensions->-economic-resources", "earth-science->-human-dimensions->-natural-hazards", "earth-science->-land-surface", "earth-science->-land-surface->-land-use/land-cover", "earth-science->-oceans", "energy-and-natural-resources", "eoc", "eoc-pan-1p", "high-resolution---hr-(5---20)-m", "human-dimensions", "kompsat-1", "kompsat.1.coverage.of.50.european.cities", "land-surface", "land-use-and-land-cover", "natural-hazards-and-disaster-risk", "oceans", "sun-synchronous", "vis-(0.40---0.75)-\u00b5m"], "license": "other", "platform": "KOMPSAT-1", "title": "KOMPSAT-1 Coverage of 50 European Cities"}}, "providers_config": {"ALOS": {"_collection": "ALOS"}, "ALOS.AVNIR-2.L1C": {"_collection": "ALOS.AVNIR-2.L1C"}, "ALOS.PALSAR.FBS.FBD.PLR.products": {"_collection": "ALOS.PALSAR.FBS.FBD.PLR.products"}, "ALOSIPY": {"_collection": "ALOSIPY"}, "ALOS_PRISM_L1B": {"_collection": "ALOS_PRISM_L1B"}, "ASA_AP__0P_Scenes": {"_collection": "ASA_AP__0P_Scenes"}, "AUX_Dynamic_Open": {"_collection": "AUX_Dynamic_Open"}, "AVHRRLocalAreaCoverageImagery10": {"_collection": "AVHRRLocalAreaCoverageImagery10"}, "Cartosat-1.Euro-Maps.3D": {"_collection": "Cartosat-1.Euro-Maps.3D"}, "CosmoSkyMed": {"_collection": "CosmoSkyMed"}, "CryoSat.products": {"_collection": "CryoSat.products"}, "ENVISAT.ASA.APM_1P": {"_collection": "ENVISAT.ASA.APM_1P"}, "ENVISAT.ASA.APP_1P": {"_collection": "ENVISAT.ASA.APP_1P"}, "ENVISAT.ASA.APS_1P": {"_collection": "ENVISAT.ASA.APS_1P"}, "ENVISAT.ASA.GM1_1P": {"_collection": "ENVISAT.ASA.GM1_1P"}, "ENVISAT.ASA.IMM_1P": {"_collection": "ENVISAT.ASA.IMM_1P"}, "ENVISAT.ASA.IMP_1P": {"_collection": "ENVISAT.ASA.IMP_1P"}, "ENVISAT.ASA.IMS_1P": {"_collection": "ENVISAT.ASA.IMS_1P"}, "ENVISAT.ASA.IM__0P": {"_collection": "ENVISAT.ASA.IM__0P"}, "ENVISAT.ASA.WSM_1P": {"_collection": "ENVISAT.ASA.WSM_1P"}, "ENVISAT.ASA.WSS_1P": {"_collection": "ENVISAT.ASA.WSS_1P"}, "ENVISAT.ASA.WS__0P": {"_collection": "ENVISAT.ASA.WS__0P"}, "ENVISAT.ASA.WVI_1P": {"_collection": "ENVISAT.ASA.WVI_1P"}, "ENVISAT.ASA.WVS_1P": {"_collection": "ENVISAT.ASA.WVS_1P"}, "ENVISAT.ASA.WVW_2P": {"_collection": "ENVISAT.ASA.WVW_2P"}, "ENVISAT.MIP.NL__1P": {"_collection": "ENVISAT.MIP.NL__1P"}, "ENVISAT.MIP.NL__2P": {"_collection": "ENVISAT.MIP.NL__2P"}, "ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40": {"_collection": "ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40"}, "ERS_SAR_Envisat_ASAR_Alaska_mountains_L1_IM_AP": {"_collection": "ERS_SAR_Envisat_ASAR_Alaska_mountains_L1_IM_AP"}, "ERS_SAR_Envisat_ASAR_Alps_mountains_L1_IM_AP": {"_collection": "ERS_SAR_Envisat_ASAR_Alps_mountains_L1_IM_AP"}, "ERS_SAR_Envisat_ASAR_Austria_coverage_L1_IM": {"_collection": "ERS_SAR_Envisat_ASAR_Austria_coverage_L1_IM"}, "ERS_SAR_Envisat_ASAR_Iceland_mountains_L1_IM_AP": {"_collection": "ERS_SAR_Envisat_ASAR_Iceland_mountains_L1_IM_AP"}, "ERS_SAR_Envisat_ASAR_Italy_volcanoes_L1_IM": {"_collection": "ERS_SAR_Envisat_ASAR_Italy_volcanoes_L1_IM"}, "ERS_SAR_Envisat_ASAR_Pyrenees_mountains_L1_IM_AP": {"_collection": "ERS_SAR_Envisat_ASAR_Pyrenees_mountains_L1_IM_AP"}, "ERS_SAR_Envisat_ASAR_Scandinavia_mountains_L1_IM_AP": {"_collection": "ERS_SAR_Envisat_ASAR_Scandinavia_mountains_L1_IM_AP"}, "ERS_SAR_Svalbard_coverage_L1_IM": {"_collection": "ERS_SAR_Svalbard_coverage_L1_IM"}, "ESA_Orthorectified_Map_oriented_Level1_products": {"_collection": "ESA_Orthorectified_Map_oriented_Level1_products"}, "ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product": {"_collection": "ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product"}, "ESA_System_corrected_map_oriented_Level_1_products": {"_collection": "ESA_System_corrected_map_oriented_Level_1_products"}, "EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT": {"_collection": "EnvisatAATSRL1BBrightnessTemperatureRadianceAT1RBT"}, "FDRforAltimetry": {"_collection": "FDRforAltimetry"}, "FDRforAtmosphericCompositionATMOSL1B": {"_collection": "FDRforAtmosphericCompositionATMOSL1B"}, "FDRforRadiometry": {"_collection": "FDRforRadiometry"}, "GEOSAT-1.and.2.ESA.archive": {"_collection": "GEOSAT-1.and.2.ESA.archive"}, "GEOSAT-2.Portugal.Coverage": {"_collection": "GEOSAT-2.Portugal.Coverage"}, "GEOSAT2SpainCoverage10": {"_collection": "GEOSAT2SpainCoverage10"}, "GOCE_Global_Gravity_Field_Models_and_Grids": {"_collection": "GOCE_Global_Gravity_Field_Models_and_Grids"}, "GOCE_Level_1": {"_collection": "GOCE_Level_1"}, "GOCE_Level_2": {"_collection": "GOCE_Level_2"}, "GOCE_TEC_and_ROTI": {"_collection": "GOCE_TEC_and_ROTI"}, "GOCE_Telemetry": {"_collection": "GOCE_Telemetry"}, "GOCE_Thermosphere_Data": {"_collection": "GOCE_Thermosphere_Data"}, "GeoEye-1.ESA.archive": {"_collection": "GeoEye-1.ESA.archive"}, "ICEYE.ESA.Archive": {"_collection": "ICEYE.ESA.Archive"}, "IKONOS.ESA.archive": {"_collection": "IKONOS.ESA.archive"}, "IRS-1.archive": {"_collection": "IRS-1.archive"}, "Image2006": {"_collection": "Image2006"}, "Image2007": {"_collection": "Image2007"}, "JERS-1.OPS.SYC": {"_collection": "JERS-1.OPS.SYC"}, "JERS-1.SAR.PRI": {"_collection": "JERS-1.SAR.PRI"}, "JERS-1.SAR.SLC": {"_collection": "JERS-1.SAR.SLC"}, "KOMPSAT-2.ESA.archive": {"_collection": "KOMPSAT-2.ESA.archive"}, "L2SW_Open": {"_collection": "L2SW_Open"}, "L3SW_Open": {"_collection": "L3SW_Open"}, "L3_FT_Open": {"_collection": "L3_FT_Open"}, "L3_SIT_Open": {"_collection": "L3_SIT_Open"}, "L4WR_Open": {"_collection": "L4WR_Open"}, "L4_SIT_Open": {"_collection": "L4_SIT_Open"}, "LANDSAT.ETM.GTC": {"_collection": "LANDSAT.ETM.GTC"}, "LANDSAT.TM.GTC": {"_collection": "LANDSAT.TM.GTC"}, "Landsat5TMEuropeandNorthAfricaCoverage198485": {"_collection": "Landsat5TMEuropeandNorthAfricaCoverage198485"}, "Landsat5TMEuropeandNorthAfricaCoverage198689": {"_collection": "Landsat5TMEuropeandNorthAfricaCoverage198689"}, "Landsat5TMEuropeandNorthAfricaCoverage199598": {"_collection": "Landsat5TMEuropeandNorthAfricaCoverage199598"}, "LandsatETMCloudFree": {"_collection": "LandsatETMCloudFree"}, "LandsatTMCloudFree": {"_collection": "LandsatTMCloudFree"}, "Landsat_MSS_ESA_Archive": {"_collection": "Landsat_MSS_ESA_Archive"}, "Landsat_RBV": {"_collection": "Landsat_RBV"}, "MER.RR__1P": {"_collection": "MER.RR__1P"}, "MER.RR__2P": {"_collection": "MER.RR__2P"}, "MER_FRS_1P": {"_collection": "MER_FRS_1P"}, "MER_FRS_2P": {"_collection": "MER_FRS_2P"}, "NRT_Open": {"_collection": "NRT_Open"}, "OceanSat-2.NRT.data": {"_collection": "OceanSat-2.NRT.data"}, "PAZ.ESA.archive": {"_collection": "PAZ.ESA.archive"}, "PROBA.CHRIS.1A": {"_collection": "PROBA.CHRIS.1A"}, "PROBA.HRC.1A": {"_collection": "PROBA.HRC.1A"}, "PlanetScopeESAarchive": {"_collection": "PlanetScopeESAarchive"}, "Pleiades.ESA.archive": {"_collection": "Pleiades.ESA.archive"}, "PleiadesNeoESAArchive10": {"_collection": "PleiadesNeoESAArchive10"}, "QuickBird-2.ESA.archive": {"_collection": "QuickBird-2.ESA.archive"}, "RADARSAT1ESAArchive10": {"_collection": "RADARSAT1ESAArchive10"}, "Radarsat-2": {"_collection": "Radarsat-2"}, "RapidEye.ESA.archive": {"_collection": "RapidEye.ESA.archive"}, "RapidEye.South.America": {"_collection": "RapidEye.South.America"}, "RapidEye.time.series.for.Sentinel-2": {"_collection": "RapidEye.time.series.for.Sentinel-2"}, "SAR_IMM_1P": {"_collection": "SAR_IMM_1P"}, "SAR_IMP_1P": {"_collection": "SAR_IMP_1P"}, "SAR_IMS_1P": {"_collection": "SAR_IMS_1P"}, "SAR_IM_0P": {"_collection": "SAR_IM_0P"}, "SCIAMACHYLevel2LimbOzone": {"_collection": "SCIAMACHYLevel2LimbOzone"}, "SMOS_Open_V7": {"_collection": "SMOS_Open_V7"}, "SPOT-6.and.7.ESA.archive": {"_collection": "SPOT-6.and.7.ESA.archive"}, "SPOT1-5": {"_collection": "SPOT1-5"}, "SPOT4-5_Take5.ESAarchive": {"_collection": "SPOT4-5_Take5.ESAarchive"}, "SeaSat.ESA.archive": {"_collection": "SeaSat.ESA.archive"}, "SkySatESAarchive": {"_collection": "SkySatESAarchive"}, "TDPforAtmosphere": {"_collection": "TDPforAtmosphere"}, "TDPforInlandWater": {"_collection": "TDPforInlandWater"}, "TDPforLandice": {"_collection": "TDPforLandice"}, "TDPforOceanCoastalTopography": {"_collection": "TDPforOceanCoastalTopography"}, "TDPforOceanWaves": {"_collection": "TDPforOceanWaves"}, "TDPforSeaice": {"_collection": "TDPforSeaice"}, "TerraSAR-X": {"_collection": "TerraSAR-X"}, "TropForest": {"_collection": "TropForest"}, "VT_GOCE_Data": {"_collection": "VT_GOCE_Data"}, "WorldView-2.European.Cities": {"_collection": "WorldView-2.European.Cities"}, "WorldView.ESA.archive": {"_collection": "WorldView.ESA.archive"}, "alos-prism-l1c": {"_collection": "alos-prism-l1c"}, "alos.prism.l1c.european.coverage.cloud.free": {"_collection": "alos.prism.l1c.european.coverage.cloud.free"}, "kompsat.1.coverage.of.50.european.cities": {"_collection": "kompsat.1.coverage.of.50.european.cities"}}}, "eumetsat_ds": {"collections_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"keywords": ["eo:eum:cm:metop:ascszfr02"], "title": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"keywords": ["eo:eum:cm:metop:ascszor02"], "title": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"keywords": ["eo:eum:cm:metop:ascszrr02"], "title": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0069": {"keywords": ["eo:eum:dat:0069"], "title": "EO:EUM:DAT:0069"}, "EO:EUM:DAT:0070": {"keywords": ["eo:eum:dat:0070"], "title": "EO:EUM:DAT:0070"}, "EO:EUM:DAT:0071": {"keywords": ["eo:eum:dat:0071"], "title": "EO:EUM:DAT:0071"}, "EO:EUM:DAT:0072": {"keywords": ["eo:eum:dat:0072"], "title": "EO:EUM:DAT:0072"}, "EO:EUM:DAT:0073": {"keywords": ["eo:eum:dat:0073"], "title": "EO:EUM:DAT:0073"}, "EO:EUM:DAT:0074": {"keywords": ["eo:eum:dat:0074"], "title": "EO:EUM:DAT:0074"}, "EO:EUM:DAT:0075": {"keywords": ["eo:eum:dat:0075"], "title": "EO:EUM:DAT:0075"}, "EO:EUM:DAT:0076": {"keywords": ["eo:eum:dat:0076"], "title": "EO:EUM:DAT:0076"}, "EO:EUM:DAT:0077": {"keywords": ["eo:eum:dat:0077"], "title": "EO:EUM:DAT:0077"}, "EO:EUM:DAT:0078": {"keywords": ["eo:eum:dat:0078"], "title": "EO:EUM:DAT:0078"}, "EO:EUM:DAT:0088": {"keywords": ["eo:eum:dat:0088"], "title": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0103": {"keywords": ["eo:eum:dat:0103"], "title": "EO:EUM:DAT:0103"}, "EO:EUM:DAT:0142": {"keywords": ["eo:eum:dat:0142"], "title": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"keywords": ["eo:eum:dat:0143"], "title": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0151": {"keywords": ["eo:eum:dat:0151"], "title": "EO:EUM:DAT:0151"}, "EO:EUM:DAT:0152": {"keywords": ["eo:eum:dat:0152"], "title": "EO:EUM:DAT:0152"}, "EO:EUM:DAT:0274": {"keywords": ["eo:eum:dat:0274"], "title": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"keywords": ["eo:eum:dat:0300"], "title": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"keywords": ["eo:eum:dat:0301"], "title": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"keywords": ["eo:eum:dat:0302"], "title": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"keywords": ["eo:eum:dat:0303"], "title": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"keywords": ["eo:eum:dat:0305"], "title": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"keywords": ["eo:eum:dat:0343"], "title": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"keywords": ["eo:eum:dat:0344"], "title": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"keywords": ["eo:eum:dat:0345"], "title": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"keywords": ["eo:eum:dat:0348"], "title": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"keywords": ["eo:eum:dat:0349"], "title": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"keywords": ["eo:eum:dat:0374"], "title": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"keywords": ["eo:eum:dat:0394"], "title": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"keywords": ["eo:eum:dat:0398"], "title": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"keywords": ["eo:eum:dat:0405"], "title": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"keywords": ["eo:eum:dat:0406"], "title": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"keywords": ["eo:eum:dat:0407"], "title": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"keywords": ["eo:eum:dat:0408"], "title": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"keywords": ["eo:eum:dat:0409"], "title": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"keywords": ["eo:eum:dat:0410"], "title": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"keywords": ["eo:eum:dat:0411"], "title": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"keywords": ["eo:eum:dat:0412"], "title": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"keywords": ["eo:eum:dat:0413"], "title": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"keywords": ["eo:eum:dat:0414"], "title": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"keywords": ["eo:eum:dat:0415"], "title": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"keywords": ["eo:eum:dat:0416"], "title": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"keywords": ["eo:eum:dat:0417"], "title": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0452": {"keywords": ["eo:eum:dat:0452"], "title": "EO:EUM:DAT:0452"}, "EO:EUM:DAT:0520": {"keywords": ["eo:eum:dat:0520"], "title": "EO:EUM:DAT:0520"}, "EO:EUM:DAT:0533": {"keywords": ["eo:eum:dat:0533"], "title": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"keywords": ["eo:eum:dat:0556"], "title": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"keywords": ["eo:eum:dat:0557"], "title": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"keywords": ["eo:eum:dat:0558"], "title": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"keywords": ["eo:eum:dat:0576"], "title": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0579": {"keywords": ["eo:eum:dat:0579"], "title": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"keywords": ["eo:eum:dat:0581"], "title": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"keywords": ["eo:eum:dat:0582"], "title": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"keywords": ["eo:eum:dat:0583"], "title": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"keywords": ["eo:eum:dat:0584"], "title": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"keywords": ["eo:eum:dat:0585"], "title": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"keywords": ["eo:eum:dat:0586"], "title": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0590": {"keywords": ["eo:eum:dat:0590"], "title": "EO:EUM:DAT:0590"}, "EO:EUM:DAT:0601": {"keywords": ["eo:eum:dat:0601"], "title": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0602": {"keywords": ["eo:eum:dat:0602"], "title": "EO:EUM:DAT:0602"}, "EO:EUM:DAT:0615": {"keywords": ["eo:eum:dat:0615"], "title": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"keywords": ["eo:eum:dat:0617"], "title": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"keywords": ["eo:eum:dat:0645"], "title": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0662": {"keywords": ["eo:eum:dat:0662"], "title": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"keywords": ["eo:eum:dat:0665"], "title": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"keywords": ["eo:eum:dat:0676"], "title": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"keywords": ["eo:eum:dat:0677"], "title": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"keywords": ["eo:eum:dat:0678"], "title": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0679": {"keywords": ["eo:eum:dat:0679"], "title": "EO:EUM:DAT:0679"}, "EO:EUM:DAT:0680": {"keywords": ["eo:eum:dat:0680"], "title": "EO:EUM:DAT:0680"}, "EO:EUM:DAT:0681": {"keywords": ["eo:eum:dat:0681"], "title": "EO:EUM:DAT:0681"}, "EO:EUM:DAT:0682": {"keywords": ["eo:eum:dat:0682"], "title": "EO:EUM:DAT:0682"}, "EO:EUM:DAT:0683": {"keywords": ["eo:eum:dat:0683"], "title": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"keywords": ["eo:eum:dat:0684"], "title": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"keywords": ["eo:eum:dat:0685"], "title": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"keywords": ["eo:eum:dat:0686"], "title": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"keywords": ["eo:eum:dat:0687"], "title": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"keywords": ["eo:eum:dat:0688"], "title": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"keywords": ["eo:eum:dat:0690"], "title": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"keywords": ["eo:eum:dat:0691"], "title": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0694": {"keywords": ["eo:eum:dat:0694"], "title": "EO:EUM:DAT:0694"}, "EO:EUM:DAT:0758": {"keywords": ["eo:eum:dat:0758"], "title": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"keywords": ["eo:eum:dat:0782"], "title": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"keywords": ["eo:eum:dat:0799"], "title": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0800": {"keywords": ["eo:eum:dat:0800"], "title": "EO:EUM:DAT:0800"}, "EO:EUM:DAT:0801": {"keywords": ["eo:eum:dat:0801"], "title": "EO:EUM:DAT:0801"}, "EO:EUM:DAT:0833": {"keywords": ["eo:eum:dat:0833"], "title": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"keywords": ["eo:eum:dat:0834"], "title": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"keywords": ["eo:eum:dat:0835"], "title": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"keywords": ["eo:eum:dat:0836"], "title": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"keywords": ["eo:eum:dat:0837"], "title": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"keywords": ["eo:eum:dat:0838"], "title": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"keywords": ["eo:eum:dat:0839"], "title": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"keywords": ["eo:eum:dat:0840"], "title": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"keywords": ["eo:eum:dat:0841"], "title": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"keywords": ["eo:eum:dat:0842"], "title": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"keywords": ["eo:eum:dat:0850"], "title": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"keywords": ["eo:eum:dat:0851"], "title": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"keywords": ["eo:eum:dat:0852"], "title": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"keywords": ["eo:eum:dat:0853"], "title": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"keywords": ["eo:eum:dat:0854"], "title": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"keywords": ["eo:eum:dat:0855"], "title": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"keywords": ["eo:eum:dat:0856"], "title": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"keywords": ["eo:eum:dat:0857"], "title": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"keywords": ["eo:eum:dat:0858"], "title": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"keywords": ["eo:eum:dat:0859"], "title": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"keywords": ["eo:eum:dat:0862"], "title": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"keywords": ["eo:eum:dat:0863"], "title": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0874": {"keywords": ["eo:eum:dat:0874"], "title": "EO:EUM:DAT:0874"}, "EO:EUM:DAT:0880": {"keywords": ["eo:eum:dat:0880"], "title": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"keywords": ["eo:eum:dat:0881"], "title": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"keywords": ["eo:eum:dat:0882"], "title": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0885": {"keywords": ["eo:eum:dat:0885"], "title": "EO:EUM:DAT:0885"}, "EO:EUM:DAT:0886": {"keywords": ["eo:eum:dat:0886"], "title": "EO:EUM:DAT:0886"}, "EO:EUM:DAT:0892": {"keywords": ["eo:eum:dat:0892"], "title": "EO:EUM:DAT:0892"}, "EO:EUM:DAT:0894": {"keywords": ["eo:eum:dat:0894"], "title": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"keywords": ["eo:eum:dat:0895"], "title": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0905": {"keywords": ["eo:eum:dat:0905"], "title": "EO:EUM:DAT:0905"}, "EO:EUM:DAT:0908": {"keywords": ["eo:eum:dat:0908"], "title": "EO:EUM:DAT:0908"}, "EO:EUM:DAT:0910": {"keywords": ["eo:eum:dat:0910"], "title": "EO:EUM:DAT:0910"}, "EO:EUM:DAT:0921": {"keywords": ["eo:eum:dat:0921"], "title": "EO:EUM:DAT:0921"}, "EO:EUM:DAT:0959": {"keywords": ["eo:eum:dat:0959"], "title": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"keywords": ["eo:eum:dat:0960"], "title": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"keywords": ["eo:eum:dat:0961"], "title": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"keywords": ["eo:eum:dat:0962"], "title": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"keywords": ["eo:eum:dat:0963"], "title": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"keywords": ["eo:eum:dat:0964"], "title": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"keywords": ["eo:eum:dat:0986"], "title": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"keywords": ["eo:eum:dat:0987"], "title": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"keywords": ["eo:eum:dat:0998"], "title": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:1003": {"keywords": ["eo:eum:dat:1003"], "title": "EO:EUM:DAT:1003"}, "EO:EUM:DAT:1015": {"keywords": ["eo:eum:dat:1015"], "title": "EO:EUM:DAT:1015"}, "EO:EUM:DAT:1027": {"keywords": ["eo:eum:dat:1027"], "title": "EO:EUM:DAT:1027"}, "EO:EUM:DAT:1064": {"keywords": ["eo:eum:dat:1064"], "title": "EO:EUM:DAT:1064"}, "EO:EUM:DAT:1069": {"keywords": ["eo:eum:dat:1069"], "title": "EO:EUM:DAT:1069"}, "EO:EUM:DAT:1070": {"keywords": ["eo:eum:dat:1070"], "title": "EO:EUM:DAT:1070"}, "EO:EUM:DAT:1071": {"keywords": ["eo:eum:dat:1071"], "title": "EO:EUM:DAT:1071"}, "EO:EUM:DAT:1072": {"keywords": ["eo:eum:dat:1072"], "title": "EO:EUM:DAT:1072"}, "EO:EUM:DAT:1073": {"keywords": ["eo:eum:dat:1073"], "title": "EO:EUM:DAT:1073"}, "EO:EUM:DAT:1074": {"keywords": ["eo:eum:dat:1074"], "title": "EO:EUM:DAT:1074"}, "EO:EUM:DAT:1083": {"keywords": ["eo:eum:dat:1083"], "title": "EO:EUM:DAT:1083"}, "EO:EUM:DAT:1086": {"keywords": ["eo:eum:dat:1086"], "title": "EO:EUM:DAT:1086"}, "EO:EUM:DAT:1087": {"keywords": ["eo:eum:dat:1087"], "title": "EO:EUM:DAT:1087"}, "EO:EUM:DAT:1088": {"keywords": ["eo:eum:dat:1088"], "title": "EO:EUM:DAT:1088"}, "EO:EUM:DAT:1091": {"keywords": ["eo:eum:dat:1091"], "title": "EO:EUM:DAT:1091"}, "EO:EUM:DAT:1099": {"keywords": ["eo:eum:dat:1099"], "title": "EO:EUM:DAT:1099"}, "EO:EUM:DAT:1101": {"keywords": ["eo:eum:dat:1101"], "title": "EO:EUM:DAT:1101"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"keywords": ["eo:eum:dat:dmsp:osi-401-b"], "title": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"keywords": ["eo:eum:dat:metop:amsul1"], "title": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"keywords": ["eo:eum:dat:metop:ascszf1b"], "title": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"keywords": ["eo:eum:dat:metop:ascszo1b"], "title": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"keywords": ["eo:eum:dat:metop:ascszr1b"], "title": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"keywords": ["eo:eum:dat:metop:avhrrl1"], "title": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"keywords": ["eo:eum:dat:metop:glb-sst-nc"], "title": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"keywords": ["eo:eum:dat:metop:gomel1"], "title": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"keywords": ["eo:eum:dat:metop:iasil1c-all"], "title": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"keywords": ["eo:eum:dat:metop:iasil2cox"], "title": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASPCS01": {"keywords": ["eo:eum:dat:metop:iaspcs01"], "title": "EO:EUM:DAT:METOP:IASPCS01"}, "EO:EUM:DAT:METOP:IASSND02": {"keywords": ["eo:eum:dat:metop:iassnd02"], "title": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"keywords": ["eo:eum:dat:metop:lsa-002"], "title": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"keywords": ["eo:eum:dat:metop:mhsl1"], "title": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"keywords": ["eo:eum:dat:metop:nto"], "title": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"keywords": ["eo:eum:dat:metop:oas025"], "title": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"keywords": ["eo:eum:dat:metop:osi-104"], "title": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"keywords": ["eo:eum:dat:metop:osi-150-a"], "title": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"keywords": ["eo:eum:dat:metop:osi-150-b"], "title": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"keywords": ["eo:eum:dat:metop:somo12"], "title": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"keywords": ["eo:eum:dat:metop:somo25"], "title": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"keywords": ["eo:eum:dat:msg:clm"], "title": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"keywords": ["eo:eum:dat:msg:clm-iodc"], "title": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"keywords": ["eo:eum:dat:msg:cth"], "title": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"keywords": ["eo:eum:dat:msg:cth-iodc"], "title": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"keywords": ["eo:eum:dat:msg:hrseviri"], "title": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"keywords": ["eo:eum:dat:msg:hrseviri-iodc"], "title": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"keywords": ["eo:eum:dat:msg:msg15-rss"], "title": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"keywords": ["eo:eum:dat:msg:rss-clm"], "title": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"keywords": ["eo:eum:dat:mult:hirsl1"], "title": "EO:EUM:DAT:MULT:HIRSL1"}}, "providers_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"_collection": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"_collection": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"_collection": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0069": {"_collection": "EO:EUM:DAT:0069"}, "EO:EUM:DAT:0070": {"_collection": "EO:EUM:DAT:0070"}, "EO:EUM:DAT:0071": {"_collection": "EO:EUM:DAT:0071"}, "EO:EUM:DAT:0072": {"_collection": "EO:EUM:DAT:0072"}, "EO:EUM:DAT:0073": {"_collection": "EO:EUM:DAT:0073"}, "EO:EUM:DAT:0074": {"_collection": "EO:EUM:DAT:0074"}, "EO:EUM:DAT:0075": {"_collection": "EO:EUM:DAT:0075"}, "EO:EUM:DAT:0076": {"_collection": "EO:EUM:DAT:0076"}, "EO:EUM:DAT:0077": {"_collection": "EO:EUM:DAT:0077"}, "EO:EUM:DAT:0078": {"_collection": "EO:EUM:DAT:0078"}, "EO:EUM:DAT:0088": {"_collection": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0103": {"_collection": "EO:EUM:DAT:0103"}, "EO:EUM:DAT:0142": {"_collection": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"_collection": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0151": {"_collection": "EO:EUM:DAT:0151"}, "EO:EUM:DAT:0152": {"_collection": "EO:EUM:DAT:0152"}, "EO:EUM:DAT:0274": {"_collection": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"_collection": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"_collection": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"_collection": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"_collection": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"_collection": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"_collection": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"_collection": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"_collection": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"_collection": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"_collection": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"_collection": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"_collection": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"_collection": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"_collection": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"_collection": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"_collection": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"_collection": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"_collection": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"_collection": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"_collection": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"_collection": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"_collection": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"_collection": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"_collection": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"_collection": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"_collection": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0452": {"_collection": "EO:EUM:DAT:0452"}, "EO:EUM:DAT:0520": {"_collection": "EO:EUM:DAT:0520"}, "EO:EUM:DAT:0533": {"_collection": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"_collection": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"_collection": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"_collection": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"_collection": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0579": {"_collection": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"_collection": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"_collection": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"_collection": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"_collection": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"_collection": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"_collection": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0590": {"_collection": "EO:EUM:DAT:0590"}, "EO:EUM:DAT:0601": {"_collection": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0602": {"_collection": "EO:EUM:DAT:0602"}, "EO:EUM:DAT:0615": {"_collection": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"_collection": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"_collection": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0662": {"_collection": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"_collection": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"_collection": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"_collection": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"_collection": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0679": {"_collection": "EO:EUM:DAT:0679"}, "EO:EUM:DAT:0680": {"_collection": "EO:EUM:DAT:0680"}, "EO:EUM:DAT:0681": {"_collection": "EO:EUM:DAT:0681"}, "EO:EUM:DAT:0682": {"_collection": "EO:EUM:DAT:0682"}, "EO:EUM:DAT:0683": {"_collection": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"_collection": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"_collection": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"_collection": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"_collection": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"_collection": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"_collection": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"_collection": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0694": {"_collection": "EO:EUM:DAT:0694"}, "EO:EUM:DAT:0758": {"_collection": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"_collection": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"_collection": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0800": {"_collection": "EO:EUM:DAT:0800"}, "EO:EUM:DAT:0801": {"_collection": "EO:EUM:DAT:0801"}, "EO:EUM:DAT:0833": {"_collection": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"_collection": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"_collection": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"_collection": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"_collection": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"_collection": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"_collection": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"_collection": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"_collection": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"_collection": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"_collection": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"_collection": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"_collection": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"_collection": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"_collection": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"_collection": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"_collection": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"_collection": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"_collection": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"_collection": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"_collection": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"_collection": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0874": {"_collection": "EO:EUM:DAT:0874"}, "EO:EUM:DAT:0880": {"_collection": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"_collection": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"_collection": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0885": {"_collection": "EO:EUM:DAT:0885"}, "EO:EUM:DAT:0886": {"_collection": "EO:EUM:DAT:0886"}, "EO:EUM:DAT:0892": {"_collection": "EO:EUM:DAT:0892"}, "EO:EUM:DAT:0894": {"_collection": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"_collection": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0905": {"_collection": "EO:EUM:DAT:0905"}, "EO:EUM:DAT:0908": {"_collection": "EO:EUM:DAT:0908"}, "EO:EUM:DAT:0910": {"_collection": "EO:EUM:DAT:0910"}, "EO:EUM:DAT:0921": {"_collection": "EO:EUM:DAT:0921"}, "EO:EUM:DAT:0959": {"_collection": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"_collection": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"_collection": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"_collection": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"_collection": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"_collection": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"_collection": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"_collection": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"_collection": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:1003": {"_collection": "EO:EUM:DAT:1003"}, "EO:EUM:DAT:1015": {"_collection": "EO:EUM:DAT:1015"}, "EO:EUM:DAT:1027": {"_collection": "EO:EUM:DAT:1027"}, "EO:EUM:DAT:1064": {"_collection": "EO:EUM:DAT:1064"}, "EO:EUM:DAT:1069": {"_collection": "EO:EUM:DAT:1069"}, "EO:EUM:DAT:1070": {"_collection": "EO:EUM:DAT:1070"}, "EO:EUM:DAT:1071": {"_collection": "EO:EUM:DAT:1071"}, "EO:EUM:DAT:1072": {"_collection": "EO:EUM:DAT:1072"}, "EO:EUM:DAT:1073": {"_collection": "EO:EUM:DAT:1073"}, "EO:EUM:DAT:1074": {"_collection": "EO:EUM:DAT:1074"}, "EO:EUM:DAT:1083": {"_collection": "EO:EUM:DAT:1083"}, "EO:EUM:DAT:1086": {"_collection": "EO:EUM:DAT:1086"}, "EO:EUM:DAT:1087": {"_collection": "EO:EUM:DAT:1087"}, "EO:EUM:DAT:1088": {"_collection": "EO:EUM:DAT:1088"}, "EO:EUM:DAT:1091": {"_collection": "EO:EUM:DAT:1091"}, "EO:EUM:DAT:1099": {"_collection": "EO:EUM:DAT:1099"}, "EO:EUM:DAT:1101": {"_collection": "EO:EUM:DAT:1101"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"_collection": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"_collection": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"_collection": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"_collection": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"_collection": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"_collection": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"_collection": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"_collection": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"_collection": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"_collection": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASPCS01": {"_collection": "EO:EUM:DAT:METOP:IASPCS01"}, "EO:EUM:DAT:METOP:IASSND02": {"_collection": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"_collection": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"_collection": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"_collection": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"_collection": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"_collection": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"_collection": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"_collection": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"_collection": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"_collection": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"_collection": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"_collection": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"_collection": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"_collection": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"_collection": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"_collection": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"_collection": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"_collection": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"_collection": "EO:EUM:DAT:MULT:HIRSL1"}}}, "fedeo_ceda": {"collections_config": {"AATSR_ADV_L2_V2.31": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 2 aerosol products from the AATSR instrument on ENVISAT, derived using the ADV algorithm, version 2.31. Data is available for the period 2002-2012.For further details about these data products please see the linked documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-adv-l2-v2.31", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ADV Algorithm), Version 2.31"}, "AATSR_ADV_L3_V2.31": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31.   Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-adv-l3-v2.31", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31"}, "AATSR_ENS_L2_V2.6": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. For further details about these data products please see the documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-ens-l2-v2.6", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ensemble product), Version 2.6"}, "AATSR_ENS_L3_V2.6": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite.    The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.)  This product is version 2.6 of the ensemble product.   Data is provided for the period 2002 to 2012.  In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.  A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.", "instruments": ["AATSR", "ATSR-2"], "keywords": ["aatsr", "aatsr-ens-l3-v2.6", "aerosol", "atsr-2", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6"}, "AATSR_ORAC_L2_V4.01": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. For further details about these data products please see the linked documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-orac-l2-v4.01", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ORAC Algorithm), Version 4.01"}, "AATSR_ORAC_L3_V4.01": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-orac-l3-v4.01", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orac", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01"}, "AATSR_SU_L2_V4.3": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.    This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the Swansea University (SU) algorithm, version 4.3.   It covers the period from 2002 - 2012.For further details about these data products please see the linked documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-su-l2-v4.3", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (SU Algorithm), Version 4.3"}, "AATSR_SU_L3_V4.3": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily and monthly aerosol products from the AATSR instrument on the ENVISAT satellite, using the Swansea University (SU) algorithm, version 4.3. Data is available for the period 2002 - 2012.For further details about these data products please see the documentation.", "instruments": ["AATSR"], "keywords": ["aatsr", "aatsr-su-l3-v4.3", "aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (SU algorithm), Version 4.3"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V03.0": {"description": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.Case A: This covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "AVHRR-3", "AVHRR-3", "AVHRR-3", "MODIS"], "keywords": ["active-layer-thickness", "active-layer-thickness-l4-area4-pp-v03.0", "aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "envisat", "meris", "modis", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "proba-v", "sar-x", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost active layer thickness for the Northern Hemisphere, v3.0"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V04.0": {"description": "This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start  to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.  Case A: It covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "MODIS", "AVHRR-3", "AVHRR-3", "AVHRR-3"], "keywords": ["active-layer-thickness", "active-layer-thickness-l4-area4-pp-v04.0", "aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "envisat", "meris", "modis", "modis-terra", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "proba-v", "sar-x", "spot", "terra"], "license": "other", "platform": "AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_ANTARCTICA": {"description": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.  Case A: It covers Antarctica (south of 60\u00b0S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-ALT-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60\u00b0S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-ALT-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc", "instruments": ["MODIS", "MODIS"], "keywords": ["active-layer-thickness", "active-layer-thickness-l4-area4-pp-v05.0-antarctica", "aqua", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>land-surface>frozen-ground>permafrost", "level-4", "modis", "orthoimagery", "permafrost", "terra"], "license": "other", "platform": "AQUA,TERRA", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for Antarctica, v5.0"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"description": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.  Case A: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-ALT-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-ALT-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc", "instruments": ["MODIS", "MERIS", "C-SAR", "MSI", "MODIS"], "keywords": ["active-layer-thickness", "active-layer-thickness-l4-area4-pp-v05.0-northern-hemisphere", "aqua", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "earth-science>land-surface>frozen-ground>permafrost", "envisat", "level-4", "meris", "modis", "msi", "msi-(sentinel-2)", "orthoimagery", "permafrost", "proba-v", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v5.0"}, "AGB_MAPS_V2.0": {"description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018.   They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u2019s ASAR instrument and JAXA\u2019s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources.    The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.  The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha)  (raster dataset).   This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes.", "instruments": ["PALSAR-2", "PALSAR", "ASAR", "C-SAR", "C-SAR", "P-SAR"], "keywords": ["agb-maps-v2.0", "alos", "alos-1", "alos-2", "asar", "biomass", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>biosphere>vegetation>biomass", "earth-science>spectral/engineering>radar", "envisat", "esa", "orthoimagery", "p-sar", "palsar", "palsar-2", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "ALOS-2,ALOS-1,Envisat,Sentinel-1A,Sentinel-1B,Biomass", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2"}, "AGB_MAPS_V3.0": {"description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018.   They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u2019s ASAR instrument and JAXA\u2019s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources.    The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.  This release of the data is version 3.  Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha)  (raster dataset).   This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the  AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017).   These consist of two sets of maps:  the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", "instruments": ["P-SAR"], "keywords": ["agb-maps-v3.0", "biomass", "cci", "dif10", "earth-science>biosphere>vegetation>biomass", "esa", "orthoimagery", "p-sar"], "license": "other", "platform": "Biomass", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3"}, "AGB_MAPS_V4.0": {"description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u2019s ASAR instrument and JAXA\u2019s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources.    The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.  This release of the data is version 4.  Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha)  (raster dataset).   This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", "instruments": ["P-SAR"], "keywords": ["agb-maps-v4.0", "biomass", "cci", "dif10", "earth-science>biosphere>vegetation>biomass", "esa", "orthoimagery", "p-sar"], "license": "other", "platform": "Biomass", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4"}, "AGB_MAPS_V5.01": {"description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u2019s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA\u2019s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.  This release of the data is version 5.  Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha)  (raster dataset).   This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude.", "instruments": ["P-SAR"], "keywords": ["agb-maps-v5.01", "biomass", "cci", "dif10", "earth-science>biosphere>vegetation>biomass", "esa", "orthoimagery", "p-sar"], "license": "other", "platform": "Biomass", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01"}, "AGB_MAPS_V6.0": {"description": "This dataset comprises estimates of forest above-ground biomass (AGB) for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u2019s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA\u2019s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.This release of the data is version 6. Compared to version 5, version 6 consists of an update of the maps of AGB for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps for 2007 and 2022. AGB change maps have been created for consecutive years (e.g., 2020-2019), for a decadal interval (2020-2010) as well as for the interval 2010-2007. The pool of remote sensing data includes multi-temporal observations at L-band for all biomes and for all years and extended ICESat-2 observations to calibrate retrieval models. A cost function that preserves the temporal features as expressed in the remote sensing data has been refined to limit biases between the 2007-2010 and the 2015+ maps.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots per unit area2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2016-2015, 2017-2016, 2018-2017, 2019-2018, 2020-2019, 2021-2020, 2022-2021), over a decade (2020-2010) and over 2010-2007 are provided. Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", "instruments": ["P-SAR"], "keywords": ["agb-maps-v6.0", "biomass", "cci", "dif10", "earth-science>biosphere>vegetation>biomass", "esa", "orthoimagery", "p-sar"], "license": "other", "platform": "Biomass", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022, v6.0"}, "AQUA_MODIS_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["aqua-modis-l3c-0.01-v3.00-daily", "not-defined", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00"}, "AQUA_MODIS_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["aqua-modis-l3c-0.01-v3.00-monthly", "cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00"}, "AQUA_MODIS_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 4th July 2002 and ends on 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.In Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["aqua-modis-l3c-0.01-v4.00-daily", "cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "land-surface-temperature", "lst", "modis-aqua", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021), version 4.00"}, "ARCTIC_MSLA_20161024": {"description": "This dataset contains estimations of Arctic sea level anomalies produced by the ESA Sea Level Climate Change Initiative project (Sea_level_cci), based on satellite altimetry from the ENVISAT and SARAL/Altika satellites.    It has been produced by Collecte Localisation Satellites (CLS)  and the Plymouth Marine Laboratory (PML).The retrieval of sea level in the Arctic sea ice covered region requires specific processing steps of the satellite altimetry measurements. For this dataset, a specific radar waveform classification method has been applied based on a neural network approach, and the waveform retracking is based on a new adaptive retracking that is able to process both open ocean and peaky echoes measured in leads without introducing any bias between the two types of surfaces. Editing and mapping processing steps have been optimized for this dataset", "keywords": ["arctic-msla-20161024", "cci", "earth-science>oceans>sea-surface-topography>sea-surface-height", "esa", "orthoimagery", "sea-level"], "license": "other", "title": "ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML)"}, "ATSR2_ADV_L2_V2.31": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises  Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31.   Data are available for the period 1995-2002.For further details about these data products please see the linked documentation.", "instruments": ["ATSR-2"], "keywords": ["aerosol", "atsr-2", "atsr2-adv-l2-v2.31", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31"}, "ATSR2_ADV_L3_V2.31": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises  Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. It covers the period from 1995-2003.For further details about these data products please see the linked documentation.", "instruments": ["ATSR-2"], "keywords": ["aerosol", "atsr-2", "atsr2", "atsr2-adv-l3-v2.31", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ADV algorithm), Version 2.31"}, "ATSR2_ENS_L2_V2.6": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite.  The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. For further details about these data products please see the documentation.", "instruments": ["AATSR", "ATSR-2"], "keywords": ["aatsr", "aerosol", "atsr-2", "atsr2-ens-l2-v2.6", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ensemble product), Version 2.6"}, "ATSR2_ENS_L3_V2.6": {"description": "In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily, monthly and yearly aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002.    In 2002, it also contains data from the AATSR instrument on the ENVISAT satellite. A separate AATSR product covering the period 2002-2012 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.", "instruments": ["AATSR", "ATSR-2"], "keywords": ["aatsr", "aerosol", "atsr-2", "atsr2-ens-l3-v2.6", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6"}, "ATSR2_ORAC_L2_V4.01": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ORAC algorithm, version 4.01.   It covers the period from 1995-2003For further details about these data products please see the linked documentation.", "instruments": ["ATSR-2"], "keywords": ["aatsr", "aerosol", "atsr-2", "atsr2-orac-l2-v4.01", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01"}, "ATSR2_ORAC_L3_V4.01": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01.  The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation.", "instruments": ["ATSR-2"], "keywords": ["aerosol", "atsr-2", "atsr2-orac-l3-v4.01", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01"}, "ATSR2_SU_L2_V4.3": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the Swansea University (SU) algorithm, version 4.3.   Data are available for the period 1995-2003.For further details about these data products please see the documentation.", "instruments": ["ATSR-2"], "keywords": ["aerosol", "atsr-2", "atsr2-su-l2-v4.3", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (SU algorithm), Version 4.3"}, "ATSR2_SU_L3_V4.3": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises the Level 3 daily and monthly aerosol products from the ATSR-2 instrument on the ERS-2 satellite, using the Swansea University (SU) algorithm, version 4.3.    Data cover the period 1995 - 2003.For further details about these data products please see the documentation.", "instruments": ["ATSR-2"], "keywords": ["aerosol", "atsr-2", "atsr2-su-l3-v4.3", "cci", "dif10", "earth-science>atmosphere>aerosols", "ers-2", "esa", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (SU algorithm), Version 4.3"}, "BURNED_AREA_AVHRR-LTDR_GRID_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide.", "instruments": ["AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "burned-area", "burned-area-avhrr-ltdr-grid-v1.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "fire", "fire-disturbance", "gcos-essential-climate-variable", "noaa-11", "noaa-14", "noaa-16", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "pixel"], "license": "other", "platform": "NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1"}, "BURNED_AREA_AVHRR-LTDR_PIXEL_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide.", "instruments": ["AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "burned-area", "burned-area-avhrr-ltdr-pixel-v1.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "fire", "fire-disturbance", "gcos-essential-climate-variable", "noaa-11", "noaa-14", "noaa-16", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "pixel"], "license": "other", "platform": "NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1"}, "BURNED_AREA_MODIS_GRID_V5.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The MODIS Fire_cci v5.1 grid product described here contains gridded data on global burned area derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2019.  This product supercedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022. This gridded dataset has been derived from the MODIS Fire_cci v5.1 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution.   Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v2.0.7  product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.", "instruments": ["MODIS"], "keywords": ["burned-area", "burned-area-modis-grid-v5.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "eos", "esa", "fire", "fire-disturbance", "gcos-essential-climate-variable", "grid", "level-4", "moderate-resolution-imaging-spectroradiometer", "modis", "modis-terra", "month", "orthoimagery", "terra", "university-of-alcala"], "license": "other", "platform": "TERRA", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1"}, "BURNED_AREA_MODIS_PIXEL_V5.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020.  This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022.The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carr\u00e9e projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation.", "instruments": ["MODIS"], "keywords": ["burned-area", "burned-area-modis-pixel-v5.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire", "fire-disturbance", "gcos", "modis", "modis-terra", "orthoimagery", "pixel", "terra"], "license": "other", "platform": "TERRA", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1"}, "BURNED_AREA_SENTINEL3_SYN_GRID_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.This gridded dataset has been derived from the FireCCIS311 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative (C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation.", "keywords": ["burned-area", "burned-area-sentinel3-syn-grid-v1.1", "cci", "climate-change", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire-disturbance", "gcos", "grid", "orthoimagery"], "license": "other", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.1"}, "BURNED_AREA_SENTINEL3_SYN_PIXEL_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.The FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation.", "instruments": ["OLCI", "OLCI"], "keywords": ["burned-area", "burned-area-sentinel3-syn-pixel-v1.1", "cci", "climate-change", "dif10", "earth-science>atmosphere", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire", "fire-disturbance", "gcos", "level-3s", "olci", "orthoimagery", "pixel", "sentinel-3a", "sentinel-3b"], "license": "other", "platform": "Sentinel-3A,Sentinel-3B", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.1"}, "BURNED_AREA_SFDL_V1.0_PIXEL": {"description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project aims to generate burned area developed from satellite observations. The Long-Term Small Fire Dataset (SFDL) pixel products have been obtained using spectral information from Landsat sensors for three study areas located in different parts of the world (Amazon, Sahel and Siberia), and coinciding with the ESA CCI High Resolution Land Cover product.The dataset uses surface reflectance information from the Landsat-4 and Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI sensors, and covers the period 1990 to 2019, with a spatial resolution of 0.00025 degrees (approximately 30 m at the Equator).", "keywords": ["burned-area", "burned-area-sfdl-v1.0-pixel", "cci", "climate-change", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire-disturbance", "orthoimagery", "pixel"], "license": "other", "title": "ESA Fire Climate Change Initiative (Fire_cci): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia, Africa and Siberia, version 1.0"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v1.1 (also available), which covers Sub-Saharan Africa for the year 2016, by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution.", "instruments": ["MSI"], "keywords": ["burned-area", "burned-area-sfd-africa-sentinel2-grid-v1.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire", "fire-disturbance", "gcos-essential-climate-variable", "grid", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a"], "license": "other", "platform": "Sentinel-2", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 1.1"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V2.0": {"description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v2.0 (also available), which covers Sub-Saharan Africa for the year 2019, by summarising its burned area information into a regular grid covering the Earth at 0.05 x 0.05 degrees resolution and at monthly temporal resolution.", "keywords": ["burned-area", "burned-area-sfd-africa-sentinel2-grid-v2.0", "cci", "climate-change", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire-disturbance", "gcos-essential-climate-variable", "grid", "orthoimagery"], "license": "other", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 2.0"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V1.1": {"description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016.   Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator).   Gridded data products are also available in a separate dataset.", "instruments": ["MSI"], "keywords": ["burned-area", "burned-area-sfd-africa-sentinel2-pixel-v1.1", "cci", "climate-change", "dif10", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>environmental-governance/management>fire-management", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire", "fire-disturbance", "gcos", "msi", "msi-(sentinel-2)", "orthoimagery", "pixel", "sentinel-2", "sentinel-2-msi", "sentinel-2a"], "license": "other", "platform": "Sentinel-2", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V2.0": {"description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.This dataset is part of v2.0 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2019.   Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator).   Gridded data products are also available in a separate dataset.", "keywords": ["burned-area", "burned-area-sfd-africa-sentinel2-pixel-v2.0", "cci", "climate-change", "earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance", "earth-science>human-dimensions>natural-hazards>wildfires>burned-area", "esa", "fire-disturbance", "gcos-essential-climate-variable", "orthoimagery", "pixel"], "license": "other", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 2.0"}, "CCI_PLUS_CH4_GO2_SRFP_V1.0": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-ch4-go2-srfp-v1.0", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm, version 1.0.0"}, "CCI_PLUS_CH4_GO2_SRFP_V2.0.2": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "cci", "cci-plus-ch4-go2-srfp-v2.0.2", "ch4", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.2"}, "CCI_PLUS_CH4_GO2_SRFP_V2.0.3": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "cci", "cci-plus-ch4-go2-srfp-v2.0.3", "ch4", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.3"}, "CCI_PLUS_CH4_GO2_SRPR_V1.0": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4).   It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2(TANSO-FTS-2) Near Infrared (NIR) and  Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRPR Proxy Retrieval algorithm.   Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-ch4-go2-srpr-v1.0", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm, version 1.0.0"}, "CCI_PLUS_CH4_GO2_SRPR_V2.0.2": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "cci", "cci-plus-ch4-go2-srpr-v2.0.2", "ch4", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.2"}, "CCI_PLUS_CH4_GO2_SRPR_V2.0.3": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "cci", "cci-plus-ch4-go2-srpr-v2.0.3", "ch4", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.3"}, "CCI_PLUS_CH4_S5P_WFMD_V1.8": {"description": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \u00b5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication:  Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669\u2013694, https://doi.org/10.5194/amt-16-669-2023, 2023.", "instruments": ["TROPOMI"], "keywords": ["atmosphere", "carbon-monoxide", "cci", "cci-plus-ch4-s5p-wfmd-v1.8", "dif10", "earth-science>atmosphere", "earth-science>atmosphere>air-quality>carbon-monoxide", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite", "sentinel-5-precursor", "sentinel-5p", "tropomi"], "license": "other", "platform": "Sentinel-5P", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8,  November 2017 - October 2023"}, "CCI_PLUS_CH4_S5P_WFMD_V1.8_EXTENDED_JUNE2024": {"description": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \u00b5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - June 2024. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication:  Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669\u2013694, https://doi.org/10.5194/amt-16-669-2023, 2023.", "instruments": ["TROPOMI"], "keywords": ["atmosphere", "carbon-monoxide", "cci", "cci-plus-ch4-s5p-wfmd-v1.8-extended-june2024", "dif10", "earth-science>atmosphere", "earth-science>atmosphere>air-quality>carbon-monoxide", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "methane", "orthoimagery", "satellite", "sentinel-5-precursor", "sentinel-5p", "tropomi"], "license": "other", "platform": "Sentinel-5P", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8,  November 2017 - June 2024"}, "CCI_PLUS_CO2_GO2_SRFP_V1.0": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared(NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-go2-srfp-v1.0", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm, version 1.0.0"}, "CCI_PLUS_CO2_GO2_SRFP_V2.0.2": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-go2-srfp-v2.0.2", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.2"}, "CCI_PLUS_CO2_GO2_SRFP_V2.0.3": {"description": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-go2-srfp-v2.0.3", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.3"}, "CCI_PLUS_CO2_OC2_FOCA_V10.1": {"description": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.This version of the data (v10.1) was produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)and got co-funding from the University of Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).When citing this data, please also cite the following peer-reviewed publications:M.Reuter, M.Buchwitz, O.Schneising, S.No\u00ebl, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017M.Reuter, M.Buchwitz, O.Schneising, S.No\u00ebl, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017", "keywords": ["carbon-dioxide", "cci", "cci-plus-co2-oc2-foca-v10.1", "co2", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10.1"}, "CCI_PLUS_CO2_OC2_FOCA_V11.0": {"description": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2) data, generated using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.This version of the data (v11) was produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg).The FOCAL OCO-2 XCO2 retrieval development, data processing and analysis has received co-funding from ESA\u2019s Climate Change Initiative (CCI+) via project GHG-CCI+ (contract 4000126450/19/I-NB, https://climate.esa.int/en/projects/ghgs), EUMETSAT via the FOCAL-CO2M study (contract EUM/CO/19/4600002372/RL), the European Union via the Horizon 2020 (H2020) projects VERIFY (Grant Agreement No. 776810, http://verify.lsce.ipsl.fr) and CHE (Grant Agreement No. 776186, https://www.che-project.eu), and by the State and the University of Bremen.When citing this data, please also cite the following peer-reviewed publications:M.Reuter, M.Buchwitz, O.Schneising, S.No\u00ebl, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017M.Reuter, M.Buchwitz, O.Schneising, S.No\u00ebl, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017", "keywords": ["carbon-dioxide", "cci", "cci-plus-co2-oc2-foca-v11.0", "co2", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 11.0"}, "CCI_PLUS_CO2_TAN_OCFP_V1.0": {"description": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP.  The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only.  A full global dataset is in production.     For further information on the dataset, please see the linked documentation.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-tan-ocfp-v1.0", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "greenhouse-gases", "orthoimagery", "satellite", "tansat"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0"}, "CCI_PLUS_CO2_TAN_OCFP_V1.0_GLOBAL_LAND": {"description": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP.  This version of the dataset provides data globally over land.    For further information on the dataset, please see the linked documentation.Initially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6.    Additional time periods will be added in the future.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-tan-ocfp-v1.0-global-land", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "greenhouse-gases", "orthoimagery", "satellite", "tansat"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0"}, "CCI_PLUS_CO2_TAN_OCFP_V1.2": {"description": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP.  This version of the dataset provides data globally over land.    For further information on the dataset, please see the linked documentation.Initially this dataset contains data from the period from March 2017 to May 2018, delivered as part of the GHG_cci Climate Research Data Package 7.  Additional time periods may be delivered in the future.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).", "keywords": ["atmosphere", "carbon-dioxide", "cci", "cci-plus-co2-tan-ocfp-v1.2", "co2", "earth-science>atmosphere", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "orthoimagery", "satellite", "tansat"], "license": "other", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.2"}, "CDR_V2_ANALYSIS_L4_V2.1": {"description": "This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid.   It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements.  These data cover the period between 09/1981 and 12/2016.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.The CDR Version 2.1 product supercedes the CDR Version 2.0 product.    Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AATSR", "ATSR-1", "ATSR-2", "AVHRR-3", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["aatsr", "atsr", "atsr-1", "atsr-2", "avhrr-2", "avhrr-3", "cdr-v2-analysis-l4-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "envisat", "ers-1", "ers-2", "esa-climate-change-initiative", "metop-a", "noaa-11", "noaa-12", "noaa-14", "noaa-15", "noaa-16", "noaa-17", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "sea-surface-temperature", "sst"], "license": "other", "platform": "Envisat,ERS-1,ERS-2,Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1"}, "CDR_V2_ATSR_L2P_V2.1": {"description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments.  It covers the period between 11/1991 and 04/2012.  This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SST's to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AATSR", "ATSR-1", "ATSR-2"], "keywords": ["aatsr", "atsr", "atsr-1", "atsr-2", "cdr-v2-atsr-l2p-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "envisat", "ers-1", "ers-2", "esa-climate-change-initiative", "orthoimagery", "sea-surface-temperature", "sst"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1"}, "CDR_V2_ATSR_L3C_V2.1": {"description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012.  This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 product.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AATSR", "ATSR-1", "ATSR-2"], "keywords": ["aatsr", "atsr", "atsr-1", "atsr-2", "cci", "cdr-v2-atsr-l3c-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "envisat", "ers-1", "ers-2", "esa-climate-change-initiative", "esacci-sst", "orthoimagery", "sst"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record, version 2.1"}, "CDR_V2_ATSR_L3U_V2.1": {"description": "This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments.  It covers the period between 11/1991 and 04/2012.  The L3U products provide these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 and the Long Term product v1.1.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AATSR", "ATSR-1", "ATSR-2"], "keywords": ["aatsr", "atsr", "atsr-1", "atsr-2", "cci", "cdr-v2-atsr-l3u-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "envisat", "ers-1", "ers-2", "esa-climate-change-initiative", "esacci-sst", "orthoimagery", "sst"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1"}, "CDR_V2_AVHRR_L2P_V2.1": {"description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments.  It covers the period between 08/1981 and 12/2016.  This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AVHRR-3", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "cdr-v2-avhrr-l2p-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "esa-climate-change-initiative", "metop-a", "noaa-11", "noaa-12", "noaa-14", "noaa-15", "noaa-16", "noaa-17", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "sst"], "license": "other", "platform": "Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1"}, "CDR_V2_AVHRR_L3C_V2.1": {"description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments.  It covers the period between 08/1981 and 12/2016.  This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AVHRR-3", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "cdr-v2-avhrr-l3c-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "esa-climate-change-initiative", "metop-a", "noaa-11", "noaa-12", "noaa-14", "noaa-15", "noaa-16", "noaa-17", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "sst"], "license": "other", "platform": "Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1"}, "CDR_V2_AVHRR_L3U_V2.1": {"description": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments.  It covers the period between 08/1981 and 12/2016.  This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product.  Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "instruments": ["AVHRR-3", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "cci", "cdr-v2-avhrr-l3u-v2.1", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "esa-climate-change-initiative", "metop-a", "noaa-11", "noaa-12", "noaa-14", "noaa-15", "noaa-16", "noaa-17", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "sea-surface-temperature", "sst"], "license": "other", "platform": "Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1"}, "CDR_V2_CLIMATOLOGY_L4_V2.2": {"description": "This v2.2 SST_cci Climatology Data Record (CDR) consists of daily climatological mean sea surface temperature on a global 0.05 degree latitude-longitude grid, derived from the SST CCI analysis data for the period 1982 to 2010 (29 years). This climatology includes the post-hoc dust corrections from Merchant and Embury (2020) https://doi.org/10.3390/rs12162554.The changes from climatology v2.1 are:* Inclusion of post-hoc dust corrections from Merchant and Embury (2020) reduces biases in affected regions (tropical Atlantic Ocean and the Mediterranean, Red, and Arabian Seas).* Improved compliance with CF Conventions.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. (2019) Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223. http://doi.org/10.1038/s41597-019-0236-x", "keywords": ["cci", "cdr-v2-climatology-l4-v2.2", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "esa-climate-change-initiative", "orthoimagery", "sst"], "license": "other", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.2"}, "CRDP_4_EMMA_CH4_V1.2": {"description": "The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4).  It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT)    This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data).  For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).", "instruments": ["TANSO-FTS"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-emma-ch4-v1.2", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "emma", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "institute-of-environmental-physics", "level-2", "orthoimagery", "satellite-orbit-frequency", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2"}, "CRDP_4_EMMA_CO2_V2.2": {"description": "The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2).  It  has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.   This version of the product is v2.2, and forms part of the Climate Research Data Package 4.The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT.     This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data).   For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).", "instruments": ["SCIAMACHY", "TANSO-FTS"], "keywords": ["cci", "co2", "column-averaged-dry-air-mole-fraction-of-co2", "crdp-4-emma-co2-v2.2", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "emma", "environmental-satellite", "envisat", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "institute-of-environmental-physics", "level-2", "merged", "orthoimagery", "satellite-orbit-frequency", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer"], "license": "other", "platform": "Envisat,GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2"}, "CRDP_4_GOSAT_CH4_GOS_OCFP_V2.1": {"description": "The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm.   It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project.  This version is version 2.1 and forms part of the Climate Research Data Package 4.The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).", "instruments": ["TANSO-FTS"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-gosat-ch4-gos-ocfp-v2.1", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "ocfp", "orthoimagery", "satellite-orbit-frequency", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer", "university-of-leicester"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1"}, "CRDP_4_GOSAT_CH4_GOS_OCPR_V7.0": {"description": "This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.)  The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci).  This version of the data is v7.0 and forms part of the Climate Research Data Package 4.This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals.  A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "instruments": ["TANSO-FTS"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-gosat-ch4-gos-ocpr-v7.0", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "ocpr", "orthoimagery", "proxy", "satellite-orbit-frequency", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer", "university-of-leicester"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0"}, "CRDP_4_GOSAT_CH4_GOS_SRFP_V2.3.8": {"description": "The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm.   It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci).  This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm.  The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document.", "instruments": ["TANSO-FTS"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-gosat-ch4-gos-srfp-v2.3.8", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "fp", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "netherlands-institute-for-space-research", "orthoimagery", "satellite-orbit-frequency", "srfp", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8"}, "CRDP_4_GOSAT_CH4_GOS_SRPR_V2.3.8": {"description": "The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm.   It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project.  This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "instruments": ["TANSO-FTS"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-gosat-ch4-gos-srpr-v2.3.8", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "netherlands-institute-for-space-research", "orthoimagery", "proxy", "remotec", "satellite-orbit-frequency", "srpr", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8"}, "CRDP_4_GOSAT_CO2_GOS_OCFP_V7.0": {"description": "The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product.  For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).", "instruments": ["TANSO-FTS"], "keywords": ["cci", "co2", "column-averaged-dry-air-mole-fraction-of-co2", "crdp-4-gosat-co2-gos-ocfp-v7.0", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "ocfp", "orthoimagery", "satellite-orbit-frequency", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer", "university-of-leicester"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0"}, "CRDP_4_GOSAT_CO2_GOS_SRFP_V2.3.8": {"description": "The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project.  This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT.   A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product.  It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product.  For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.   The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.", "instruments": ["TANSO-FTS"], "keywords": ["cci", "co2", "column-averaged-dry-air-mole-fraction-of-co2", "crdp-4-gosat-co2-gos-srfp-v2.3.8", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "esa", "ghg", "gosat", "gosat-1", "gosat-programme", "greenhouse-gases", "level-2", "netherlands-institute-for-space-research", "orthoimagery", "satellite-orbit-frequency", "srfp", "tanso-fts", "thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer"], "license": "other", "platform": "GOSAT-1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8"}, "CRDP_4_SCIAMACHY_CH4_SCI_IMAP_V7.2": {"description": "The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4).  It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm.   It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.   This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4.The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.", "instruments": ["SCIAMACHY"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-sciamachy-ch4-sci-imap-v7.2", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "environmental-satellite", "envisat", "esa", "ghg", "greenhouse-gases", "imap", "level-2", "netherlands-institute-for-space-research", "orthoimagery", "satellite-orbit-frequency", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy"], "license": "other", "platform": "Envisat", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2"}, "CRDP_4_SCIAMACHY_CH4_SCI_WFMD_V4.0": {"description": "The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project.   This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.The Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. The data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. For further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "instruments": ["SCIAMACHY"], "keywords": ["cci", "ch4", "column-averaged-dry-air-mole-fraction-of-ch4", "crdp-4-sciamachy-ch4-sci-wfmd-v4.0", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "environmental-satellite", "envisat", "esa", "ghg", "greenhouse-gases", "level-2", "orthoimagery", "satellite-orbit-frequency", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy", "university-of-bremen", "wfmd"], "license": "other", "platform": "Envisat", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0"}, "CRDP_4_SCIAMACHY_CO2_SCI_BESD_V02.01.02": {"description": "The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT.  It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data.  A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm.   It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.", "instruments": ["SCIAMACHY"], "keywords": ["besd", "cci", "co2", "column-averaged-dry-air-mole-fraction-of-co2", "crdp-4-sciamachy-co2-sci-besd-v02.01.02", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "environmental-satellite", "envisat", "esa", "ghg", "greenhouse-gases", "institute-of-environmental-physics", "level-2", "orthoimagery", "satellite-orbit-frequency", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy"], "license": "other", "platform": "Envisat", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02"}, "CRDP_4_SCIAMACHY_CO2_SCI_WFMD_V4.0": {"description": "The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT.   It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles.  Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm).  It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.", "instruments": ["SCIAMACHY"], "keywords": ["cci", "co2", "column-averaged-dry-air-mole-fraction-of-co2", "crdp-4-sciamachy-co2-sci-wfmd-v4.0", "dif10", "earth-science>atmosphere>atmospheric-chemistry", "earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide", "environmental-satellite", "envisat", "esa", "ghg", "greenhouse-gases", "level-2", "orthoimagery", "satellite-orbit-frequency", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy", "university-of-bremen", "wfmd"], "license": "other", "platform": "Envisat", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0"}, "DAILY_FILES_ACTIVE_V05.2": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["AMI/Scatterometer", "AMI/Scatterometer", "ASCAT", "ASCAT"], "keywords": ["active", "ami-scat", "ami/scatterometer", "ascat", "cci", "daily-files-active-v05.2", "dif10", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "metop-a", "metop-b", "orthoimagery", "soil-moisture"], "license": "other", "platform": "ERS-1,ERS-2,Metop-A,Metop-B", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2"}, "DAILY_FILES_ACTIVE_V05.3": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.3 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["AMI/Scatterometer", "AMI/Scatterometer", "ASCAT", "ASCAT"], "keywords": ["active", "ami-scat", "ami/scatterometer", "ascat", "cci", "daily-files-active-v05.3", "dif10", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "metop-a", "metop-b", "orthoimagery", "soil-moisture"], "license": "other", "platform": "ERS-1,ERS-2,Metop-A,Metop-B", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.3"}, "DAILY_FILES_ACTIVE_V06.1": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "instruments": ["AMI/Scatterometer", "AMI/Scatterometer", "ASCAT", "ASCAT"], "keywords": ["active", "ami-scat", "ami/scatterometer", "ascat", "cci", "daily-files-active-v06.1", "dif10", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "metop-a", "metop-b", "orthoimagery", "soil-moisture"], "license": "other", "platform": "ERS-1,ERS-2,Metop-A,Metop-B", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1"}, "DAILY_FILES_ACTIVE_V06.2": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["active", "cci", "daily-files-active-v06.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2"}, "DAILY_FILES_ACTIVE_V07.1": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v07.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["active", "cci", "daily-files-active-v07.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 07.1"}, "DAILY_FILES_ACTIVE_V08.1": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v08.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["active", "cci", "daily-files-active-v08.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 08.1"}, "DAILY_FILES_ACTIVE_V09.1": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v09.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["active", "cci", "daily-files-active-v09.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.1"}, "DAILY_FILES_ACTIVE_V09.2": {"description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v09.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 ACTIVE product. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["active", "cci", "daily-files-active-v09.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.2"}, "DAILY_FILES_BREAK_ADJUSTED_COMBINED_V06.1": {"description": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su,  C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "SSM/I", "AMI/Scatterometer", "AMI/Scatterometer", "AMSR2", "ASCAT", "ASCAT", "SMMR", "MIRAS", "TMI"], "keywords": ["ami-scat", "ami/scatterometer", "amsr-e", "amsr2", "amsre", "aqua", "ascat", "cci", "combined", "coriolis", "daily-files-break-adjusted-combined-v06.1", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "dmsp-f13", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>microwave", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "fy-3b", "gcom-w1", "gmi", "metop-a", "metop-b", "miras", "nimbus-7", "orthoimagery", "smap", "smmr", "smos", "soil-moisture", "ssm/i", "tmi", "trmm", "virr", "windsat"], "license": "other", "platform": "FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1"}, "DAILY_FILES_BREAK_ADJUSTED_COMBINED_V07.1": {"description": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for their v07.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v07.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v07.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su,  C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "combined", "daily-files-break-adjusted-combined-v07.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 07.1"}, "DAILY_FILES_COMBINED_V05.2": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "SSM/I", "AMI/Scatterometer", "AMI/Scatterometer", "AMSR2", "ASCAT", "ASCAT", "SMMR", "MIRAS", "TMI"], "keywords": ["ami-scat", "ami/scatterometer", "amsr-e", "amsr2", "amsre", "aqua", "ascat", "cci", "combined", "coriolis", "daily-files-combined-v05.2", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "dmsp-f13", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture", "earth-science>spectral/engineering>microwave", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "gcom-w1", "metop-a", "metop-b", "miras", "nimbus-7", "orthoimagery", "smap", "smmr", "smos", "soil-moisture", "ssm/i", "tmi", "trmm", "windsat"], "license": "other", "platform": "AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2"}, "DAILY_FILES_COMBINED_V05.3": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.3 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["WINDSAT", "SSM/I", "SSM/I", "AMI/Scatterometer", "AMI/Scatterometer", "AMSR2", "ASCAT", "ASCAT", "SMMR", "TMI"], "keywords": ["ami-scat", "ami/scatterometer", "amsr2", "aqua", "ascat", "cci", "combined", "coriolis", "daily-files-combined-v05.3", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>microwave", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "fy-3b", "gcom-w1", "metop-a", "metop-b", "miras", "nimbus-7", "orthoimagery", "smap", "smmr", "soil-moisture", "ssm/i", "tmi", "trmm", "windsat"], "license": "other", "platform": "FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3"}, "DAILY_FILES_COMBINED_V06.1": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "SSM/I", "AMI/Scatterometer", "AMI/Scatterometer", "AMSR2", "ASCAT", "ASCAT", "SMMR", "MIRAS", "TMI"], "keywords": ["ami-scat", "ami/scatterometer", "amsr-e", "amsr2", "amsre", "aqua", "ascat", "cci", "combined", "coriolis", "daily-files-combined-v06.1", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "dmsp-f13", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>microwave", "earth-science>spectral/engineering>radar", "ers-1", "ers-2", "ers-wind-scatterometer", "esa", "fy-3b", "gcom-w1", "gmi", "metop-a", "metop-b", "miras", "nimbus-7", "orthoimagery", "smap", "smmr", "smos", "soil-moisture", "ssm/i", "tmi", "trmm", "virr", "windsat"], "license": "other", "platform": "FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1"}, "DAILY_FILES_COMBINED_V06.2": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["cci", "combined", "daily-files-combined-v06.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.2"}, "DAILY_FILES_COMBINED_V07.1": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v07.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["cci", "combined", "daily-files-combined-v07.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 07.1"}, "DAILY_FILES_COMBINED_V08.1": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v08.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "combined", "daily-files-combined-v08.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 08.1"}, "DAILY_FILES_COMBINED_V09.1": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v09.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "combined", "daily-files-combined-v09.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.1"}, "DAILY_FILES_COMBINED_V09.2": {"description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v09.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 COMBINED product. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "combined", "daily-files-combined-v09.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.2"}, "DAILY_FILES_PASSIVE_V05.2": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "SSM/I", "AMSR2", "SMMR", "MIRAS", "TMI"], "keywords": ["amsr-e", "amsr2", "amsre", "aqua", "cci", "coriolis", "daily-files-passive-v05.2", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "dmsp-f13", "earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture", "earth-science>spectral/engineering>microwave", "esa", "gcom-w1", "miras", "nimbus-7", "orthoimagery", "passive", "smap", "smmr", "smos", "soil-moisture", "ssm/i", "tmi", "trmm", "windsat"], "license": "other", "platform": "AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,GCOM-W1,Nimbus-7,SMOS,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2"}, "DAILY_FILES_PASSIVE_V05.3": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.3 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "AMSR2", "SMMR", "TMI"], "keywords": ["amsr-e", "amsr2", "amsre", "aqua", "cci", "coriolis", "daily-files-passive-v05.3", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "earth-science>spectral/engineering>microwave", "esa", "fy-3b", "gcom-w1", "miras", "nimbus-7", "orthoimagery", "passive", "smap", "smmr", "ssm/i", "tmi", "trmm", "windsat"], "license": "other", "platform": "FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,GCOM-W1,Nimbus-7,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3"}, "DAILY_FILES_PASSIVE_V06.1": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "instruments": ["AMSR-E", "WINDSAT", "SSM/I", "SSM/I", "SSM/I", "AMSR2", "SMMR", "MIRAS", "TMI"], "keywords": ["amsr-e", "amsr2", "amsre", "aqua", "cci", "coriolis", "daily-files-passive-v06.1", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-f08", "dmsp-f11", "dmsp-f13", "earth-science>agriculture>soils>soil-moisture/water-content", "earth-science>spectral/engineering>microwave", "esa", "fy-3b", "gcom-w1", "gmi", "miras", "nimbus-7", "orthoimagery", "passive", "smap", "smmr", "smos", "soil-moisture", "ssm/i", "tmi", "trmm", "virr", "windsat"], "license": "other", "platform": "FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,GCOM-W1,Nimbus-7,SMOS,TRMM,SMAP", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.1"}, "DAILY_FILES_PASSIVE_V06.2": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["cci", "daily-files-passive-v06.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "passive", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.2"}, "DAILY_FILES_PASSIVE_V07.1": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v07.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001", "keywords": ["cci", "daily-files-passive-v07.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "passive", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 07.1"}, "DAILY_FILES_PASSIVE_V08.1": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.The v08.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "daily-files-passive-v08.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "passive", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 08.1"}, "DAILY_FILES_PASSIVE_V09.1": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.The v09.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "daily-files-passive-v09.1", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "passive", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.1"}, "DAILY_FILES_PASSIVE_V09.2": {"description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.The v09.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 PASSIVE product. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u2013739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.", "keywords": ["cci", "daily-files-passive-v09.2", "earth-science>agriculture>soils>soil-moisture/water-content", "esa", "orthoimagery", "passive", "soil-moisture"], "license": "other", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.2"}, "DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_CRYOSAT_V1.0_NH": {"description": "This dataset provides daily drift-aware sea ice freeboard and thickness maps, using satellite altimetry data from CryoSat-2, covering the entire Arctic sea ice domain. Daily files are provided during boreal winter seasons (October to April).Neglecting sea ice drift when generating monthly sea ice thickness maps from satellite altimetry will cause blurring of the spatial distribution of ice thickness. This dataset synergizes sea ice freeboard and thickness information from satellite altimetry with sea ice drift estimates from passive microwave satellite sensors. Individual parcels of satellite altimeter measurements are advected daily over a time span of one month to obtain drift-aware sea ice freeboard and thickness maps. Because of the drift correction, this allows the determination of sea ice that was overflown by the satellite multiple times, and therefore the estimation of growth rates and changes in the sea ice thickness distribution due to deformation and thermodynamic ice growth between satellite overflights. With the estimation of sea ice growth, measurements can be corrected for the time offset between the acquisition day and the target day, the day to which all measurements within a month are projected.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, as part of the ESA CCI Sea Ice project.", "keywords": ["arctic", "cci", "drift-aware", "drift-aware-sea-ice-thickness-l3c-cryosat-v1.0-nh", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from CryoSat-2, v1.0"}, "DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_ENVISAT_V1.0_NH": {"description": "This dataset provides daily drift-aware sea ice freeboard and thickness maps, using satellite altimetry data from Envisat, covering the entire Arctic sea ice domain. Daily files are provided during boreal winter seasons (October to April).Neglecting sea ice drift when generating monthly sea ice thickness maps from satellite altimetry will cause blurring of the spatial distribution of ice thickness. This dataset synergizes sea ice freeboard and thickness information from satellite altimetry with sea ice drift estimates from passive microwave satellite sensors. Individual parcels of satellite altimeter measurements are advected daily over a time span of one month to obtain drift-aware sea ice freeboard and thickness maps. Because of the drift correction, this allows the determination of sea ice that was overflown by the satellite multiple times, and therefore the estimation of growth rates and changes in the sea ice thickness distribution due to deformation and thermodynamic ice growth between satellite overflights. With the estimation of sea ice growth, measurements can be corrected for the time offset between the acquisition day and the target day, the day to which all measurements within a month are projected.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, as part of the ESA CCI Sea Ice project.", "keywords": ["arctic", "cci", "drift-aware", "drift-aware-sea-ice-thickness-l3c-envisat-v1.0-nh", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from Envisat, v1.0"}, "DTU_TUM_ARCTIC_ANTARCTIC_MSLA_20170720": {"description": "This dataset contains high latitude sea level anomalies produced by DTU (Technical University of Denmark) and TUM (Technical University of Munich) as part of the ESA Sea Level CCI (Climate Change Initiative) project, covering both the Arctic and Antarctic regions.The data comprises weekly means from August 1991 to April 2017 and has been obtained using satellite altimetry data from four satellite missions: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336).Two datasets are available: dataset #1 is based on the ALES+ retracking without correction of the inverse barometer whereas dataset #2 has been corrected for this effect.Dataset #1 is provided both 'masked' and 'unmasked', where the masked data have been masked using sea ice concentrations downloaded from osisaf.met.no/p/ice. Dataset #2 is provided both 'masked' and 'unmasked', where the masked data have had data points retrieved over land removed from the files.", "keywords": ["dtu-tum-arctic-antarctic-msla-20170720", "esa-cci", "orthoimagery", "sla"], "license": "other", "title": "ESA Sea Level Climate Change Initiative  (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)"}, "ENVISAT_ATSR_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["AATSR"], "keywords": ["aatsr", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "envisat", "envisat-atsr-l3c-0.01-v3.00-daily", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00"}, "ENVISAT_ATSR_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["AATSR"], "keywords": ["aatsr", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "envisat", "envisat-atsr-l3c-0.01-v3.00-monthly", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00"}, "ENVISAT_ATSR_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on the Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 20th May 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.Version 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["AATSR"], "keywords": ["aatsr", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "envisat", "envisat-atsr-l3c-0.01-v4.00-daily", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 4.00"}, "ERS-2_ATSR_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag,  and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["ATSR-2"], "keywords": ["atsr-2", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "ers-2", "ers-2-atsr-l3c-0.01-v3.00-daily", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2013), version 3.00"}, "ERS-2_ATSR_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag,  and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["ATSR-2"], "keywords": ["atsr-2", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "ers-2", "ers-2-atsr-l3c-0.01-v3.00-monthly", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2013), version 3.00"}, "ERS-2_ATSR_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st June 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.Version 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["ATSR-2"], "keywords": ["atsr-2", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "ers-2", "ers-2-atsr-l3c-0.01-v4.00-daily", "esa", "land-surface-temperature", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2003), version 4.00"}, "FCDR_V2.0": {"description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project,  Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project.   These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015.   This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products.   They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km.  The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00e8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u20132010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org", "instruments": ["SIRAL", "RA-2", "RA", "RA", "POSEIDON-2", "SSALT"], "keywords": ["altika", "cryosat-2", "dif10", "earth-science>oceans>sea-surface-topography>sea-surface-height", "earth-science>spectral/engineering>radar", "envisat", "ers-1", "ers-2", "esa-cci", "fcdr-v2.0", "gfo", "gfo-ra", "jason-1", "jason-2", "orthoimagery", "poseidon-2", "poseidon-3", "ra", "ra-2", "saral", "sea-level", "siral", "sla", "ssalt", "topex/poseidon"], "license": "other", "platform": "CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Fundamental Climate Data Records of sea level anomalies and altimeter standards, Version 2.0"}, "GIEMS_METHANE_CENTRIC_V1.0": {"description": "To aid methane emission modelling within ESA's Regional Carbon Cycle Assessment and Processes Phase 2 (RECCAP-2) project, a methane-centric wetland dataset based on the Global Inundation Estimate from Multiple Satellites (GIEMS-2) database has been produced.The GIEMS-2 database provides the monthly extent of the continental water surfaces, including lakes, rivers, wetlands, and rice paddies, from 1992 to 2015, as described in Prigent et al. (2020). It is on a 0.25 x 0.25 degree regular grid in latitude and longitude. It has recently been extended to 2020 within the RECCAP-2 project.For methane emission modeling, three water surface types are usually considered separately: the permanent water surfaces (such as lakes, rivers, and reservoirs), the rice paddies, and the wetlands (i.e., the remaining water surfaces). As a consequence, the possibility to separate these contributions within the GIEMS pixels is required. This methane-centric GIEMS dataset isolates wetlands from the other surface waters in order to facilitate the estimation of the wetland methane emissions.", "keywords": ["earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane", "giems", "giems-methane-centric-v1.0", "methane", "orthoimagery", "reccap-2", "wetland"], "license": "other", "title": "ESA RECCAP-2 Climate Change Initiative (RECCAP2_cci): Methane-Centric Wetland Dataset Based on GIEMS (1992-2020), v1.0"}, "GMPE_CDR_V2_L4_V2.0": {"description": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci) has accurately determined the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified sea surface temperatures (SSTs) to a quality suitable for climate research. This GHRSST (Group for High Resolution Sea Surface Temperature) Multi-Product Ensemble (GMPE) dataset was produced by the ESA SST_cci project to facilitate comparison of its own spatially complete analyses with other level 4 SST analysis products. It provides the median and standard deviation of the ensemble of input analyses, differences between the individual analyses and the median, and gradients in the input data and the median. The outputs are provided on a 0.25\u02da regular latitude-longitude grid. The product extends from 1 September 1981 to 31 December 2016.The product was generated using the following inputs: ESA SST_cci Analysis version 2.0; ESA SST_cci Analysis version 1.1; E.U. Copernicus Marine Environment Monitoring Service (CMEMS) SST information (the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing); National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis; Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis; Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 (10 realisations); Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis. Full details of the data used to generate this product are provided in the associated documentation.", "keywords": ["cci", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "esa", "esacci-sst", "gmpe-cdr-v2-l4-v2.0", "orthoimagery", "sst", "unspecified"], "license": "other", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): GHRSST Multi-Product ensemble (GMPE), v2.0"}, "GOES_IMAGER_ABI_L3C_V1.00_MONTHLY": {"description": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16.   The original surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16, and in the L3C dataset a monthly average at each time step is provided. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05\u00bax0.05\u00ba. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. LSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "goes", "goes-imager-abi-l3c-v1.00-monthly", "land-surface-temperature", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Geostationary Operational Environmental Satellite (GOES) level 3C (L3C) product (2009-2020), version 1.00"}, "GOES_IMAGER_ABI_L3U_V1.00": {"description": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16.   The surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05\u00bax0.05\u00ba. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. LSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "goes", "goes-imager-abi-l3u-v1.00", "land-surface-temperature", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3U (L3U) product (2009-2020), version 1.00"}, "GOMOS_AERGOM_L3_V3.00": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 3 gridded stratospheric aerosol properties from the GOMOS instrument on the ENVISAT satellite.  This version of the data is version 3.00, and has been derived using the AERGOM algorithm by BIRA (Belgian Institute for Space Aeronomy). For further details about these data products please see the linked documentation.", "instruments": ["GOMOS"], "keywords": ["aerosol", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "esa", "gomos", "gomos-aergom-l3-v3.00", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from GOMOS (AERGOM algorithm), Version 3.00"}, "GRAVIMETRIC_MASS_BALANCE_BASIN_V3.0": {"description": "This dataset contains the Gravimetric Mass Balance (GMB) basin product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth\u2019s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)The GMB basin product provides time series of integrated mass changes for 26 drainage basins and the aggregations of the Antarctic Peninsula, East Antarctica, West Antarctica and the entire AIS. Based on the GMB basin product, ice mass balance estimates, i.e. linear trend in the change in ice mass, were derived for all drainage basins and aggregations.   A gridded GMB product is also available as a separate dataset.Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736", "keywords": ["antarctica", "earth-science>solid-earth>gravity/gravitational-field", "esa-cci", "grace", "grace-fo", "gravimetric-mass-balance-basin-v3.0", "ice-sheet-mass-balance", "orthoimagery"], "license": "other", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance basin product, v3.0, 2002-2020"}, "GRAVIMETRIC_MASS_BALANCE_GRIDDED_V3.0": {"description": "This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci).  The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth\u2019s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden.  (Groh & Horwath, 2021)The GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km\u00b2 covering the entire AIS.  A GMB basin product is also available as a separate dataset.Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736", "keywords": ["antarctica", "earth-science>solid-earth>gravity/gravitational-field", "esa-cci", "grace", "grace-fo", "gravimetric-mass-balance-gridded-v3.0", "ice-sheet-mass-balance", "orthoimagery"], "license": "other", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0,  2002 - 2020"}, "GREENLAND_CALVING_FRONT_LOCATIONS_V3.0": {"description": "The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet, produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change.  The calving front location has been derived by manual delineation using SAR (Synthetic Aperture Radar) data from the  ERS-1/2, Envisat and Sentinel-1 satellites  and  satellite imagery from LANDSAT 5,7,8.    The digitized calving fronts are stored in ESRI vector shape-file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The product was generated by ENVEO (Environmental Earth Observation Information Technology GmbH)", "instruments": ["AMI/SAR", "AMI/SAR", "TM", "ETM", "TIRS", "C-SAR", "C-SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "ers-1", "ers-2", "esa", "etm", "etm+", "greenland", "greenland-calving-front-locations-v3.0", "ice-sheet", "ice-sheets", "landsat-5", "landsat-7", "landsat-8", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b", "tirs", "tm"], "license": "other", "platform": "ERS-1,ERS-2,Landsat-5,Landsat-7,Landsat-8,Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v3.0"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.4": {"description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space.  The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017.   This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin.  For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided.   The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., S\u00f8rensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.", "instruments": ["GRACE LRR"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>solid-earth>gravity/gravitational-field", "esa", "grace", "grace-instrument", "grace-lrr", "greenland", "greenland-gravimetric-mass-balance-dtu-space-v1.4", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "GRACE", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space,  v1.4"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.5": {"description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space.  The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016.   This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin.  For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided.   The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., S\u00f8rensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.", "instruments": ["GRACE LRR"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>solid-earth>gravity/gravitational-field", "esa", "grace", "grace-instrument", "grace-lrr", "greenland", "greenland-gravimetric-mass-balance-dtu-space-v1.5", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "GRACE", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V2.2": {"description": "This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth\u2019s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through August 2021.The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.Two different types of products are available. First, the  gridded mass trends product is comprised of ice mass change trends for cells of equal area with 50 km resolution covering the whole GIS. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS.Reference:Barletta, V. R., S\u00f8rensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.\",", "keywords": ["cci", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "gravimetric-mass-balance", "greenland-gravimetric-mass-balance-dtu-space-v2.2", "greenland-ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V3.0": {"description": "This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth\u2019s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through May 2024.The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.Two different types of products are available. First, the gridded mass trends product is comprised of ice mass change trends for cells of equal area with 44 km resolution covering the whole GIS and different drainage basins. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS over different 5-year periods between 2002 and 2024. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document and the Product Specification Document which are provided on the project website. Reference:Barletta, V. R., S\u00f8rensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.", "keywords": ["cci", "esa", "gravimetric-mass-balance", "greenland-gravimetric-mass-balance-dtu-space-v3.0", "greenland-ice-sheet", "orthoimagery"], "license": "other", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v3.0"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.2": {"description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden.  The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016.   This version (1.2) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016)The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin.  For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided.   The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065", "instruments": ["GRACE LRR"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>solid-earth>gravity/gravitational-field", "esa", "grace", "grace-instrument", "grace-lrr", "greenland", "greenland-gravimetric-mass-balance-tu-dresden-v1.2", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "GRACE", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.2"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.3": {"description": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden.  The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016.   This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin.  For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided.   The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065", "instruments": ["GRACE LRR"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>solid-earth>gravity/gravitational-field", "esa", "grace", "grace-instrument", "grace-lrr", "greenland", "greenland-gravimetric-mass-balance-tu-dresden-v1.3", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "GRACE", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3"}, "GREENLAND_GROUNDING_LINE_LOCATIONS_V1.3": {"description": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 and Sentinel-1 SAR (Synthetic Aperture Radar) interferometry.  This version of the dataset (v1.3) has been extended with grounding lines for 2017. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. The grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.Remote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone).", "instruments": ["AMI/SAR", "AMI/SAR", "C-SAR", "C-SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "ers-1", "ers-2", "esa", "greenland", "greenland-grounding-line-locations-v1.3", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "ERS-1,ERS-2,Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations from SAR Interferometry,  v1.3"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2013_2014_V1.0": {"description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2013-2014, derived from RADARSAT-2 data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.  The ice velocity data were derived from intensity-tracking of RADARSAT-2 data aquired between 21/1/2014 and 02/04/2014. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E).  The horizontal velocity is provided in true meters per day, towards the Eastings and Northings direction of the grid; the vertical displacement, derived from a digital elevation model, is also provided.  Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided.  This product was generated by DTU Space - Microwaves and Remote Sensing.", "instruments": ["SAR"], "keywords": ["dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>terrestrial-hydrosphere>snow/ice>ice-velocity", "greenland", "greenland-ice-velocity-greenland-ice-velocity-map-winter-2013-2014-v1.0", "ice-sheets", "ice-velocity", "orthoimagery", "radarsat-2", "sar", "sar-(radarsat-2)"], "license": "other", "platform": "RADARSAT-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2013-2014, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2014_2015_V1.0": {"description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2014-2015, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.", "instruments": ["C-SAR"], "keywords": ["c-sar", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>terrestrial-hydrosphere>snow/ice>ice-velocity", "greenland", "greenland-ice-velocity-greenland-ice-velocity-map-winter-2014-2015-v1.0", "ice-sheets", "ice-velocity", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a"], "license": "other", "platform": "Sentinel-1A", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2014-2015, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2015_2016_V1.2": {"description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data acquired from 01/10/2015 to 31/10/2016, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.   The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "instruments": ["C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-ice-velocity-map-winter-2015-2016-v1.2", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a"], "license": "other", "platform": "Sentinel-1A", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2015-2016, v1.2"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2016_2017_V1.0": {"description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2016-2017, derived from Sentinel-1 SAR data acquired from 23/12/2016 to 27/02/2017, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.   In total approximately 1800 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided.   The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-ice-velocity-map-winter-2016-2017-v1.0", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2016-2017, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2017_2018_V1.0": {"description": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2017-2018, derived from Sentinel-1 SAR data acquired from 28/12/2017 to 28/02/2018, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.   In total approximately 1900 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz),derived from a digital elevation model, is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-ice-velocity-map-winter-2017-2018-v1.0", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2017-2018, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_CSK_JAKOBSHAVN_V1.0": {"description": "This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014.  The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs.   It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space.  For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0.", "instruments": ["SAR"], "keywords": ["cci", "cosmo-skymed", "csk-1", "csk-2", "csk-4", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-csk-jakobshavn-v1.0", "ice-sheet", "ice-sheets", "orthoimagery", "sar", "sar-2000"], "license": "other", "platform": "COSMO-SkyMed", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_79FJORD_V1.1": {"description": "This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-79fjord-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the 79-Fjord Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HAGEN_V1.1": {"description": "This dataset contains a time series of ice velocities for the Hagen glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-hagen-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HELHEIM_V1.1": {"description": "This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-helheim-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_JAKOBSHAVN_V1.1": {"description": "This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-jakobshavn-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_KANGERLUSSUAQ_V1.1": {"description": "This dataset contains a time series of ice velocity maps for the Kangerlussuag  Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-kangerlussuaq-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq Glacier for 2015-2017 from Sentinel-1, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_PETERMANN_V1.1": {"description": "This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 22/1/2015-19/3/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-petermann-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_STORSTROEMMEN_V1.1": {"description": "This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 24/1/2015 and 22/03/2017.  It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-storstroemmen-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Storstroemmen Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_UPERNAVIK_V1.1": {"description": "This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-upernavik-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_ZACHARIAE_V1.1": {"description": "This dataset contains a time series of ice velocities for the Zachariae  glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "instruments": ["C-SAR", "C-SAR"], "keywords": ["c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-250m-s1-zachariae-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-1b"], "license": "other", "platform": "Sentinel-1A,Sentinel-1B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_79FJORD_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.  The data have been produced by S[&]T Norway", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-79fjord-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_DOCKER_SMITH_V1.0": {"description": "This dataset contains an optical ice velocity time series for the D\u00f8cker Smith Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2016-05-08 and 2016-05-18.  It is part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.   The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.   The product was generated by S[&]T Norway.", "instruments": ["MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-docker-smith-v1.0", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a"], "license": "other", "platform": "Sentinel-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the D\u00c3\u00b8cker Smith Glacier between 2016-05-08 and 2016-05-18, generated using Sentinel-2 data, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HAGEN_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Hagen Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-30 and 2017-08-14.   It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-hagen-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Hagen Glacier between 2017-06-30 and 2017-08-14, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HELHEIM_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Helheim Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-08-29. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-helheim-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Helheim Glacier between 2017-05-01 and 2017-08-29, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_JAKOBSHAVN_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Jakobshavn Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-03 and 2017-09-08.  It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-jakobshavn-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Jakobshavn Glacier between 2017-06-03 and 2017-09-08, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_KANGERDLUGSSUAQ_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-kangerdlugssuaq-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_PETERMANN_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Petermann Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-09-14.  It has been produced as part of the ESA Greenland Ice sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-petermann-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Petermann Glacier between 2017-05-01 and 2017-09-14, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_UPERNAVIK_V1.1": {"description": "This dataset contains optical ice velocity time series and seasonal product of the Upernavik Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-15 and 2017-08-14.   It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The product was generated by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-upernavik-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Upernavik Glacier between 2017-07-15 and 2017-08-14, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_ZACHARIAE_V1.1": {"description": "This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.  The product was generated by S[&]T Norway.", "instruments": ["MSI", "MSI"], "keywords": ["cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland", "greenland-ice-velocity-greenland-iv-50m-s2-zachariae-v1.1", "ice-sheet", "ice-sheets", "msi", "msi-(sentinel-2)", "orthoimagery", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "sentinel-2b"], "license": "other", "platform": "Sentinel-2,Sentinel-2B", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_HAGEN_TIMESERIES_V1.1": {"description": "This dataset contains a time series of ice velocities for the Hagen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 26/08/1991 and 7/5/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used.  The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-hagen-timeseries-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_HELHEIM_TIMESERIES_V1.1": {"description": "This dataset contains a time series of ice velocities for the Helheim glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 29/05/1996 and 26/2/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle of 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-helheim-timeseries-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim glacier from ERS-1, ERS-2 and Envisat data for 1996-2010, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_JAKOBSHAVN_TIMESERIES_V1.2": {"description": "This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010.  It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage.  It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity.  The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-jakobshavn-timeseries-v1.2", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series  for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_KANGERLUSSUAQ_TIMESERIES_V1.0": {"description": "This dataset contains a time series of ice velocities for the Kangerlussuaq glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data aquired between 02/01/1992 and 17/12/2008.  The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing.  The image pairs used have a repeat cycle between 3 and 35 days.  The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "esa", "greenland", "greenland-ice-velocity-greenland-iv-kangerlussuaq-timeseries-v1.0", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq glacier from ERS-1, ERS-2, Envisat for 1992-2008, v1.0"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_PETERMANN_TIMESERIES_V1.1": {"description": "This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-petermann-timeseries-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_STORSTROMMEN_TIMESERIES_V1.1": {"description": "This dataset contains a time series of ice velocities for the Storstrommen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 06/10/1991 and 20/03/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-storstrommen-timeseries-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series of the Storstrommen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_UPERNAVIK_TIMESERIES_V1.2": {"description": "This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010.  The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing.  The image pairs used have a repeat cycle between 1 and 35 days.  The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["PALSAR", "ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["alos", "alos-1", "ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>spectral/engineering>radar", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-upernavik-timeseries-v1.2", "ice-sheet", "ice-sheets", "orthoimagery", "palsar"], "license": "other", "platform": "ALOS-1,Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_ZACHARIAE_79FJORD_TIMESERIES_V1.1": {"description": "This dataset contains a time series of ice velocities for the Zachariae and 79Fjord area in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 01/08/1991 and 07/02/2011. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.  The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "instruments": ["ASAR", "AMI/SAR", "AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "asar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-iv-zachariae-79fjord-timeseries-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae and 79Fjord area from ERS-1, ERS-2 and Envisat data for 1991-2011, v1.1"}, "GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_ERS2_1995_1996_V1.1": {"description": "This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.  The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E).  The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid;  the vertical displacement (z), derived from a digital elevation model, is also provided.  Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided.  The product was generated by DTU Space - Microwaves and Remote Sensing.  For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product.", "instruments": ["AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "ers-2", "esa", "greenland", "greenland-ice-velocity-greenland-margin-ers2-1995-1996-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_PALSAR_TIMESERIES_2006_2011_V1.1": {"description": "This dataset contains a time series of ice velocities for the Greenland margin from the PALSAR instrument on the ALOS satellite. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.     This dataset consists of a time series of ice velocity with yearly sampling, derived from intensity tracking of PALSAR data acquired between 20-12-2016 and 17-03-2011.  It provides components of the ice velocity and the magnitude of the velocity. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E).  The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation  model, is also provided.  Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided.  The product was generated by GEUS.  For further details, please consult the Product User Guide (v2.0)Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016.  Please now use the later v1.1 product.", "instruments": ["PALSAR"], "keywords": ["alos", "alos-1", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "earth-science>spectral/engineering>radar", "esa", "greenland", "greenland-ice-velocity-greenland-margin-palsar-timeseries-2006-2011-v1.1", "ice-sheet", "ice-sheets", "orthoimagery", "palsar"], "license": "other", "platform": "ALOS-1", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from the PALSAR instrument for 2006-2011, v1.1 (June 2016 version)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_NORTHERN_DRAINAGE_BASINS_ERS1_WINTER_1991_1992_V1.1": {"description": "This dataset contains ice velocities for the Greenland Northern Drainage Basin for winter 1991-1992, which have been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.  The data has been derived from intensity-tracking of ERS-1 Ice phase (3 days repeat) data aquired between 29th December 1991 and 22nd March 1992.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevationmodel, is also provided.  (Please note that in earlier versions of this product the horizontal velocities were provided as true East and North velocities).  Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use this later v1.1 product.", "instruments": ["AMI/SAR"], "keywords": ["ami", "ami-sar", "ami/sar", "cci", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "ers-1", "esa", "greenland", "greenland-ice-velocity-greenland-northern-drainage-basins-ers1-winter-1991-1992-v1.1", "ice-sheet", "ice-sheets", "orthoimagery"], "license": "other", "platform": "ERS-1", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Northern Drainage basin from ERS-1 for winter 1991-1992, v1.1 (June 2016 release)"}, "GREENLAND_ML_CALVING_FRONT_LOCATIONS_V1.0": {"description": "Calving Front locations for Upernavik A,E,F, Humboldt and Hagen glaciers in Greenland, generated by a deep learning based model using Sentinel-2 imagery.The calving front location is generated by a deep learning based model using Sentinel-2 imagery acquired from 2019-2020. The digitized calving fronts are stored in geoJSON vector file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The CCI Calving Front Locations (CFL) v1.0 release contains one primary dataset, the calving front locations, and auxiliary files to describe the file product: locations.png and glaciers.geojson for visualizing the glaciers, README and DESCRIPTION text files about the product structure, and a visual example of what a calving front looks like. The Greenland CCI Calving Front Locations (CFL) v1.0 product is an experimental product using deep learning to automatically derive calving front locations for selected glaciers based on Sentinel-2 imagery at the end of the summer season.The product was generated by S[&]T Norway and ENVEO.", "keywords": ["cci", "esa", "greenland", "greenland-ml-calving-front-locations-v1.0", "ice-sheet", "orthoimagery"], "license": "other", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Machine Learning Generated Greenland Calving Front Locations v1.0"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_CRYOSAT2_V2.2": {"description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017. The surface elevation change data  are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors.   Data are provided in both NetCDF and gridded ASCII format, as well as png plots.The algorithm used  to devive the product is described in the paper \u201cImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry\u201d by S.B. Simonsen and L.S. S\u00f8rensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012", "instruments": ["SIRAL"], "keywords": ["cci", "cryosat-2", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland-ice-sheet", "greenland-surface-elevation-change-cryosat2-v2.2", "ice-sheets", "orthoimagery", "siral"], "license": "other", "platform": "CryoSat-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.2"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_SARAL-ALTIKA_V0.1": {"description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from SARAL-AltiKa for 2013-2017. This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite. The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.The corresponding SEC grid from Cryosat-2 is included for comparison. The algorithm used to devive the product is described in the paper \u201cImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry\u201d by S.B. Simonsen and L.S. S\u00f8rensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012.   The approach used here corresponds to Least Squares Method (LSM) 5 described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 20 km.", "instruments": ["SIRAL"], "keywords": ["altika", "cci", "cryosat-2", "dif10", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "esa", "greenland-ice-sheet", "greenland-surface-elevation-change-saral-altika-v0.1", "ice-sheets", "orthoimagery", "saral", "siral"], "license": "other", "platform": "CryoSat-2,SARAL", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change grid from SARAL-AltiKa for 2013-2017, v0.1"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_V1.2": {"description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from satellite (ERS\u20101, ERS\u20102, Envisat and Cryosat) radar altimetry.   The ice mask is based on the GEUS/GST land/ice/ocean mask provided as part of national mapping projects, and based on 1980\u2019s aerial photography. The data from ERS and Envisat are based on a 5\u2010year running average, using combined algorithms of repeat\u2010track (RT), along\u2010track (AT) or cross\u2010over (XO) algorithms, and include propagated error estimates. It is important to note that different processing algorithms were applied to the ERS\u20101, ERS\u20102, Envisat and CryoSat data;  for details see the Product User Guide (PUG), available on the CCI website and in the documentation section here. For ERS\u20101, the radar data were processed using a cross\u2010over algorithm (XO) only. For ERS\u20102 data and Envisat data in repeat mode, a combination of RT and XO algorithms was applied, followed by filtering. For across\u2010mission (i.e. ERS\u20102\u2010Envisat) combinations, and for Envisat operating in a drifting orbit, an AT and XO combination was applied (the difference between RT and AT algorithms is that AT use reference tracks and searches for data in the vicinity of this track). For CryoSat data a binning/gridding and plane fit method has been applied, following by weak filtering (0.05 degree resolution).", "instruments": ["SIRAL", "RA-2", "RA", "RA"], "keywords": ["cci", "cryosat-2", "dif10", "dtu-space", "earth-science>cryosphere>glaciers/ice-sheets>ice-sheets", "envisat", "ers-1", "ers-2", "esa", "greenland-ice-sheet", "greenland-surface-elevation-change-v1.2", "ice-sheets", "orthoimagery", "ra", "ra-2", "siral"], "license": "other", "platform": "CryoSat-2,Envisat,ERS-1,ERS-2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2"}, "GROUNDING_LINE_LOCATIONS_KEY_GLACIERS_V2.0": {"description": "This dataset contains grounding line locations (GLL) for key glaciers in Antarctica,  produced as part of the ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci) project.    The data have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 satellites, acquired between 1994 and 2020.", "keywords": ["antarctica", "cci", "esa", "ground-line-location", "grounding-line-locations-key-glaciers-v2.0", "orthoimagery"], "license": "other", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci):  Grounding line location for key glaciers, Antarctica, 1994-2020, v2.0"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V03.0": {"description": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).Case A: This covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "AVHRR-3", "AVHRR-3", "AVHRR-3", "MODIS"], "keywords": ["aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "envisat", "ground-temperature", "ground-temperature-l4-area4-pp-v03.0", "meris", "modis", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "proba-v", "sar-x", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost Ground Temperature for the Northern Hemisphere, v3.0"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V04.0": {"description": "This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "MODIS", "AVHRR-3", "AVHRR-3", "AVHRR-3"], "keywords": ["aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "envisat", "ground-temperature", "ground-temperature-l4-area4-pp-v04.0", "meris", "modis", "modis-terra", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "proba-v", "sar-x", "spot", "terra"], "license": "other", "platform": "AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_ANTARCTICA": {"description": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m). Case A: It covers Antarctica (south of 60\u00b0S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60\u00b0S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc", "instruments": ["MODIS", "MODIS"], "keywords": ["aqua", "cci", "department-of-geosciences", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>land-surface>frozen-ground>permafrost", "ground-temperature", "ground-temperature-l4-area4-pp-v05.0-antarctica", "level-4", "modis", "orthoimagery", "permafrost", "terra", "university-of-oslo", "year"], "license": "other", "platform": "AQUA,TERRA", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for Antarctica, v5.0"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"description": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m). Case A: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc", "instruments": ["MODIS", "MERIS", "C-SAR", "MSI", "MODIS"], "keywords": ["aqua", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "earth-science>land-surface>frozen-ground>permafrost", "envisat", "ground-temperature", "ground-temperature-l4-area4-pp-v05.0-northern-hemisphere", "level-4", "meris", "modis", "msi", "msi-(sentinel-2)", "orthoimagery", "permafrost", "proba-v", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v5.0"}, "IASI_NH3_L3_V4R_MERGED": {"description": "This long-term dataset contains monthly Level 3 (L3) gridded ammonia (NH3) total column data derived from IASI (Infrared Atmospheric Sounding Interferometer) measurements onboard Metop-A/B/C (Meteorological operational satellite-A/B/C) data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Precursors for aerosols and ozone project. The temporal range of the dataset is October 2007 to June 2025. Full documentation can be found on the project website at https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/.The version number is 4.0.1R. Data are available in NetCDF format.This dataset forms part of the ESA Precursors Climate Change Initiative (Precursors_cci): Precursors IASI NH3 L3 data products v4R. doi:10.5285/463862326d574951bea52527f9df397d. https://dx.doi.org/10.5285/463862326d574951bea52527f9df397d", "keywords": ["aerosol-precursors", "agriculture", "air-quality", "ammonia", "atmosphere", "biomass-burning", "cci", "earth-science>agriculture", "earth-science>agriculture>agricultural-chemicals>fertilizers", "earth-science>atmosphere", "earth-science>atmosphere>air-quality", "earth-science>human-dimensions>environmental-impacts>biomass-burning", "earth-science>human-dimensions>environmental-impacts>industrial-emissions", "earth-science>oceans>ocean-chemistry>ammonia", "esa", "fertilizers", "iasi", "iasi-nh3-l3-v4r-merged", "industrial-emissions", "level-3", "livestock", "nh3", "observation", "orthoimagery", "precursors", "satellite", "total-column"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI NH3 L3 merged product, version 4R"}, "IASI_NH3_L3_V4R_METOP-A": {"description": "This long-term dataset contains monthly Level 3 (L3) gridded ammonia (NH3) total column data derived from IASI (Infrared Atmospheric Sounding Interferometer) measurements onboard Metop-A (Meteorological operational satellite-A) data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Precursors for aerosols and ozone project. The temporal range of this dataset is October 2007 to October 2021. Full documentation can be found at the project website: https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/.The version number is 4.0.1R. Data are available in NetCDF format.This dataset forms part of the ESA Precursors Climate Change Initiative (Precursors_cci): Precursors IASI NH3 L3 data products v4R. doi:10.5285/463862326d574951bea52527f9df397d. https://dx.doi.org/10.5285/463862326d574951bea52527f9df397d", "keywords": ["aerosol-precursors", "agriculture", "air-quality", "ammonia", "atmosphere", "biomass-burning", "cci", "earth-science>agriculture", "earth-science>agriculture>agricultural-chemicals>fertilizers", "earth-science>atmosphere", "earth-science>atmosphere>air-quality", "earth-science>human-dimensions>environmental-impacts>biomass-burning", "earth-science>human-dimensions>environmental-impacts>industrial-emissions", "earth-science>oceans>ocean-chemistry>ammonia", "esa", "fertilizers", "iasi", "iasi-nh3-l3-v4r-metop-a", "industrial-emissions", "level-3", "livestock", "nh3", "observation", "orthoimagery", "precursors", "satellite", "total-column"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-A NH3 L3 product, version 4R"}, "IASI_NH3_L3_V4R_METOP-B": {"description": "This long-term dataset contains monthly Level 3 (L3) gridded ammonia (NH3) total column data derived from IASI (Infrared Atmospheric Sounding Interferometer) measurements onboard Metop-B (Meteorological operational satellite-B) data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Precursors for aerosols and ozone project. The temporal range of this dataset is from March 2013 to June 2025. Full documentation can be found on the project website is https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/.The version number is 4.0.1R. Data are available in NetCDF format.This dataset forms part of the ESA Precursors Climate Change Initiative (Precursors_cci): Precursors IASI NH3 L3 data products v4R. doi:10.5285/463862326d574951bea52527f9df397d. https://dx.doi.org/10.5285/463862326d574951bea52527f9df397d", "keywords": ["aerosol-precursors", "agriculture", "air-quality", "ammonia", "atmosphere", "biomass-burning", "cci", "earth-science>agriculture", "earth-science>agriculture>agricultural-chemicals>fertilizers", "earth-science>atmosphere", "earth-science>atmosphere>air-quality", "earth-science>human-dimensions>environmental-impacts>biomass-burning", "earth-science>human-dimensions>environmental-impacts>industrial-emissions", "earth-science>oceans>ocean-chemistry>ammonia", "esa", "fertilizers", "iasi", "iasi-nh3-l3-v4r-metop-b", "industrial-emissions", "level-3", "livestock", "nh3", "observation", "orthoimagery", "precursors", "satellite", "total-column"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-B NH3 L3 product, version 4R"}, "IASI_NH3_L3_V4R_METOP-C": {"description": "This long-term dataset contains monthly Level 3 (L3) gridded ammonia (NH3) total column data derived from IASI (Infrared Atmospheric Sounding Interferometer) measurements onboard Metop-C (Meteorological operational satellite-C) data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Precursors for aerosols and ozone project. The temporal range of this dataset is from September 2019 to June 2025. Full documentation can be found at the project website https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/.The version number is 4.0.1R. Data are available in NetCDF format.This dataset forms part of the ESA Precursors Climate Change Initiative (Precursors_cci): Precursors IASI NH3 L3 data products v4R.  doi:10.5285/463862326d574951bea52527f9df397d. https://dx.doi.org/10.5285/463862326d574951bea52527f9df397d", "keywords": ["aerosol-precursors", "agriculture", "air-quality", "ammonia", "atmosphere", "biomass-burning", "cci", "earth-science>agriculture", "earth-science>agriculture>agricultural-chemicals>fertilizers", "earth-science>atmosphere", "earth-science>atmosphere>air-quality", "earth-science>human-dimensions>environmental-impacts>biomass-burning", "earth-science>human-dimensions>environmental-impacts>industrial-emissions", "earth-science>oceans>ocean-chemistry>ammonia", "esa", "fertilizers", "iasi", "iasi-nh3-l3-v4r-metop-c", "industrial-emissions", "level-3", "livestock", "nh3", "observation", "orthoimagery", "precursors", "satellite", "total-column"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-C NH3 L3 product, version 4R"}, "ICE_VELOCITY_ANTARCTIC_ICE_SHEET_SENTINEL_1_MONTHLY_V1.0": {"description": "This dataset contains monthly gridded ice velocity maps of the Antarctic Ice Sheet derived from Sentinel-1 data acquired between 2017-01-01 and 2020-08-31.  It was generated by ENVEO, as part of the ESA Antarctic Ice Sheet Climate Change Initiative project (Antarctic_Ice_Sheet_cci).The surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 200m grid spacing in Polar Stereographic projection (EPSG: 3031). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty. The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired within a single month in a monthly averaged product.", "keywords": ["antarctic", "cci", "esa", "ice-sheet-velocity", "ice-velocity-antarctic-ice-sheet-sentinel-1-monthly-v1.0", "orthoimagery"], "license": "other", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1"}, "IIML_GREENLAND_V1_2017": {"description": "The 2017 inventory of ice marginal lakes in Greenland (IIML) has been produced as part of the European Space Agency (ESA) Climate Change Initiative (CCI) in Option 6 of the Glaciers_cci project, and is a product that addresses the terrestrial essential climate variable (ECV) Lakes.The IIML is a comprehensive record of all identified ice marginal lakes across the terrestrial margin of Greenland, detected using remote sensing techniques. The detected lakes are presented as polygon vector features in shapefile format, with coordinates provided in the WGS 1984 UTM Zone 24N projected coordinate system. Ice marginal lakes were identified using three independent remote sensing methods: 1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery; 2) multi-spectral indices classification from Sentinel-2 optical imagery; and 3) sink detection from the ArcticDEM (v3). All data were compiled and filtered in a semi-automated approach, using a modified version of the MEaSUREs GIMP ice mask (https://nsidc.org/data/NSIDC-0714/versions/1) to clip the dataset to within 1 km of the ice margin. Each detected lake was then verified manually. The IIML was collected to better understand the impact of ice marginal lake change on the future sea level budget and the terrestrial and marine landscapes of Greenland, such as its ecosystems and human activities.The IIML is a complete inventory of Greenland, with no absent data.", "keywords": ["esa", "glaciers-cci", "greenland", "ice-marginal-lakes", "iiml-greenland-v1-2017", "orthoimagery"], "license": "other", "title": "ESA Glaciers Climate Change Initiative (Glaciers_cci):  2017 inventory of ice marginal lakes in Greenland (IIML), v1"}, "IND_V2.0": {"description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a number of oceanic indicators of mean sea level changes have been produced from merging satellite altimetry measurements of sea level anomalies.  The oceanic indicators dataset consists of static files covering the whole altimeter period, describing the evolution of the project's monthly sea level anomaly gridded product (see separate dataset record).The oceanic indicators that are provided are: 1) the temporal evolution of the global Mean Sea Level (MSL) DOI: 10.5270/esa-sea_level_cci-IND_MSL_MERGED-1993_2015-v_2.0-201612 ;2) the geographic distribution of Mean Sea Level changes (MSLTR) DOI: 10.5270/esa-sea_level_cci-IND_MSLTR_MERGED-1993_2015-v_2.0-201612 ;3) Maps of the amplitude and phase of the annual cycle (MSLAMPH) DOI: 10.5270/esa-sea_level_cci-IND_MSLAMPH_MERGED-1993_2015-v_2.0-201612.The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following  DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612.When using or referring to the SL_cci products, please mention the associated DOIs and also use the following citation where a detailed description of the SL_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00e8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u20132010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these products please email: info-sealevel@esa-sealevel-cci.org", "instruments": ["RA-2", "RA", "RA", "POSEIDON-2", "SSALT"], "keywords": ["altika", "cci", "centre-national-detudes-spatiales", "collecte-localisation-satellites", "cryosat-2", "cryosat-programme", "dif10", "earth-science>oceans>sea-surface-topography>sea-surface-height", "earth-science>spectral/engineering>radar", "environmental-satellite", "envisat", "ers", "ers-1", "ers-2", "esa", "european-space-agency", "geodetic", "geosat-follow-on-radar-altimeter", "gfo", "gfo-ra", "ind-v2.0", "indicator", "jason", "jason-1", "jason-2", "mean-sea-level-trends", "merged", "month", "orthoimagery", "poseidon-2", "poseidon-3", "ra", "ra-2", "radar-altimeter", "radar-altimeter-2", "saral", "saral-programme", "sea-level", "sea-level-indicators", "single-frequency-solid-state-altimeter", "ssalt", "topex/poseidon"], "license": "other", "platform": "CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Oceanic Indicators of Mean Sea Level Changes,  Version 2.0"}, "L3S_VP_PRODUCTS_V1.0": {"description": "Climate Research Data Package 1 from the ESA Climate Change Initiative Vegetation Parameters Project (Vegetation_parameters_cci). The dataset consists of Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) gridded at 1 km resolution for the period 2000-2020. The dataset is based on data from SPOT4/5-VEGETATION1/2 and PROBA-V as input data.LAI and fAPAR are retrieved using OptiSAIL (see Blessing and Giering, 2021 doi:10.20944/preprints202109.0147.v1). The dataset is processed for a north-south transect from Finland to South-Africa, as well as for a set of globally distributed sites that is representative for all biomes and for those sites where in-situ reference data is available.The temporal resolution of both datasets is 5 days, but is computed using data selected from a symmetric 10-days window. The data are not smoothed in time. The transect is ordered in tiles following the PROBA-V tiling definition. These files contain the fully validated layers of (effective) LAI, fAPAR, their uncertainties and the correlation between both. The sites additionally include the variables Chlorophyll a+b leaf pigment concentration (Cab), the fraction of Chlorophyll Absorbed Photosynthetically Active Radiation (fAPAR_Cab) and Surface Albedo calculated as bi-hemispheric reflectance (BHR) for diffuse illumination with a reference spectrum for spectral broadband intervals visible wavelengths (VIS, 400-700 nm), near-infrared wavelengths (NIR, 700-2500 nm), and for the combined shortwave range (SW, 400-2500 nm), as well as directional-hemispherical reflectance (DHR) for the same spectral broadbands, computed for local solar noon. These additional variables are not validated.Further details about the data, including validation and intercomparison with similar datasets, can be found in the PDF documentation.", "keywords": ["cci", "climate-change", "earth-science>biosphere>vegetation", "earth-science>biosphere>vegetation>photosynthetically-active-radiation>fraction-of-absorbed-photosynthetically-active-radiation-(fapar)", "earth-science>biosphere>vegetation>vegetation-index>leaf-area-index-(lai)", "esa", "fapar", "gcos", "l3s-vp-products-v1.0", "lai", "orthoimagery", "vegetation"], "license": "other", "title": "ESA Vegetation Parameters Climate Change Initiative (Vegetation_Parameters_cci): LAI and fAPAR, Version 1.0"}, "L4_MSLA_V2.0": {"description": "As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a multi-satellite merged time series of monthly gridded Sea Level Anomalies (SLA) has been produced from satellite altimeter measurements. The Sea Level Anomaly grids have been calculated after merging the altimetry mission measurements together into monthly grids, with a spatial resolution of 0.25 degrees.   This version of the product is Version 2.0.  The following DOI can be used to reference the monthly Sea Level Anomaly product: DOI: 10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faug\u00e8re, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993\u20132010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org", "instruments": ["RA-2", "RA", "RA", "POSEIDON-2", "SSALT"], "keywords": ["altika", "cryosat-2", "dif10", "earth-science>oceans>sea-surface-topography>sea-surface-height", "earth-science>spectral/engineering>radar", "envisat", "ers-1", "ers-2", "esa-cci", "gfo", "gfo-ra", "jason-1", "jason-2", "l4-msla-v2.0", "orthoimagery", "poseidon-2", "poseidon-3", "ra", "ra-2", "saral", "sea-level", "sla", "ssalt", "topex/poseidon"], "license": "other", "platform": "CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0"}, "LAKE_PRODUCTS_L3S_V1.0": {"description": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and   functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\u2022\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\u2022\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\u2022\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \u2022\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \u2022\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.", "instruments": ["RA-2", "POSEIDON-2", "AVHRR-3", "OLCI", "OLCI", "SSALT"], "keywords": ["aatsr", "altika", "atsr-2", "avhrr-3", "cci", "dif10", "earth-science>atmosphere", "earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake", "earth-science>spectral/engineering>infrared-wavelengths", "earth-science>spectral/engineering>radar", "ecv", "envisat", "esa", "gfo", "jason-1", "jason-2", "jason-3", "lake-products-l3s-v1.0", "lakes", "meris", "metop-a", "modis", "mss", "olci", "oli", "orbview-2", "orthoimagery", "poseidon-2", "poseidon-3", "ra", "ra-2", "saral", "sentinel-3a", "sentinel-3b", "sral", "ssalt", "tm", "topex/poseidon", "viirs"], "license": "other", "platform": "Envisat,Jason-1,JASON-3,Metop-A,OrbView-2,SARAL,Sentinel-3A,Sentinel-3B,TOPEX/POSEIDON", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.0"}, "LAKE_PRODUCTS_L3S_V1.1": {"description": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and   functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\u2022\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\u2022\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\u2022\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \u2022\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \u2022\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.", "instruments": ["MODIS", "RA-2", "RA", "POSEIDON-2", "TM", "TM", "ETM", "OLI", "AVHRR-3", "AVHRR-3", "SeaWiFS", "C-SAR", "OLCI", "OLCI", "MODIS", "SSALT"], "keywords": ["aatsr", "altika", "aqua", "atsr-2", "avhrr-3", "c-sar", "cci", "collecte-localisation-satellites", "day", "dif10", "earth-science>agriculture>soils", "earth-science>atmosphere", "earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake", "earth-science>spectral/engineering>infrared-wavelengths", "earth-science>spectral/engineering>radar", "ecv", "envisat", "ers-2", "esa", "etm", "etm+", "gfo", "h2o-geomatics", "jason-1", "jason-2", "jason-3", "laboratoire-detudes-en-geodesie-et-oceanographie-spatiales", "lake-products-l3s-v1.1", "lakes", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "level-3", "level-3s", "merged", "meris", "metop-a", "metop-b", "modis", "mss", "multiple-lake-products", "olci", "oli", "orbview-2", "orthoimagery", "plymouth-marine-laboratory", "poseidon-2", "poseidon-3", "ra", "ra-2", "saral", "seawifs", "sentinel-1a", "sentinel-3a", "sentinel-3b", "snpp", "sral", "ssalt", "terra", "tm", "topex/poseidon", "university-of-reading", "viirs"], "license": "other", "platform": "AQUA,Envisat,ERS-2,Jason-1,JASON-3,Landsat-4,Landsat-5,Landsat-7,Landsat-8,Metop-A,Metop-B,OrbView-2,SARAL,Sentinel-1A,Sentinel-3A,Sentinel-3B,TERRA,TOPEX/POSEIDON", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.1"}, "LAKE_PRODUCTS_L3S_V2.0.2": {"description": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset.   The five thematic climate variables included in this dataset are:\u2022 Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\u2022 Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\u2022 Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\u2022 Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\u2022 Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website  and in Carrea, L., Cr\u00e9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z", "keywords": ["cci", "earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake", "ecv", "esa", "lake-products-l3s-v2.0.2", "lakes", "orthoimagery"], "license": "other", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.0.2"}, "LAKE_PRODUCTS_L3S_V2.1": {"description": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.1.0 of the dataset.The six thematic climate variables included in this dataset are:\u2022 Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\u2022 Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\u2022 Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\u2022 Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\u2022 Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\u2022 Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.Satellite sensors associated with the thematic climate variables are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website  and in Carrea, L., Cr\u00e9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z", "instruments": ["ETM", "C-SAR", "MSI"], "keywords": ["c-sar", "cci", "dif10", "earth-science>atmosphere", "earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake", "earth-science>spectral/engineering>radar", "earth-science>spectral/engineering>visible-wavelengths", "ecv", "envisat", "ers", "esa", "etm", "etm+", "jason-1", "jason-2", "jason-3", "lake-products-l3s-v2.1", "lakes", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "metop", "metop-a", "metop-b", "modis-aqua", "modis-terra", "msi", "orthoimagery", "saral", "sentinel-1", "sentinel-2", "sentinel-2-msi", "sentinel-3a", "sentinel-3b", "terra", "topex/poseidon"], "license": "other", "platform": "Envisat,Jason-1,TOPEX/POSEIDON,TERRA,Landsat-8,Landsat-5,JASON-3,Landsat-7,Metop-A,Metop-B,Landsat-4,SARAL,Sentinel-3A,Sentinel-3B,Sentinel-1,Sentinel-2", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.1"}, "LAKE_PRODUCTS_L3S_V3.0": {"description": "This dataset contains the Lakes Essential Climate Variable (ECV) Products, comprised of processed satellite observations at the global scale, over the period 1992-2023, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website.This is version 3.0.0 of the dataset which benefits from longer observation time series, improved spatial coverage, new variables and improved algorithms for certain variables.The Essential Climate Variable Products included in this dataset are:\u2022Lake Water Level (LWL), derived from satellite altimetry, fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\u2022Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at various time-points, describing the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example.\u2022Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, correlated with regional air temperatures and supporting analysis of vertical mixing regimes, biogeochemical cycling and seasonality.\u2022Lake Ice Cover (LIC), determined from optical observations, describing the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\u2022Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, and supporting interpretation of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and serving as an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\u2022Lake Ice Thickness (LIT), provided for 13 lakes from radar altimetry to study the integrity and velocity of lake ice formation. \u2022Lake Storage Change (LSC), derived from Water extent and/or level, describing water volume evolution of water bodies over time to inform studies of water stress. Data generated in the Lakes_cci are derived from over 35 satellite sensors. The following sensors are associated with each of the ECV Products:\u2022LWL: Poseidon-1 (TOPEX/Poseidon), Poseidon-2 (Jason-1),  Poseidon-3 (Jason-2), Poseidon-3B (Jason-3), Poseidon-4 (Sentinel-6A), Radar Altimeter RA-2 (Envisat), AltiKa (SARAL), GFO, SAR Altimeter \u2013 SRAL (Sentinel-3A, Sentinel-3B), Radar Altimeter RA (ERS-1, ERS-2)\u2022LWE: Landsat (4 TM, 5 TM, 7 ETM+, 8 OLI), Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI\u2022LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B\u2022LIC: Terra/Aqua MODIS\u2022LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS\u2022LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B\u2022LSC: All sensors listed under LWL and LWE.Detailed information on the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website, relating to this specific release. A further presentation of the dataset v2 can be found in Carrea, L., Cr\u00e9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z.", "keywords": ["cci", "earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake", "ecv", "esa", "lake-products-l3s-v3.0", "lakes", "orthoimagery"], "license": "other", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 3.0"}, "LAND_COVER_MAPS_A01_AFRICA_HISTORICAL_V1.2_GEOTIFF": {"description": "This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Africa, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.   It consists of the following products:1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty products.They cover the geographic range (3.5\u00b0N \u2013 16.3\u00b0N; 27.0\u00b0E \u2013 43.3\u00b0E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a01-africa-historical-v1.2-geotiff", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Africa (Eastern Sahel region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2"}, "LAND_COVER_MAPS_A01_AFRICA_STATIC_V1.2_GEOTIFF_HRLC10": {"description": "This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Africa, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.  This consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (0.1\u00b0S \u2013 18.1\u00b0N; 9.9\u00b0E \u2013 43.3\u00b0E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a01-africa-static-v1.2-geotiff-hrlc10", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Africa (Eastern Sahel region) at 10m spatial resolution for 2019 in Geotiff format, v1.2"}, "LAND_COVER_MAPS_A02_AMAZONIA_HISTORICAL_V1.2_GEOTIFF": {"description": "This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Amazonia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.   It consists of the following products:1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty products.They cover the geographic range (23.6\u00b0S \u2013 11.7\u00b0S; 46.7\u00b0W \u2013 62.1\u00b0W).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a02-amazonia-historical-v1.2-geotiff", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Amazonia (Eastern Amazonas region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2"}, "LAND_COVER_MAPS_A02_AMAZONIA_STATIC_V1.2_GEOTIFF_HRLC10": {"description": "This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Amazonia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.  It consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (23.6\u00b0S \u2013 0\u00b0S; 42.9\u00b0W \u2013 62.1\u00b0W).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a02-amazonia-static-v1.2-geotiff-hrlc10", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Amazonia (Eastern Amazonas region) at 10m spatial resolution for 2019 in Geotiff format, v1.2"}, "LAND_COVER_MAPS_A03_SIBERIA_HISTORICAL_V1.2_GEOTIFF": {"description": "This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Siberia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.  It consists of the following products:1) HRLC30:  High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty productsThey cover the geographic range (59.4\u00b0N \u2013 73.9\u00b0N, 64.8\u00b0E \u2013 87.4\u00b0E).The data are provided as both GeoTIFF tiles following the Sentinel 2 MGRS tiling scheme and as a GeoTiff format mosaic.    These maps are also referred to as historical maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a03-siberia-historical-v1.2-geotiff", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Siberia (Norther Siberia region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2"}, "LAND_COVER_MAPS_A03_SIBERIA_STATIC_V1.2_GEOTIFF_HRLC10": {"description": "This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Siberia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project.  It consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (51.3\u00b0N \u2013 75.7\u00b0N; 64.4\u00b0E \u2013 93.4\u00b0E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "high-resolution", "land-cover", "land-cover-maps-a03-siberia-static-v1.2-geotiff-hrlc10", "orthoimagery"], "license": "other", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Siberia (Northern Siberia region) at 10m spatial resolution for 2019 in Geotiff format, v1.2"}, "LAND_COVER_MAPS_V2.0.7": {"description": "As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS).  The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps.    Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php .   Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).", "instruments": ["MERIS", "VG1", "VG2"], "keywords": ["cci", "dif10", "earth-science>biosphere>vegetation", "earth-science>land-surface>land-use/land-cover", "envisat", "land-cover", "land-cover-maps-v2.0.7", "meris", "orthoimagery", "proba-v", "spot-4", "spot-5", "vegetation", "vegetation-1", "vegetation-2", "vg1", "vg2", "v\u00e3\u00a9g\u00e3\u00a9tation-p"], "license": "other", "platform": "Envisat,SPOT-4,SPOT-5,PROBA-V", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Land Cover Maps, Version 2.0.7"}, "LIMB_PROFILES_L3_ACE_FTS_SCISAT_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc\u201d contains monthly zonal mean data for ACE in 2008.", "instruments": ["ACE-FTS"], "keywords": ["ace", "ace-fts", "ace-fts-scisat", "atmospheric-chemistry-experiment---fourier-transform-spectrometer", "cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-ace-fts-scisat-monthly-zonal-mean-v0001", "orthoimagery", "ozone", "ozone-limb-profile", "scisat", "scisat-1", "scisat-1/ace"], "license": "other", "platform": "SCISAT-1", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_GOMOS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc\u201d contains monthly zonal mean data for GOMOS in 2008.", "instruments": ["GOMOS"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "environmental-satellite", "envisat", "esa", "global-ozone-monitoring-by-occultation-of-stars", "gomos", "gomos-envisat", "level-3", "limb-profiles-l3-gomos-envisat-monthly-zonal-mean-v0001", "orthoimagery", "ozone", "ozone-limb-profile"], "license": "other", "platform": "Envisat", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_MEGRIDOP_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura. The merged dataset was generated as a contribution to the European Space Agency Climate Change Initiative Ozone project (Ozone_cci).  The period of this merged time series of ozone profiles is from late 2001 until the end of 2022.The monthly mean gridded ozone profiles and deseasonalised anomalies are provided in the altitude range from 10 to 50 km in bins of 10 degree latitude x 20 degree longitude.  For more details please see the associated readme file and Sofieva, V. F., Szel\u0105g, M., Tamminen, J., Kyr\u00f6l\u00e4, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G. P., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M. and Retscher, C.: Measurement report: regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys., 21(9), 6707\u20136720, doi:10.5194/acp-21-6707-2021, 2021", "keywords": ["cci", "climate-change-initiative", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "limb-profiles-l3-megridop-monthly-zonal-mean-v0001", "orthoimagery", "ozone", "profiles"], "license": "other", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001"}, "LIMB_PROFILES_L3_MERGED_MERGED_MONTHLY_ZONAL_MEAN_V0002": {"description": "This dataset consists of a gridded composite of limb ozone profile data, combining data from a range of instruments. The data are  zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean. The merged monthly zonal mean data (MMZM) include merged ozone profiles in 10\u00b0 latitude zones for each month, on the ozone-CCI pressure grid from 250 hPa to 1 hPa, and the parameters which characterize the uncertainty of the merged profiles. In Phase I of the ESA CCI Programme, the dataset has been created for 2 years, 2007 and 2008.The merged monthly zonal mean data are structured into monthly netcdf files with self-explanatory names. For example, the file \u201cESACCI-OZONE-L3-LP-MERGED-MZM-200801-fv0002.nc\u201d contains merged monthly zonal mean data for January 2008. In addition to the variables of the merged data, the profiles from individual instruments with their uncertainty parameters are also included (for the altitude range 250-1 hPa used in data merging).", "keywords": ["cci", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-merged-merged-monthly-zonal-mean-v0002", "merged", "orthoimagery", "ozone", "ozone-limb-profile"], "license": "other", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 2"}, "LIMB_PROFILES_L3_MERGED_MERGED_SEMI_MONTHLY_MEAN_V0002": {"description": "This dataset consists of a gridded composite of limb ozone profile data, combining data from a range of instruments. The Merged Semi-Monthly Mean (MSMM) dataset is created using measurements from limb sensors participating in Ozone_cci project, for years 2007-2008.First, the ozone profiles from individual instruments are averaged in 10\u00b0 x 20\u00b0 latitude-longitude zones over half-month time intervals, and then merged.The merged semi-monthly mean ozone profiles are structured into yearly netcdf files with self-explanatory names. For example, the file \u201cESACCI-OZONE-L3-LP-SMM-2008-fv0002.nc\u201d contains the semi-monthly mean ozone profiles for January 2008. In addition to the variables of the merged data, the profiles from individual instruments with their uncertainty parameters are also included (for the altitude range 250-1 hPa used in data merging).", "keywords": ["cci", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-merged-merged-semi-monthly-mean-v0002", "orthoimagery", "ozone", "ozone-limb-profile", "smm"], "license": "other", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Semi-Monthly Mean Profiles, Version 2"}, "LIMB_PROFILES_L3_MERGED_SAGE_CCI_OMPS_MONTHLY_ZONAL_MEAN_V0002": {"description": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP;  POAM III on SPOT 4 and SAGE III on ISS. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The long-term SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023).The merged SAGE-CCI-OMPS+ dataset consists of deseasonalized anomalies of ozone and ozone concentrations in 10 degree latitude bands from 90S to 90N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to December 2021.", "keywords": ["cci", "climate-change-initiative", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "limb-profiles-l3-merged-sage-cci-omps-monthly-zonal-mean-v0002", "orthoimagery", "ozone", "profiles"], "license": "other", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles, v0002"}, "LIMB_PROFILES_L3_MIPAS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the MIPAS instrument on the ENVISAT satellite. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \"ESACCI-OZONE-L3-LP-MIPAS_ENVISAT-MZM-2008-fv0001.nc\u201c contains monthly zonal mean data for MIPAS in 2008.", "instruments": ["MIPAS"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "environmental-satellite", "envisat", "esa", "level-3", "limb-profiles-l3-mipas-envisat-monthly-zonal-mean-v0001", "michelson-interferometer-for-passive-atmospheric-sounding", "mipas", "mipas-envisat", "orthoimagery", "ozone", "ozone-limb-profile"], "license": "other", "platform": "Envisat", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): MIPAS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_OSIRIS_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc\u201d contains monthly zonal mean data for OSIRIS in 2008.", "instruments": ["OSIRIS"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-osiris-monthly-zonal-mean-v0001", "odin", "optical\u00e2\\xa0spectrograph-and-infrared\u00e2\\xa0imager\u00e2\\xa0system", "orthoimagery", "osiris", "osiris-odin", "ozone", "ozone-limb-profile"], "license": "other", "platform": "ODIN", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_SCIAMACHY_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc\u201d contains monthly zonal mean data for SCIAMACHY in 2008.", "instruments": ["SCIAMACHY"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "environmental-satellite", "envisat", "esa", "level-3", "limb-profiles-l3-sciamachy-envisat-monthly-zonal-mean-v0001", "orthoimagery", "ozone", "ozone-limb-profile", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy", "sciamachy-envisat"], "license": "other", "platform": "Envisat", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_SMR_ODIN_544_6_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR (544.6 GHz) instrument. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-MZM-SMR_ODIN-544_6_GHz-2008-fv0001.nc\u201d contains monthly zonal mean data for ODIN/SMR at 544.6GHz in 2008.", "instruments": ["SMR"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-smr-odin-544-6-monthly-zonal-mean-v0001", "mzm", "odin", "orthoimagery", "ozone", "ozone-limb-profile", "smr", "sub-millimetre-radiometer"], "license": "other", "platform": "ODIN", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR (544.6 GHz) Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LIMB_PROFILES_L3_SMR_ODIN_MONTHLY_ZONAL_MEAN_V0001": {"description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR instrument. The data are zonal mean time series (10\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00b0 latitude zones from 90\u00b0S to 90\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u201cESACCI-OZONE-L3-LP-SMR_ODIN-MZM-2008-fv0001.nc\u201d contains monthly zonal mean data for ODIN/SMR in 2008.", "instruments": ["SMR"], "keywords": ["cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "esa", "level-3", "limb-profiles-l3-smr-odin-monthly-zonal-mean-v0001", "odin", "orthoimagery", "ozone", "ozone-limb-profile", "smr", "smr-odin", "sub-millimetre-radiometer"], "license": "other", "platform": "ODIN", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1"}, "LT_ANALYSIS_L4_V01.1": {"description": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements.  These data cover the period between 09/1991 and 12/2010.The Version 1.1  data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20", "instruments": ["AVHRR-3", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AATSR", "ATSR-2", "ATSR-1", "AVHRR-2", "AVHRR-3", "AVHRR-3"], "keywords": ["aatsr", "advanced-along-track-scanning-radiometer", "advanced-very-high-resolution-radiometer---1", "along-track-scanning-radiometer---1", "along-track-scanning-radiometer---2", "atsr", "atsr-1", "atsr-2", "avhrr", "avhrr-2", "avhrr-3", "cci", "day", "dif10", "earth-science>oceans>ocean-temperature>sea-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "environmental-satellite", "envisat", "ers", "ers-1", "ers-2", "esacci-sst", "level-4", "lt-analysis-l4-v01.1", "metop", "metop-a", "metop-b", "noaa-12", "noaa-14", "noaa-15", "noaa-16", "noaa-17", "noaa-18", "noaa-4th", "noaa-5th", "orthoimagery", "ostia", "sea-surface-temperature", "sea-water-temperature", "sst"], "license": "other", "platform": "NOAA-15,NOAA-12,Metop-A,Metop-B,NOAA-17,Envisat,ERS-2,ERS-1,NOAA-14,NOAA-18,NOAA-16", "title": "ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1"}, "MERGED_CO_V1.0": {"description": "The carbon monoxide (CO) Climate Data Record (CDR) merged product is a new monthly Level 3 CO product developed by merging satellite data from the IASI instrument (on METOP-A, B, and C) and the MOPITT instrument (on TERRA) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.An intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio. The merged dataset includes CO total column monthly 1\u00b0x1\u00b0 resolution grids as well as uncertainty grids, for both daytime and night-time from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.The European Space Agency (ESA) Precursors for Aerosol and Ozone Climate Change Initiative (Precursors CCI) project is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.The version number is 1.0. Data are available in NetCDF format.", "keywords": ["carbon-monoxide", "cci", "cdr", "climate-data-record", "co", "earth-science>atmosphere>air-quality>carbon-monoxide", "iasi", "merged-co-v1.0", "mopitt", "orthoimagery"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): Merged CO product, version 1.0"}, "MERIS_ALAMO_L2_V2.2": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2.   The data have been provided by Hygeos.For further details about these data products please see the linked documentation.", "instruments": ["MERIS"], "keywords": ["aerosol", "aerosol-optical-depth", "cci", "dif10", "earth-science>atmosphere>aerosols", "environmental-satellite", "envisat", "esa", "hygeos", "icare", "imaging-spectrometer", "level-2", "level-2-pre-processing", "medium-spectral-resolution", "meris", "meris-alamo-l2-v2.2", "meris-envisat", "orthoimagery", "satellite-orbit-frequency"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2"}, "MERIS_ALAMO_L3_V2.2": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2.   The data have been provided by Hygeos.For further details about these data products please see the linked documentation.", "instruments": ["MERIS"], "keywords": ["aerosol", "aerosol-optical-depth", "cci", "day", "dif10", "earth-science>atmosphere>aerosols", "environmental-satellite", "envisat", "esa", "hygeos", "icare", "imaging-spectrometer", "level-3", "level-3c", "medium-spectral-resolution", "meris", "meris-alamo-l3-v2.2", "meris-envisat", "month", "orthoimagery"], "license": "other", "platform": "Envisat", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2"}, "METOPA_AVHRR_L3C_0.01_V1.10_DAILY": {"description": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on  the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st March 2007 and ends on 15th November 2021. There are minor interruptions  during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["avhrr-metop-a", "cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "metopa-avhrr-l3c-0.01-v1.10-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10"}, "METOPA_AVHRR_L3C_0.01_V2.00_DAILY": {"description": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st March 2007 and ends on 31st December 2020. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["avhrr-metop-a", "cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "metopa-avhrr-l3c-0.01-v2.00-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 2.00"}, "MS_UVAI_L3_V1.7": {"description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7.  L3 products are provided as daily and monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation.", "instruments": ["OMI", "SCIAMACHY", "GOME", "GOME-2", "GOME-2", "TOMS"], "keywords": ["aerosol", "aura", "cci", "dif10", "earth-science>atmosphere>aerosols", "envisat", "ers-2", "esa", "gome", "gome-2", "metop-a", "metop-b", "ms-uvai-l3-v1.7", "nimbus-7", "omi", "orthoimagery", "sciamachy", "toms"], "license": "other", "platform": "Aura,Envisat,ERS-2,Metop-A,Metop-B,Nimbus-7", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products  from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7"}, "MTSAT_JAMI_L3C_V1.00_MONTHLY": {"description": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The original surface temperatures are generated every 3 hours and in this L3C product are monthly averaged at each time step and distributed on a regular latitude-longitude grid with a resolution of 0.05\u00bax0.05\u00ba. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.The LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "land-surface-temperature", "mtsat", "mtsat-jami-l3c-v1.00-monthly", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci):  Monthly Multi-Functional Transport Satellite (MTSAT) level 3C (L3C) product (2009-2015), version 1.00"}, "MULTISENSOR_IRCDR_L3S_0.01_V2.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "infra-red", "land-surface-temperature", "multisensor-ircdr-l3s-0.01-v2.00-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00"}, "MULTISENSOR_IRCDR_L3S_0.01_V2.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "infra-red", "land-surface-temperature", "multisensor-ircdr-l3s-0.01-v2.00-monthly", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00"}, "MULTISENSOR_IRCDR_L3S_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra), to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.This version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024.   This dataset provides a daily product, and a separate monthly averaged product also exists.  The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include:  SLSTR on Sentinel 3A is no longer used, instead data from  SLSTR on Sentinel 3B is used from November 2018; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "infra-red", "land-surface-temperature", "multisensor-ircdr-l3s-0.01-v3.00-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00"}, "MULTISENSOR_IRCDR_L3S_0.01_V3.00_MONTHLY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR); the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u2013 further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. Full Earth coverage is achieved in 3 days. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.  In this dataset, data has been averaged to a monthly grid.  A separate daily product is also available.Dataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.This version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024. The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include a change from SLSTR on Sentinel 3A to SLSTR on Sentinel 3B; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "infra-red", "land-surface-temperature", "multisensor-ircdr-l3s-0.01-v3.00-monthly", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00"}, "MULTISENSOR_IRMGP_L3S_0.05_V1.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Portugu\u00eas do Mar e da Atmosfera (IPMA) before being merged into the final dataset.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "multisensor-irmgp-l3s-0.05-v1.00-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00"}, "MULTISENSOR_IRMGP_L3S_0.05_V1.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Portugu\u00eas do Mar e da Atmosfera (IPMA) before being merged into the final dataset.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "multisensor-irmgp-l3s-0.05-v1.00-monthly", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00"}, "MULTISENSOR_IRMGP_L3S_0.05_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1 and MTSAT 2, Advanced Himawari Imager (AHI) on Himawari 8 and Himawari 9 ; and polar, Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Along-Track Scanning Radiometer 2 (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2), Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and Sentinel-3B, Advanced Very High Resolution Radiometer (AVHRR) on Metop-A, and Visible Infra-red Imaging Radiometer Suite(VIIRS) on Suomi National Polar-orbiting Partnership (Suomi NPP) . However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.Dataset coverage starts on 24th January 2004 and ends on 31st December 2023.LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.This version of the dataset (Version 3.00) extends the temporal coverage to the end of 2023. An extension of the dataset to the end of 2024 is planned in the future.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "multisensor-irmgp-l3s-0.05-v3.00-daily", "orthoimagery"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product, version 3.00"}, "NADIR_PROFILES_L3_MERGED_V0002": {"description": "This dataset contains Level 3 nadir profile ozone data  from the ESA Ozone Climate Change Initiative (CCI) project.   The Level 3 data are monthly averages on a regular 3D grid derived from level 2 ozone profiles.  In this version 2 of the dataset, data are available for 1997 and 2007 and 2008 only, and use data from the GOME instrument on ERS (1997) and the GOME-2 instrument on METOP-A (2007, 2008).", "instruments": ["GOME-2", "GOME-2", "SCIAMACHY", "GOME", "OMI"], "keywords": ["aura", "cci", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "environmental-satellite", "envisat", "eos", "ers", "ers-2", "esa", "global-monitoring-of-atmospheric-ozone", "global-monitoring-of-atmospheric-ozone---2", "gome", "gome-2", "level-3", "merged", "metop", "metop-a", "metop-b", "month", "nadir-profiles-l3-merged-v0002", "omi", "orthoimagery", "ozone", "ozone-monitoring-instrument", "ozone-nadir-profile", "royal-netherlands-meteorological-institute", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy"], "license": "other", "platform": "Metop-A,Metop-B,Envisat,ERS-2,Aura", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Nadir Ozone Profile Merged Data Product, version 2"}, "NOAA15_AVHRR_L3C_0.05_V1.50_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on NOAA-15. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Separate files are provided for temperatures retrieved from descending and ascending orbits. Descending and ascending equator crossing times were originally 07:30 and 19:30 local solar time but have been allowed to drift.This product includes a time correction which can be added to the observed LST to correct to a consistent overpass time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. AVHRR achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 24th September 1998 and continues to 3rd July 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.", "instruments": ["AVHRR-3"], "keywords": ["avhrr-3", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "noaa-15", "noaa15-avhrr-l3c-0.05-v1.50-daily", "orthoimagery"], "license": "other", "platform": "NOAA-15", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-15  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (1998-2010), version 1.50"}, "NOAA16_AVHRR_L3C_0.05_V1.50_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on NOAA-16. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Separate files are provided for temperatures retrieved from descending and ascending orbits. Descending and ascending equator crossing times were originally 02:00 and 14:00 local solar time but have been allowed to drift.This product includes a time correction which can be added to the observed LST to correct to a consistent overpass time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. AVHRR achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 14th January 2002 and continues to 30th December 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.", "instruments": ["AVHRR-3"], "keywords": ["avhrr-3", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "noaa-16", "noaa16-avhrr-l3c-0.05-v1.50-daily", "orthoimagery"], "license": "other", "platform": "NOAA-16", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-16  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2002-2010), version 1.50"}, "NOAA17_AVHRR_L3C_0.05_V1.50_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on NOAA-17. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Separate files are provided for temperatures retrieved from descending and ascending orbits. Descending and ascending equator crossing times were originally 10:00 and 22:00 local solar time but have been allowed to drift.This product includes a time correction which can be added to the observed LST to correct to a consistent overpass time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. AVHRR achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 10th July 2002 and continues to 14th February 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.", "instruments": ["AVHRR-3"], "keywords": ["avhrr-3", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "noaa-17", "noaa17-avhrr-l3c-0.05-v1.50-daily", "orthoimagery"], "license": "other", "platform": "NOAA-17", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-17  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2002-2010), version 1.50"}, "NOAA18_AVHRR_L3C_0.05_V1.50_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on NOAA-18. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Separate files are provided for temperatures retrieved from descending and ascending orbits. Descending and ascending equator crossing times were originally 01:30 and 13:30 local solar time but have been allowed to drift.This product includes a time correction which can be added to the observed LST to correct to a consistent overpass time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. AVHRR achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 5th June 2005 and continues to 17th September 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.", "instruments": ["AVHRR-3"], "keywords": ["avhrr-3", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "noaa-18", "noaa18-avhrr-l3c-0.05-v1.50-daily", "orthoimagery"], "license": "other", "platform": "NOAA-18", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-18  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2005-2020), version 1.50"}, "NOAA19_AVHRR_L3C_0.05_V1.50_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on NOAA-19. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Separate files are provided for temperatures retrieved from descending and ascending orbits. Descending and ascending equator crossing times were originally 02:00 and 14:00 local solar time but have been allowed to drift.This product includes a time correction which can be added to the observed LST to correct to a consistent overpass time.Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.05\u00b0 longitude and 0.05\u00b0 latitude. AVHRR achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 22nd February 2009 and continues to 17th September 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.", "instruments": ["AVHRR-3"], "keywords": ["avhrr-3", "cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "noaa-18", "noaa19-avhrr-l3c-0.05-v1.50-daily", "orthoimagery"], "license": "other", "platform": "NOAA-18", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-19  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2009-2020), version 1.50"}, "NOAA20_VIIRS": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and nighttime NOAA-20 equator crossing times which are 13:25 and 01:25 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. VIIRS achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 5th January 2018 and continues to 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The European Space Agency (ESA) funded the research and development of software to generate these data (ESA grant reference 4000123553/18/I-NB) in addition to funding the production of the data for 2012 to 2023. The data for 2024 and development of software for the production of the ICDR is funded by the UK Natural Environment Research Council (NERC grant reference number NE/X019071/1 Earth Observation Climate Information Service).", "keywords": ["canopy", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "land-surface-temperature", "noaa20-viirs", "orthoimagery", "soil", "viirs", "visible-infrared-imaging-radiometer-suite"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on NOAA-20  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2018-2024), version 1.00"}, "OBS4MIPS_DWD_ESACCI-CLOUD-ATSR2-AATSR-3-0_MON": {"description": "This dataset provides a version of the Cloud_cci ATSR2-AATSRv3 monthly gridded dataset  in Obs4MIPs format.   The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on measurements taken by the Along-Track  Scanning  Radiometer (ATSR-2)  on-board  the  European  Remote Sensing Satellite -2 (ERS-2),  and by the Advanced  Along-Track  Scanning  Radiometer (AATSR) on-board  the  Environmental Satellite (Envisat).  It contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework.     This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.   Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)", "keywords": ["cci", "cloud", "earth-science>atmosphere>clouds", "obs4mips", "obs4mips-dwd-esacci-cloud-atsr2-aatsr-3-0-mon", "orthoimagery"], "license": "other", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from ATSR2 and AATSR, version 3"}, "OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-AM-3-0_MON": {"description": "This dataset provides a version of the Cloud_cci AVHRR-AMv3 monthly gridded dataset  in Obs4MIPs format.   The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime morning (AM) satellite NOAA-12,-15,-17, and the EUMETSAT Metop-A satellite. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework.    This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.   Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)", "keywords": ["cci", "cloud", "earth-science>atmosphere>clouds", "obs4mips", "obs4mips-dwd-esacci-cloud-avhrr-am-3-0-mon", "orthoimagery"], "license": "other", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-AM), version 3"}, "OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-PM-3-0_MON": {"description": "This dataset provides a version of the Cloud_cci AVHRR-PMv3 monthly gridded dataset  in Obs4MIPs format.   The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime afternoon (PM) satellite NOAA-7,-9,11,-14,-16,-18,-19 satellites. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework.    This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.   Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)", "keywords": ["cci", "cloud", "earth-science>atmosphere>clouds", "obs4mips", "obs4mips-dwd-esacci-cloud-avhrr-pm-3-0-mon", "orthoimagery"], "license": "other", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-PM), version 3"}, "OBS4MIPS_UREADING_ESA-CCI-SST-V2-1_MON_TOS_GN_V20201130": {"description": "This dataset contains monthly 1 degree averages of sea surface temperature data in Obs4MIPS format, from the European Space Agency (ESA)'s Climate Change Initiatve (CCI) Sea Surface Temperature (SST) v2.1 analysis.The data covers the period from 1981-2017, with the data from 1981 to 2016 coming from the Sea Surface Temperature (SST) project of the ESA CCI project. The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).This particular product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", "keywords": ["earth-science>oceans>ocean-temperature>sea-surface-temperature", "esa-climate-change-initiative", "obs4mips-ureading-esa-cci-sst-v2-1-mon-tos-gn-v20201130", "orthoimagery", "sst"], "license": "other", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Obs4MIPS monthly-averaged sea surface temperature data, v2.1"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V03.0": {"description": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).Case A: This covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.  Case B: This covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "AVHRR-3", "AVHRR-3", "AVHRR-3", "MODIS"], "keywords": ["aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "envisat", "meris", "modis", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "permafrost-extent", "permafrost-extent-l4-area4-pp-v03.0", "proba-v", "sar-x", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost extent for the Northern Hemisphere, v3.0"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V04.0": {"description": "This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).  Case A: It covers the Northern Hemisphere (north of 30\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: It covers the Northern Hemisphere (north of 30\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", "instruments": ["MODIS", "MERIS", "MODIS", "AVHRR-3", "AVHRR-3", "AVHRR-3"], "keywords": ["aqua", "asar", "avhrr-3", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "envisat", "meris", "modis", "modis-terra", "noaa-15", "noaa-16", "noaa-17", "orthoimagery", "permafrost", "permafrost-extent", "permafrost-extent-l4-area4-pp-v04.0", "proba-v", "sar-x", "spot", "terra"], "license": "other", "platform": "AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_ANTARCTICA": {"description": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).  Case A: It covers Antarctica (south of 60\u00b0S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60\u00b0S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the yeare.g. ESACCI-PERMAFROST-L4-PFR-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc", "instruments": ["MODIS", "MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>agriculture>soils>permafrost", "earth-science>land-surface>frozen-ground>permafrost", "level-4", "modis", "orthoimagery", "permafrost", "permafrost-extent", "permafrost-extent-l4-area4-pp-v05.0-antarctica", "terra"], "license": "other", "platform": "AQUA,TERRA", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for Antarctica, v5.0"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"description": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).  Case A: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. e.g. ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30\u00b0N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-PFR-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc", "instruments": ["MODIS", "MERIS", "C-SAR", "MSI", "MODIS"], "keywords": ["aqua", "c-sar", "cci", "dif10", "earth-science>agriculture>soils", "earth-science>agriculture>soils>permafrost", "earth-science>biosphere>vegetation", "earth-science>land-surface>frozen-ground>permafrost", "envisat", "level-4", "meris", "modis", "msi", "msi-(sentinel-2)", "orthoimagery", "permafrost", "permafrost-extent", "permafrost-extent-l4-area4-pp-v05.0-northern-hemisphere", "proba-v", "sar-c-(sentinel-1)", "sentinel-1a", "sentinel-2", "sentinel-2-msi", "sentinel-2a", "terra", "vegetation"], "license": "other", "platform": "AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v5.0"}, "PFT_V2.0.81": {"description": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project.   The data provides yearly data, and initially covers the time period from 1992 to 2020.   It is anticipated that the dataset will be updated annually going forward.This version of the data is v2.0.81, which corrects an issue found with a file in v2.0.8.The PFT v2.0.81 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\"Plant Functional Types\u201d (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).All terrestrial zones of the Earth between the parallels 90\u00b0N and 90\u00b0S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778\u00b0, corresponding to ~300 m at the equator and ~200 m in the midlatitudes.    The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.The plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. This dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.This study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci.", "keywords": ["cci", "earth-science>land-surface>land-use/land-cover", "land-cover", "orthoimagery", "pft", "pft-v2.0.81"], "license": "other", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Plant Functional Types (PFT) Dataset, v2.0.81"}, "PHASE-2_L3C_MERIS-AATSR_V2.0": {"description": "The Cloud_cci MERIS+AATSR dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MERIS and AATSR (onboard ENVISAT) measurements and contains a variety of cloud properties which were derived employing the Freie Universit\u00e4t Berlin AATSR MERIS Cloud (FAME-C) retrieval system. The core cloud properties contained in the Cloud_cci MERIS+AATSR dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "instruments": ["AATSR"], "keywords": ["aatsr", "advanced-along-track-scanning-radiometer", "cci", "cloud", "clouds", "dif10", "earth-science>atmosphere>clouds", "environmental-satellite", "envisat", "esa", "freie-universitaet-berlin", "imaging-spectrometer", "level-3", "level-3c", "medium-spectral-resolution", "meris", "month", "multiple-cloud-products", "orthoimagery", "phase-2-l3c-meris-aatsr-v2.0"], "license": "other", "platform": "Envisat", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MERIS+AATSR monthly gridded cloud properties,  Version 2.0"}, "PHASE-2_L3C_MODIS-AQUA_V2.0": {"description": "The Cloud_cci MODIS-Aqua dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Aqua) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Aqua dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "cloud", "clouds", "deutscher-wetterdienst", "dif10", "earth-science>atmosphere>clouds", "eos", "esa", "level-3", "level-3c", "moderate-resolution-imaging-spectroradiometer", "modis", "modis-aqua", "month", "multiple-cloud-products", "orthoimagery", "phase-2-l3c-modis-aqua-v2.0"], "license": "other", "platform": "AQUA", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-AQUA monthly gridded cloud properties, version 2.0"}, "PHASE-2_L3C_MODIS-TERRA_V2.0": {"description": "The Cloud_cci MODIS-Terra dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Terra) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Terra dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.", "instruments": ["MODIS"], "keywords": ["cci", "cloud", "clouds", "deutscher-wetterdienst", "dif10", "earth-science>atmosphere>clouds", "eos", "esa", "level-3", "level-3c", "moderate-resolution-imaging-spectroradiometer", "modis", "modis-terra", "month", "multiple-cloud-products", "orthoimagery", "phase-2-l3c-modis-terra-v2.0", "terra"], "license": "other", "platform": "TERRA", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0"}, "RANDOLPH_GLACIER_INVENTORY_GRIDDED_V5.0": {"description": "The Randolph Glacier Inventory (RGI 5.0) is a global inventory of glacier outlines. It is supplemental to the Global Land Ice Measurements from Space initiative (GLIMS). Production of the RGI was motivated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Future updates will be made to the RGI and the GLIMS Glacier Database in parallel during a transition period. As all these data are incorporated into the GLIMS Glacier Database and as download tools are developed to obtain GLIMS data in the RGI data format, the RGI will evolve into a downloadable subset of GLIMS, offering complete one-time coverage, version control, and a standard set of attributes.The product provided here is a converted raster version of the Randolph Glacier Inventory (RGI 5.0) data, provided by the ESA Climate Change Initiative (CCI) Glaciers project.   The CCI Glaciers project is one of a number of contributors to the RGI 5.0 dataset. For more details, and for a complete list of contributors, please see the RGI 5.0 Technical Report in the Documentation section below.The following reference is recommended when citing RGI version 5.0:Arendt, A., A. Bliss, T. Bolch, J.G. Cogley, A.S. Gardner, J.-O. Hagen, R. Hock, M. Huss, G. Kaser, C. Kienholz, W.T. Pfeffer, G. Moholdt, F. Paul, V. Radi\u0107, L. Andreassen, S. Bajracharya, N.E. Barrand, M. Beedle, E. Berthier, R. Bhambri, I. Brown, E. Burgess, D. Burgess, F. Cawkwell, T. Chinn, L. Copland, B. Davies, H. De Angelis, E. Dolgova, L. Earl, K. Filbert, R. Forester, A.G. Fountain, H. Frey, B. Giffen, N. Glasser, W.Q. Guo, S. Gurney, W. Hagg, D. Hall, U.K. Haritashya, G. Hartmann, C. Helm, S. Herreid, I. Howat, G. Kapustin, T. Khromova, M. K\u00f6nig, J. Kohler, D. Kriegel, S. Kutuzov, I. Lavrentiev, R. LeBris, S.Y. Liu, J. Lund, W. Manley, R. Marti, C. Mayer, E.S. Miles, X. Li, B. Menounos, A. Mercer, N. M\u00f6lg, P. Mool, G. Nosenko, A. Negrete, T. Nuimura, C. Nuth, R. Pettersson, A. Racoviteanu, R. Ranzi, P. Rastner, F. Rau, B. Raup, J. Rich, H. Rott, A. Sakai, C. Schneider, Y. Seliverstov, M. Sharp, O. Sigur\u00f0sson, C. Stokes, R.G. Way, R. Wheate, S. Winsvold, G. Wolken, F. Wyatt, N. Zheltyhina, 2015, Randolph Glacier Inventory \u2013 A Dataset of Global Glacier Outlines: Version 5.0. Global Land Ice Measurements from Space, Boulder Colorado, USA. Digital Media.", "keywords": ["cci", "earth-science>terrestrial-hydrosphere>glaciers/ice-sheets>glaciers", "esa", "glaciers", "orthoimagery", "randolph-glacier-inventory-gridded-v5.0", "rgi"], "license": "other", "title": "ESA Glaciers Climate Change Initiative (Glaciers CCI): Randolph Glacier Inventory gridded data product, v5.0"}, "RD_RD-ALTI_V1.0": {"description": "This dataset comprises the altimetry-based river discharge (RD-ALTI) Climate Research Data Package (CRDP), derived from nadir radar altimeter missions by the ESA CCI River Discharge precursor project (RD_cci). It provides long-term satellite river discharge (RD) time series at specified locations (defined in the \"Selection of river basins\" document, available at https://climate.esa.int/documents/2189/D2_CCI-Discharge-0004-RP_WP2_v1-1.pdf). River discharge (in m3/s) corresponds to the water volume passing through the river cross-section per unit of time. In this dataset, it is computed from a rating curve applied to long-term satellite altimeter water surface elevation (WSE) from https://catalogue.ceda.ac.uk/uuid/c5f0aa806ec444b4a4209b49efc4bb65. The rating curve is obtained by fitting the relationship between in-situ discharge and altimeter WSE with a power law following a Bayesian approach.", "keywords": ["altimeter", "cci", "orthoimagery", "rd-rd-alti-v1.0", "river-discharge"], "license": "other", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Altimetry-based River Discharge product, v1.0"}, "RD_RD-COMBINED_V1.0": {"description": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci).These river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series (called CM) and the water level product derived by radar altimeters (called Altimetry),  and Level-3, in which the river discharge products obtained from altimeters and multispectral sensors are used. The river discharges are derived following several approaches:1) L2 Merged river discharge:a) COPULA Altimetry \u2013 CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution. b) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry2) L3 Merged river discharge:a) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approachb) Altimetry \u2013 CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approachc) Altimetry \u2013 CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach", "keywords": ["cci", "orthoimagery", "rd-rd-combined-v1.0", "river-discharge"], "license": "other", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Combined river discharge product, v1.0"}, "RD_RD-MULTI_V1.2": {"description": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci).  These river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series.  These multi-mission, multispectral time series are also referred to as CM.  The river discharges are derived following several approaches:Calibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;Calibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;Uncalibrated CM approach \u2013 CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.", "keywords": ["cci", "multispectral", "orthoimagery", "rd-rd-multi-v1.2", "river-discharge"], "license": "other", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2"}, "SCFG_AATSR_V1.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (\u201ctransmissivity\u201d) of the forest canopy. The SCFG is given in percentage (%) per grid cell. The global SCFG product is available at 0.01\u00b0 grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2002 \u2013 2012. The SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Mets\u00e4m\u00e4ki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 \u00b5m, and an emissive band centred at about 10.85 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-aatsr-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 snow on ground (SCFG) from AATSR (2002 \u00e2\u0080\u0093 2012), version 1.0"}, "SCFG_ATSR-2_V1.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (\u201ctransmissivity\u201d) of the forest canopy. The SCFG is given in percentage (%) per grid cell. The global SCFG product is available at 0.01\u00b0 grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 1995 \u2013 2003. The SCFG product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. The retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Mets\u00e4m\u00e4ki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 \u00b5m, and an emissive band centred at about 10.85 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-atsr-2-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 snow on ground (SCFG) from ATSR-2 (1995 \u00e2\u0080\u0093 2003), version 1.0"}, "SCFG_AVHRR_MERGED_V2.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-avhrr-merged-v2.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0"}, "SCFG_AVHRR_SINGLE_V3.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1979-2022. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises a few data gaps in 1979 \u2013 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March \u2013 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-avhrr-single-v3.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0"}, "SCFG_AVHRR_SINGLE_V4.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information. The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises a few data gaps in 1979 \u2013 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March \u2013 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "instruments": ["AVHRR-3", "AVHRR-3", "AVHRR", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR", "AVHRR-2", "AVHRR", "AVHRR-2", "TIROS-N"], "keywords": ["avhrr", "avhrr-2", "avhrr-3", "cci", "dif10", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "level-3c", "metop-a", "metop-b", "metop-c", "noaa-10", "noaa-11", "noaa-12", "noaa-14", "noaa-16", "noaa-17", "noaa-18", "noaa-6", "noaa-7", "noaa-8", "noaa-9", "orthoimagery", "scfg-avhrr-single-v4.0", "snow", "snow-cover-fraction", "tiros-n"], "license": "other", "platform": "Metop-A,Metop-B,Metop-C,NOAA-10,NOAA-11,NOAA-12,NOAA-14,NOAA-16,NOAA-17,NOAA-18,NOAA-6,NOAA-7,NOAA-8,NOAA-9,TIROS-N", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0"}, "SCFG_CRYOCLIM_V1.0": {"description": "This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05\u00b0 grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 \u2013 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).", "instruments": ["AVHRR", "AVHRR", "SMMR"], "keywords": ["avhrr", "cci", "dif10", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "earth-science>cryosphere>snow/ice>snow-cover", "earth-science>spectral/engineering>microwave", "esa", "metop-a", "metop-b", "nimbus-7", "orthoimagery", "scfg-cryoclim-v1.0", "sensor-fusion", "smmr", "snow", "snow-cover", "snow-cover-fraction", "ssm/i", "ssmis"], "license": "other", "platform": "Metop-A,Metop-B,Nimbus-7", "title": "ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0"}, "SCFG_MODIS_V2.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 \u2013 2020. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m.   The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Mets\u00e4m\u00e4ki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.Improvements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 \u00b5m.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-modis-v2.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0"}, "SCFG_MODIS_V3.0": {"description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 \u2013 2022. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Mets\u00e4m\u00e4ki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.Compared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/), the following improvements were applied for the generation of the SCFG CRDP v3.0: 1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; 2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;5) the time series, starting in February 2000, was extended from December 2020 to December 2022;6) two additional layers are provided for each daily product: \u2022\tthe sensor zenith angle in degree per pixel;\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel. The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-modis-v3.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0"}, "SCFG_MODIS_V4.0": {"description": "This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project.SCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel.The SCFG product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series spans 24 February 2000 to 31 December 2023.The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Mets\u00e4m\u00e4ki et al., 2015).  For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFG retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask.  Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.Compared with SCFG CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a/), the SCFG CRDP v4.0 includes the following improvements: \u2022\tmore permissive pre-classification allowing more pixels to enter the SCFG retrieval; \u2022\tcorrection function applied to spectral reflectance for improved SCFG retrieval at low solar illumination conditions;\u2022\tupdated spectral reflectance layers for snow free ground and snow free forest to improve SCFG retrieval;\u2022\tupdated uncertainty estimation to account for the changes in the SCFG retrieval;\u2022\timproved merging method for generating daily global SCFG products;\u2022\tupdated salt lake mask;\u2022\textended time series, to December 2023.There are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFG products are provided for all of these days, but they contain data gaps.The SCFG product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.ENVEO leads the SCFG product development and product generation from MODIS data, with contributions on the product development from Syke.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-modis-v4.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2023), version 4.0"}, "SCFG_SLSTR_V1.0": {"description": "This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.SCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel. The SCFG product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards. The SCFG product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFG product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Mets\u00e4m\u00e4ki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFG retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.The retrieval approach used for the SLSTR based SCFG CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFG CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 \u2013 2023 (https://catalogue.ceda.ac.uk/uuid/ 375ffdb8f0a445e380b4b9548655f5f9).The SCFG product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.ENVEO leads the SCFG product development and product generation from SLSTR data, with contributions on the product development from Syke.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfg-slstr-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0"}, "SCFV_AATSR_V1.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at 0.01\u00b0 grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2002 \u2013 2012. The SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Mets\u00e4m\u00e4ki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 \u00b5m, and an emissive band centred at about 10.85 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-aatsr-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 viewable snow (SCFV) from AATSR (2002 \u00e2\u0080\u0093 2012), version 1.0"}, "SCFV_ATSR-2_V1.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel.The global SCFV product is available at 0.01\u00b0 grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1995 \u2013 2003. The SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Mets\u00e4m\u00e4ki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 \u00b5m, and an emissive band centred at about 10.85 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-atsr-2-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 viewable snow (SCFV) from ATSR-2 (1995 \u00e2\u0080\u0093 2003), version 1.0"}, "SCFV_AVHRR_MERGED_V2.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied.  Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-avhrr-merged-v2.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0"}, "SCFV_AVHRR_SINGLE_V3.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1979-2022. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied.  Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises a few data gaps in 1979 \u2013 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March \u2013 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-avhrr-single-v3.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0"}, "SCFV_AVHRR_SINGLE_V4.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 \u00b5m and 1.61 \u00b5m (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 \u00b5m. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. RMSE is retrieved from a statistical model and added as pixel-wise information.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises a few data gaps in 1979 \u2013 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March \u2013 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.", "instruments": ["AVHRR-3", "AVHRR-3", "AVHRR", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR", "AVHRR-2", "AVHRR", "AVHRR-2", "TIROS-N"], "keywords": ["avhrr", "avhrr-2", "avhrr-3", "cci", "dif10", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "level-3c", "metop-a", "metop-b", "metop-c", "noaa-10", "noaa-11", "noaa-12", "noaa-14", "noaa-16", "noaa-17", "noaa-18", "noaa-19", "noaa-6", "noaa-7", "noaa-8", "noaa-9", "orthoimagery", "scfv-avhrr-single-v4.0", "snow", "snow-cover-fraction", "tiros-n"], "license": "other", "platform": "Metop-A,Metop-B,Metop-C,NOAA-10,NOAA-11,NOAA-12,NOAA-14,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-6,NOAA-7,NOAA-8,NOAA-9,TIROS-N", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2023), version 4.0"}, "SCFV_MODIS_V2.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 \u2013 2020. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Mets\u00e4m\u00e4ki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.Improvements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 \u00b5m.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-modis-v2.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2020), version 2.0"}, "SCFV_MODIS_V3.0": {"description": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 \u2013 2022. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00e4m\u00e4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00e4m\u00e4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Mets\u00e4m\u00e4ki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.Compared to the SCFV CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b/), the following improvements were applied for the generation of the SCFV CRDP v3.0: 1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; 2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;5) the time series, starting in February 2000, was extended from December 2020 to December 2022;6) two additional layers are provided for each daily product: \u2022\tthe sensor zenith angle in degree per pixel;\u2022\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-modis-v3.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0"}, "SCFV_MODIS_V4.0": {"description": "This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project.SCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel.This SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 24 February 2000 to 31 December 2023.This SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Mets\u00e4m\u00e4ki et al., 2015).   For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask.    Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.Compared with SCFV CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3/), the SCFV CRDP v4.0 includes the following improvements: \u2022\tmore permissive pre-classification allowing more pixels to enter the SCFV retrieval; \u2022\tcorrection function applied to spectral reflectance for improved SCFV retrieval at low solar illumination conditions;\u2022\tupdated spectral reflectance layers for snow free ground and snow free forest to improve SCFV retrieval;\u2022\tupdated uncertainty estimation to account for the changes in the SCFV retrieval;\u2022\timproved merging method for generating daily global SCFV products;\u2022\tupdated salt lake mask;\u2022\textended time series, to December 2023.There are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFV products are provided for all of these days, but they contain data gaps.The SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.ENVEO leads the SCFV product development and product generation from MODIS data, with contributions on the product development from Syke.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-modis-v4.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2023), version 4.0"}, "SCFV_SLSTR_V1.0": {"description": "This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.SCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel. The SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards.The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Mets\u00e4m\u00e4ki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 \u00b5m and 1.6 \u00b5m, and an emissive band centred at about 11 \u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.The retrieval approach used for the SLSTR based SCFV CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFV CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 \u2013 2023 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/).The SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology. ENVEO leads the SCFV product development and product generation from SLSTR data, with contributions on the product development from Syke.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "scfv-slstr-v1.0", "snow", "snow-cover-fraction"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0"}, "SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.0": {"description": "This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the first version of the product, v1.0.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.", "keywords": ["antarctic", "arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-concentration-l3c-esmr-25km-v1.0"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.0"}, "SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.1": {"description": "This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the second version of the product, v1.1.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.", "keywords": ["antarctic", "arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-concentration-l3c-esmr-25km-v1.1"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.1"}, "SEA_ICE_CONCENTRATION_L4_AMSR_25KM_V2.1": {"description": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2).  It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing.   This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 50 km grid spacing is also available.", "instruments": ["AMSR-E", "AMSR2"], "keywords": ["advanced-microwave-scanning-radiometer-2", "advanced-microwave-scanning-radiometer-for-earth-observation-from-space-(amsr-e)", "amsr-25kmease2", "amsr-e", "amsr2", "amsre", "antarctic", "aqua", "arctic", "cci", "day", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "earth-science>oceans>sea-ice>sea-ice-concentration", "earth-science>spectral/engineering>microwave", "eos", "esa", "gcom", "gcom-w1", "level-4", "norwegian-meteorological-institute", "orthoimagery", "sea-ice", "sea-ice-concentration", "sea-ice-concentration-l4-amsr-25km-v2.1"], "license": "other", "platform": "AQUA,GCOM-W1", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1"}, "SEA_ICE_CONCENTRATION_L4_AMSR_50KM_V2.1": {"description": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2).  It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 25km grid spacing is also available.", "instruments": ["AMSR-E", "AMSR2"], "keywords": ["advanced-microwave-scanning-radiometer-2", "advanced-microwave-scanning-radiometer-for-earth-observation-from-space-(amsr-e)", "amsr-50kmease2", "amsr-e", "amsr2", "amsre", "antarctic", "aqua", "arctic", "cci", "day", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "earth-science>oceans>sea-ice>sea-ice-concentration", "earth-science>spectral/engineering>microwave", "eos", "esa", "gcom", "gcom-w1", "level-4", "norwegian-meteorological-institute", "orthoimagery", "sea-ice", "sea-ice-concentration", "sea-ice-concentration-l4-amsr-50km-v2.1"], "license": "other", "platform": "AQUA,GCOM-W1", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50km grid spacing, version 2.1"}, "SEA_ICE_CONCENTRATION_L4_SSMI_SSMIS_12.5KM_V3.0": {"description": "This climate data record of sea ice concentration (SIC) is obtained using passive microwave satellite data from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90 GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the EUMETSAT Ocean and Sea Ice Satellite Application Facility Global Sea Ice Concentration Climate Data Record (OSI SAF OSI-450-a CDR) over the period 1991-2020.", "keywords": ["antarctic", "arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-concentration-l4-ssmi-ssmis-12.5km-v3.0"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite.   This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.", "instruments": ["SIRAL"], "keywords": ["arctic", "cci", "cryosat-2", "cryosat-programme", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "esa", "level-2", "level-2-pre-processing", "orthoimagery", "satellite-orbit-frequency", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l2p-cryosat2-v2.0-nh", "siral"], "license": "other", "platform": "CryoSat-2", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from CryoSat-2   on the satellite swath (L2P), v2.0"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.  Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "instruments": ["SIRAL"], "keywords": ["antarctic", "cci", "cryosat-2", "cryosat-programme", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "esa", "level-2", "level-2-pre-processing", "orthoimagery", "satellite-orbit-frequency", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l2p-cryosat2-v2.0-sh", "siral"], "license": "other", "platform": "CryoSat-2", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite.   This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-cryosat2-v3.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from CryoSat-2   on the satellite swath (L2P), v3.0"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020.  Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["antarctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-cryosat2-v3.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2010 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-cryosat2-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2024. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-cryosat2-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.", "instruments": ["RA-2"], "keywords": ["arctic", "cci", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "environmental-satellite", "envisat", "esa", "level-2", "level-2-pre-processing", "orthoimagery", "ra-2", "radar-altimeter-2", "satellite-orbit-frequency", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l2p-envisat-v2.0-nh"], "license": "other", "platform": "Envisat", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.   Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "instruments": ["RA-2"], "keywords": ["antarctic", "cci", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "environmental-satellite", "envisat", "esa", "level-2", "level-2-pre-processing", "orthoimagery", "ra-2", "radar-altimeter-2", "satellite-orbit-frequency", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l2p-envisat-v2.0-sh"], "license": "other", "platform": "Envisat", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-envisat-v3.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.   Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["antarctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-envisat-v3.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-envisat-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-envisat-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_ERS2_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA (Radar Altimeter) instrument on the ERS-2 satellite (European Remote-sensing Satellite - 2). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 1995 to April 2003.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-ers2-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from ERS-2 on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3A_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel-3A satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2016 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-sentinel3a-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3A on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3A_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Southern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel-3A satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2016 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-sentinel3a-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3A on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3B_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the from the Sentinel-3B satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2018 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-sentinel3b-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3B on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3B_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the from the Sentinel-3B satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2018 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l2p-sentinel3b-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3B on the satellite swath (L2P), v4.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April.", "instruments": ["SIRAL"], "keywords": ["arctic", "cci", "cryosat-2", "cryosat-programme", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "esa", "level-3", "level-3c", "month", "orthoimagery", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l3c-cryosat2-v2.0-nh", "siral"], "license": "other", "platform": "CryoSat-2", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "instruments": ["SIRAL"], "keywords": ["antarctic", "cci", "cryosat-2", "cryosat-programme", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "esa", "level-3", "level-3c", "month", "orthoimagery", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l3c-cryosat2-v2.0-sh", "siral"], "license": "other", "platform": "CryoSat-2", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Data are only available for the NH winter months, October - April.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-cryosat2-v3.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["antarctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-cryosat2-v3.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2010 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-cryosat2-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period November 2010 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-cryosat2-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April.", "instruments": ["RA-2"], "keywords": ["arctic", "cci", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "environmental-satellite", "envisat", "esa", "level-3", "level-3c", "month", "orthoimagery", "ra-2", "radar-altimeter-2", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l3c-envisat-v2.0-nh"], "license": "other", "platform": "Envisat", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "instruments": ["RA-2"], "keywords": ["antarctic", "cci", "dif10", "earth-science>cryosphere>sea-ice", "earth-science>oceans>sea-ice", "environmental-satellite", "envisat", "esa", "level-3", "level-3c", "month", "orthoimagery", "ra-2", "radar-altimeter-2", "sea-ice", "sea-ice-thickness", "sea-ice-thickness-l3c-envisat-v2.0-sh"], "license": "other", "platform": "Envisat", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-envisat-v3.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "keywords": ["antarctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-envisat-v3.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2002 to March 2012.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-envisat-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2002 to March 2012.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-envisat-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_ERS2_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA (Radar Altimeter) instrument on the ERS-2 satellite (European Remote-sensing Satellite - 2). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 1995 to April 2003.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-ers2-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from ERS-2 on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3A_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel -3A satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2016 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-sentinel3a-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3A on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3A_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Southern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel -3A satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2016 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-sentinel3a-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3A on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3B_V4.0_NH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel -3B satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period November 2018 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-sentinel3b-v4.0-nh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3B on a monthly grid (L3C), v4.0"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3B_V4.0_SH": {"description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Southern Hemisphere polar region, derived from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel -3B satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly sea ice thickness data on the satellite measurement grid (Level 3C) at the full sensor resolution for the period November 2018 to April 2024.", "keywords": ["arctic", "cci", "earth-science>cryosphere>sea-ice", "esa", "orthoimagery", "sea-ice", "sea-ice-thickness-l3c-sentinel3b-v4.0-sh"], "license": "other", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3B on a monthly grid (L3C), v4.0"}, "SENTINEL3A_SLSTR_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3a-slstr-l3c-0.01-v3.00-daily", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00"}, "SENTINEL3A_SLSTR_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3a-slstr-l3c-0.01-v3.00-monthly", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00"}, "SENTINEL3A_SLSTR_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.In Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.An extended version of this dataset is also provided through the EOCIS project.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3a-slstr-l3c-0.01-v4.00-daily", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00"}, "SENTINEL3B_SLSTR_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3b-slstr-l3c-0.01-v3.00-daily", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00"}, "SENTINEL3B_SLSTR_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3b-slstr-l3c-0.01-v3.00-monthly", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00"}, "SENTINEL3B_SLSTR_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.In Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.An extended version of this dataset is also provided through the EOCIS project.", "instruments": ["SLSTR"], "keywords": ["cci", "dif10", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "sentinel-3", "sentinel3b-slstr-l3c-0.01-v4.00-daily", "slstr"], "license": "other", "platform": "Sentinel-3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00"}, "SNPP_VIIRS": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening SNPP equator crossing times which are 13:25 and 01:25 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. VIIRS achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 19th January 2012 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The European Space Agency (ESA) funded the research and development of software to generate these data (ESA grant reference 4000123553/18/I-NB) in addition to funding the production of the data for 2012 to 2023. The data for 2024 and development of software for the production of the ICDR is funded by the UK Natural Environment Research Council (NERC grant reference number NE/X019071/1 Earth Observation Climate Information Service).", "keywords": ["canopy", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "earth-science>spectral/engineering>infrared-wavelengths", "land-surface-temperature", "orthoimagery", "snpp-viirs", "soil", "viirs", "visible-infrared-imaging-radiometer-suite"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on Suomi National Polar-orbiting Partnership (SNPP), level 3 collated (L3C) global product (2012-2024), version 1.00"}, "SSMI_SSMIS_L3C_V2.33": {"description": "MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci).   Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.This version of the data is v2.33.   It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "land-surface-temperature", "orthoimagery", "ssmi-ssmis-l3c-v2.33"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33"}, "SWE_MERGED_V2.0": {"description": "This dataset contains v2.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2020/05. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration\u2019s (NASA) Nimbus-7 satellite, the  Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km.  The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2020, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.This version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset  (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR)  https://nsidc.org/pmesdr/data-sets/), the grid spacing is reduced from 25 km to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added.The ESA CCI phased product development framework allowed for a systematic analysis of these changes to the input data and snow density parameterization that occurred between v1 and v2 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v2 improved 18% (0.1) and 12% (5mm), respectively, relative to v1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is removed in v2.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "snow", "swe", "swe-merged-v2.0"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP)  (1979 \u00e2\u0080\u0093 2020), version 2.0"}, "SWE_MERGED_V3.1": {"description": "This dataset contains v3.1 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2022/05 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October \u2013 May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland. The product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/), resampled to the 12.5km EASE-Grid 2.0. A background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC). Changes from v2.0 and v3.0v3.1 applies spatially and temporally varying snow densities within the SWE retrieval instead of during post-processing. The dry snow detection algorithm as well as the snow masking in post-production have also been updated. The time series has been extended from snow_cci version 2 by two years from 2020 to 2022. In comparison with in situ snow courses, the correlation and RMSE of v3.1 improved by 0.014 and 0.6 mm, respectively, relative to v2.0. The timing of peak snow mass is shifted two weeks later compared to v1.0 and reduction in peak snow mass presented in v2.0 is removed in v3.1. Differences between v3.0 and v.3.1 are minor, the resampling from 12.5km EASE-Grid 2.0 to the final 0.1 resolution grid has been changed for v.3.1 resulting in improved peak snow mass estimation.", "keywords": ["cci", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "orthoimagery", "snow", "swe", "swe-merged-v3.1"], "license": "other", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.1"}, "SWE_MERGED_V4.0": {"description": "This dataset contains v4.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2023/12 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October \u2013 May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland.The product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/), resampled to the 12.5km EASE-Grid 2.0.A background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute (FMI) is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC).Changes from v3.1 The time series has been extended from version 3.1 by one year, to 2023. The retrieval algorithm has been modified to prioritize morning overpass (descending) data over evening (ascending) data. This change affects the SWE retrieval for all years except 1988\u20131991. Data from those years is from the F08 satellite, which has a reversed orbit, and evening (descending) data is prioritized, as in earlier versions of the SWE retrieval. Snow masking in post-production now uses CryoClim SCE data for 35\u201340\u00b0 latitude and \u221230\u20133\u00b0 longitude. Elsewhere, the baseline snow mask and CryoClim are combined so that any pixel flagged by either is marked snow-covered, as in v3.1. The pixel-wise uncertainty model has been updated for North America using extensive snow course data.", "instruments": ["SSM/I", "SSM/I", "SSM/I", "SMMR"], "keywords": ["cci", "dif10", "dmsp-5d-2/f11", "dmsp-5d-2/f13", "dmsp-5d-2/f8", "dmsp-5d-3/f17", "dmsp-5d-3/f18", "dmsp-f08", "dmsp-f11", "dmsp-f13", "dmsp-f17", "dmsp-f18", "earth-science>climate-indicators>cryospheric-indicators>snow-cover", "esa", "level-3c", "merged", "nimbus-7", "orthoimagery", "smmr", "snow", "ssm/i", "ssmi/s", "swe", "swe-merged-v4.0"], "license": "other", "platform": "DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,DMSP 5D-3/F17,DMSP 5D-3/F18,Nimbus-7", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2023), version 4.0"}, "TCWV-LAND_L3_V3.2_0.05DEG_DAILY": {"description": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments.   It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1  (TCWV-land (CDR-1).This version of the data is v3.2.  This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.", "keywords": ["cci", "earth-science>atmosphere>atmospheric-water-vapor", "orthoimagery", "tcwv", "tcwv-land-l3-v3.2-0.05deg-daily", "water-vapour"], "license": "other", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.2"}, "TCWV-LAND_L3_V3.2_0.05DEG_MONTHLY": {"description": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments.   It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1  (TCWV-land (CDR-1).This version of the data is v3.2.  This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.", "keywords": ["cci", "earth-science>atmosphere>atmospheric-water-vapor", "orthoimagery", "tcwv", "tcwv-land-l3-v3.2-0.05deg-monthly", "water-vapour"], "license": "other", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.05 degree resolution, version 3.2"}, "TCWV-LAND_L3_V3.2_0.5DEG_DAILY": {"description": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments.   It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1  (TCWV-land (CDR-1).This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.", "keywords": ["cci", "earth-science>atmosphere>atmospheric-water-vapor", "orthoimagery", "tcwv", "tcwv-land-l3-v3.2-0.5deg-daily", "water-vapour"], "license": "other", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.5 degree resolution, version 3.2"}, "TCWV-LAND_L3_V3.2_0.5DEG_MONTHLY": {"description": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).This version of the data is v3.2.  This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.", "keywords": ["cci", "earth-science>atmosphere>atmospheric-water-vapor", "orthoimagery", "tcwv", "tcwv-land-l3-v3.2-0.5deg-monthly", "water-vapour"], "license": "other", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.5 degree resolution, version 3.2"}, "TERRA_MODIS_L3C_0.01_V3.00_DAILY": {"description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 24th February 2000 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["not-defined", "orthoimagery", "terra-modis-l3c-0.01-v3.00-daily"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00"}, "TERRA_MODIS_L3C_0.01_V3.00_MONTHLY": {"description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The monthly dataset starts from March 2000 and ends  December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["cci", "earth-science>land-surface>surface-thermal-properties>land-surface-temperature", "esa", "land-surface-temperature", "orthoimagery", "terra-modis-l3c-0.01-v3.00-monthly"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00"}, "TERRA_MODIS_L3C_0.01_V4.00_DAILY": {"description": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System \u2013 Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00b0 longitude and 0.01\u00b0 latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The daily dataset starts from 24th February 2000 and ends 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.In Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "keywords": ["not-defined", "orthoimagery", "terra-modis-l3c-0.01-v4.00-daily"], "license": "other", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2021), version 4.00"}, "TIMESERIES_SLB_ELEMENTS_V2.2": {"description": "This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget:(a) global mean sea level(b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion(c) the mass contribution to global mean sea level(d) the global glaciers contribution (excluding Greenland and Antarctica)(e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution(f) the Antarctic Ice Sheet contribution(g) the contribution from changes in land water storage (including snow cover).The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA\u2019s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets.  Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.For some elements, more than one time series are given, as a result of different assessments from different data sources and methods.Data and methods underlying the time series are as follows:(a) satellite altimetry analysis by the Sea Level CCI project.(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.(d) results from a global glacier model.(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.(g) results from the WaterGAP global hydrological model.Version 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE.", "keywords": ["cci", "esa", "orthoimagery", "sea-level-budget-closure", "timeseries-slb-elements-v2.2"], "license": "other", "title": "ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.2"}, "TOTAL_COLUMNS_L3_MERGED_V0100": {"description": "This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI).  The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011.", "instruments": ["GOME-2", "GOME-2", "SCIAMACHY", "GOME"], "keywords": ["cci", "day", "deutsches-zentrum-fuer-luft--und-raumfahrt", "dif10", "earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone", "environmental-satellite", "envisat", "ers", "ers-2", "esa", "global-monitoring-of-atmospheric-ozone", "global-monitoring-of-atmospheric-ozone---2", "gome", "gome-2", "level-3", "level-3s", "merged", "metop", "metop-a", "metop-b", "orthoimagery", "ozone", "ozone-total-column", "scanning\u00e2\\xa0imaging\u00e2\\xa0absorption-spectrometer-for\u00e2\\xa0atmospheric-chartography", "sciamachy", "total-columns-l3-merged-v0100"], "license": "other", "platform": "Metop-A,Metop-B,Envisat,ERS-2", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01"}, "TROPOMI_HCHO_L3_V2.0_MONTHLY": {"description": "The Formaldehyde (HCHO) Climate Data Record (CDR) product is a Level 3 (L3) HCHO product developed by using satellite data from the Tropospheric Monitoring Instrument  (TROPOMI) (on Sentinel 5P) as part of the ESA (European Space Agency) Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.This dataset provides gridded HCHO tropospheric column densities of monthly 0.125\u00b0x0.125\u00b0 resolution grids from May 2018 to December 2024. The L3 product is based on the Level 2 HCHO product created for the Precursors_cci project. Compared to the operational TROPOMI product, the air mass factors have been reprocessed using an update albedo climatology, and the CAMS (Copernicus Atmosphere Monitoring Service) Reanalysis Model for the a priori vertical profiles. The background correction and the quality values have also been updated. In addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the Level 3 data files contain several additional parameters and diagnostic information such as uncertainties, a priori profiles and averaging kernels. The version number is 2.0. Data are available in NetCDF format. https://doi.org/10.18758/y591kda5The European Space Agency (ESA) Precursors for Aerosol and Ozone Climate Change Initiative (Precursors CCI) project is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data (https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/).", "keywords": ["cci", "esa", "orthoimagery", "precursors", "tropomi-hcho-l3-v2.0-monthly"], "license": "other", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0"}, "V02.31_30DAYS": {"description": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month.  This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available.   In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "instruments": ["MIRAS"], "keywords": ["aquarius", "cci", "dif10", "earth-science>spectral/engineering>microwave", "esa-climate-change-initiative", "miras", "orthoimagery", "sac-d", "sea-surface-salinity", "smap", "smos", "v02.31-30days"], "license": "other", "platform": "SMOS,SAC-D,SMAP", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v2.31, for 2010 to 2019"}, "V02.31_7DAYS": {"description": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset contains Sea Surface Salinity (SSS) v2.31 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "instruments": ["MIRAS"], "keywords": ["aquarius", "cci", "dif10", "earth-science>spectral/engineering>microwave", "esa-climate-change-initiative", "miras", "orthoimagery", "sac-d", "sea-surface-salinity", "smap", "smos", "v02.31-7days"], "license": "other", "platform": "SMOS,SAC-D,SMAP", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019"}, "V03.21_30DAYS": {"description": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month.  This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available.  In addition to salinity, information on errors are provided.  For more information, see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v03.21-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020"}, "V03.21_7DAYS": {"description": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.This dataset contains Sea Surface Salinity (SSS) v03.21 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided.  For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page).Compared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v03.21-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v03.21, for 2010 to 2020"}, "V04.41_GLOBALV4.41_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-globalv4.41-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a global grid, v04.41, for 2010 to 2022"}, "V04.41_GLOBALV4.41_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a 0.25 degree grid and 1 day of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset also covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-globalv4.41-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a global grid, v04.41, for 2010 to 2022"}, "V04.41_NHV4.41_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a NH polar 25km EASE (Equal Area Scaleable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-nhv4.41-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022"}, "V04.41_NHV4.41_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a NH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-nhv4.41-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022"}, "V04.41_SHV4.41_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-shv4.41-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022"}, "V04.41_SHV4.41_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v04.41-shv4.41-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022"}, "V05.5_GLOBALV5.5_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-globalv5.5-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023"}, "V05.5_GLOBALV5.5_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a 0.25 degree grid and 1 day of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-globalv5.5-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023"}, "V05.5_NHV5.5_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a NH polar 25km EASE (Equal Area Scaleable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-nhv5.5-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023"}, "V05.5_NHV5.5_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a NH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-nhv5.5-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023"}, "V05.5_SHV5.5_30DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-shv5.5-30days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023"}, "V05.5_SHV5.5_7DAYS": {"description": "This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "sea-surface-salinity", "v05.5-shv5.5-7days"], "license": "other", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023"}, "V3_RELEASE_ALTIMETER_L2P": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2022021 (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "earth-science>oceans>ocean-waves>significant-wave-height", "orthoimagery", "sea-state", "significant-wave-height", "v3-release-altimeter-l2p"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetry, L2P product, version 3"}, "V3_RELEASE_ALTIMETER_L3_V3.0": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021.The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "earth-science>oceans>ocean-waves>significant-wave-height", "orthoimagery", "sea-state", "significant-wave-height", "v3-release-altimeter-l3-v3.0"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height from altimetry, L3 product, version 3"}, "V3_RELEASE_ALTIMETER_L4_V3.0": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1\u00b0x1\u00b0 resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization.The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "earth-science>oceans>ocean-waves>significant-wave-height", "orthoimagery", "sea-state", "significant-wave-height", "v3-release-altimeter-l4-v3.0"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height from altimetry,  L4 product, version 3"}, "V3_RELEASE_INSITU_MICROSEISM_V1.0": {"description": "This dataset provides microseism data, produced as part of the ESA Sea State Climate Change Initiative (Sea_State_cci).  This microseism dataset is version 1.0, and forms part of the v3 release from the ESA Sea State Climate Change Initiative.Microseism data is a new addition to this CCI Sea State version 3 Dataset. Microseisms are well known to contain rich spectral information about sea states and have been used as early as 1898 to locate typhoons . As such they are unique sources of information on sea states before the advent of surface-following buoys and satellites, with the potential to define long-term trends alongside Voluntary Observing Ship data . Microseisms can be a useful resource when looking for the occurrence of wave events or investigating trends, in particular in regions where no in situ data is available. The data here are spectra of vertical ground displacement at all the available land-based stations from 3 global seismic networks (Geoscope, IRIS USGS and IRIS IDA) from 1988 to 2019. So far microseism data was either handled by seismologists with specific data formats or processed by oceanographers with limited knowledge of the measurement (such as instrument corrections); in the frame of the CCI Sea State project, they have been transformed into yearly NetCDF files containing the seismic spectrograms (Power Spectral Density of displacement) for each station using the \u201cLHZ\u201d channel (vertical displacement sampled at 1 Hz). Note that some of these stations have recordings (with different instruments and stored on different media) that go back to the early part of the 20th century, hence their possible importance for extending climate time series to the pre-satellite era in a future release.", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "integrated-sea-state-parameters", "issp", "orthoimagery", "sar", "sea-state", "v3-release-insitu-microseism-v1.0"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Seismic spectrograms from broadband seismometers, release version 3"}, "V3_RELEASE_SAR_L2P_ENVISAT_ISSP_V1.1": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from ENVISAT (referred to as SAR Wave Mode onboard ENVISAT Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the  ENVISAT Remote Sensing Integrated Sea State Parameter product (version 1.1), which forms part of the ESA Sea State CCI version 3.0 release.   This product provides along-track significant wave height (SWH) measurements  at 5km resolution every 100km, processed using the Li et al., 2020 empirical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard ENVISAT Level 2P (L2P) ISSP v3 dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "integrated-sea-state-parameters", "issp", "orthoimagery", "sar", "sea-state", "v3-release-sar-l2p-envisat-issp-v1.1"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, release version 3"}, "V3_RELEASE_SAR_L2P_SENTINEL1_ISSP_V1.0": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from Sentinel-1 (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the Sentinel-1 SAR Remote Sensing Integrated Sea State Parameter product (v1.0), which forms part of the ESA Sea State CCI version 3.0 release.   This product provides along-track primary significant wave height measurements and secondary sea state parameters, calibrated with CMEMS model data and reference in situ measurements at 20km resolution every 100km, processed using the Pleskachevsky et. al., 2021 emprical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP v3 dataset come from the Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "integrated-sea-state-parameters", "issp", "orthoimagery", "sar", "sea-state", "v3-release-sar-l2p-sentinel1-issp-v1.0"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3"}, "V3_RELEASE_SAR_L2P_SENTINEL1_SWH_V1.0": {"description": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) significant wave height (SWH) data (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) SWH data) with a particular focus for use in climate studies. This dataset contains the Sentinel-1 SAR Remote Sensing Significant Wave Height product (version 1.0), which is part of the ESA Sea State CCI Version 3.0 release.   This product provides along-track SWH measurements at 20km resolution every 100km, processed using the Quach et al statistical model , separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI dataset v3 come from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B)", "keywords": ["cci", "earth-science>oceans>ocean-waves>sea-state", "earth-science>oceans>ocean-waves>significant-wave-height", "orthoimagery", "sar", "sea-state", "significant-wave-height", "v3-release-sar-l2p-sentinel1-swh-v1.0"], "license": "other", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3."}, "V6.0-RELEASE_CLIMATOLOGY_NETCDF_MONTHLY_V6.0": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-climatology-netcdf-monthly-v6.0"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_ALL_PRODUCTS": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022.  Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-geographic-netcdf-all-products"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_CHLOR_A": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022.   Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.  (A separate dataset is also available for data on a sinusoidal projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-geographic-netcdf-chlor-a"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_IOP": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022.  Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.  (A separate dataset is also available for data on a sinusoidal projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-geographic-netcdf-iop"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_KD": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.  (A separate dataset is also available for data on a sinusoidal projection).", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-geographic-netcdf-kd"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_RRS": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>cryosphere>snow/ice>reflectance", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "reflectance", "v6.0-release-geographic-netcdf-rrs"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_ALL_PRODUCTS": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "geographic", "modis", "ocean-colour", "orthoimagery", "v6.0-release-sinusoidal-netcdf-all-products"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_CHLOR_A": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "chlorophyll-a", "dif10", "earth-science>oceans>ocean-optics>chlorophyll>chlorophyll-a", "earth-science>oceans>ocean-optics>ocean-color", "esa", "modis", "ocean-colour", "orthoimagery", "sinusoidal", "v6.0-release-sinusoidal-netcdf-chlor-a"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_IOP": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note,  the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "modis", "ocean-colour", "orthoimagery", "sinusoidal", "v6.0-release-sinusoidal-netcdf-iop"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_KD": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>oceans>ocean-optics>ocean-color", "esa", "modis", "ocean-colour", "orthoimagery", "sinusoidal", "v6.0-release-sinusoidal-netcdf-kd"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_RRS": {"description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "instruments": ["MODIS"], "keywords": ["aqua", "cci", "dif10", "earth-science>cryosphere>snow/ice>reflectance", "earth-science>oceans>ocean-optics>ocean-color", "esa", "modis", "ocean-colour", "orthoimagery", "reflectance", "sinusoidal", "v6.0-release-sinusoidal-netcdf-rrs"], "license": "other", "platform": "AQUA", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0"}, "VERSION3_L3C_ATSR2-AATSR_V3.0": {"description": "The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels:  This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset.   Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the ATSR2-AATSR L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003.   To cite the full dataset, please use the following citation: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003", "instruments": ["AATSR", "ATSR-2"], "keywords": ["aatsr", "atsr-2", "cci", "cloud", "clouds", "dif10", "earth-science>atmosphere>clouds", "envisat", "ers-2", "esa", "orthoimagery", "version3-l3c-atsr2-aatsr-v3.0"], "license": "other", "platform": "Envisat,ERS-2", "title": "ESA Cloud Climate Change Initiative (Cloud_cci):  ATSR2-AATSR monthly gridded cloud properties, version 3.0"}, "VERSION3_L3C_AVHRR-AM_V3.0": {"description": "The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project  which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.  To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00fcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.", "instruments": ["AVHRR-3", "AVHRR-2", "AVHRR-3", "AVHRR-3"], "keywords": ["avhrr-2", "avhrr-3", "cci", "cloud", "dif10", "earth-science>atmosphere>clouds", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "metop-a", "noaa-12", "noaa-15", "noaa-17", "orthoimagery", "version3-l3c-avhrr-am-v3.0"], "license": "other", "platform": "Metop-A,NOAA-12,NOAA-15,NOAA-17", "title": "ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0"}, "VERSION3_L3C_AVHRR-PM_V3.0": {"description": "The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.  To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00fcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.", "instruments": ["AVHRR-2", "AVHRR-2", "AVHRR-3", "AVHRR-3", "AVHRR-3", "AVHRR-2", "AVHRR-2"], "keywords": ["avhrr-2", "avhrr-3", "cci", "cloud", "dif10", "earth-science>atmosphere>clouds", "earth-science>spectral/engineering>infrared-wavelengths", "esa", "noaa-11", "noaa-14", "noaa-16", "noaa-18", "noaa-19", "noaa-7", "noaa-9", "orthoimagery", "version3-l3c-avhrr-pm-v3.0"], "license": "other", "platform": "NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): AVHRR-PM monthly gridded cloud properties, version 3.0"}, "VERTICAL_LAND_MOTIONS_TUM_MAPS_V1": {"description": "This dataset contains a regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania  produced as part of the ESA Climate Change Initiative Sea Level project.Vertical Land Motions have been estimated as the difference between the altimeter coastal sea level v1.1 dataset (available from https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4 ) and tide gauge measurements from the Permanent Service for Mean Sea Level (PMSML) network. Spatial interpolation has allowed the production of a regularly spaced coastline profile of vertical land movements together with their uncertainties.The altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018.", "keywords": ["earth-science>oceans>sea-surface-topography>sea-surface-height", "esa-cci", "orthoimagery", "sea-level", "sla", "vertical-land-motions-tum-maps-v1"], "license": "other", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania,  v1"}, "WATER_BODIES_V4.0": {"description": "As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced.  The CCI WB v4.0 is composed of two layers:1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.This product is delivered at 150 m as a stand-alone product but it is consistent with class \"Water Bodies\" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water2. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D\u2019Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036", "instruments": ["ASAR"], "keywords": ["13-years", "advanced-synthetic-aperture-radar", "asar", "cci", "condition-water-(water-bodies)", "dif10", "earth-science>land-surface>land-use/land-cover", "envisat", "land-cover", "level-4", "map", "orthoimagery", "universite-catholique-de-louvain", "water-bodies", "water-bodies-v4.0"], "license": "other", "platform": "Envisat", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Water Bodies Map, v4.0"}, "WL_V1.1": {"description": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci).   Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications. These river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series have also been produced.", "keywords": ["cci", "orthoimagery", "river-discharge", "water-level", "wl-v1.1"], "license": "other", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Nadir radar altimeters Water Level product, v1.1"}, "WL_V2.0": {"description": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci).   Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications.This version of the dataset is v2.0These river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6A). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series (with two different methodologies for some basins) have also been produced.", "keywords": ["cci", "orthoimagery", "river-discharge", "water-level", "wl-v2.0"], "license": "other", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Nadir radar altimeters Water Level product, v2.0"}, "WV-STRATO_L3S_V3.3": {"description": "This water vapour (WV) climate data record (CCI WV-strato or WV_cci CDR-3) has been generated within the European Space Agency (ESA) Water Vapour Climate Change Initiative (Water Vapour_cci). CCI WV-strato is a merged product based on a number of WV datasets obtained from limb and solar occultation satellite instruments. CCI WV-strato features zonal monthly mean vertically resolved water vapour in the stratosphere, covers a pressure range from 300 to 0.1 hPa at 5 degree latitudinal resolution, and spans the time period between 1985 and 2019.This version of the data is v3.3.", "keywords": ["cci", "esa-climate-change-initiative", "orthoimagery", "stratospheric-water-vapour", "wv-strato-l3s-v3.3"], "license": "other", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3"}, "XTRACK_ALES_SLA_ENVISAT_SARAL_SLA_V1.1": {"description": "This dataset contains along-track sea level anomalies derived from satellite altimetry.   Altimeter along-track sea level measurements from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia).  The product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections.  The main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.The Envisat and SARAL/AltiKa missions have the same ground track but the temporal gap between both missions prevents from computing reliable trends during the total period between both missions.This dataset has been produced by the Climate Change Initiative Coastal Sea Level team, within the extension phase of the European Sapce Agency (ESA) Climate Change Initiative.", "keywords": ["esa-cci", "orthoimagery", "sla", "xtrack-ales-sla-envisat-saral-sla-v1.1"], "license": "other", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions from ENVISAT (2002-2010) and SARAL (2013-2016) satellite altimetry, v1.1"}, "XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V2.2": {"description": "This dataset contains  a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic,  Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia,  Australia and North and South America.  Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+  dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called \u2018retracking\u2019) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856).  The dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Leg\u00e9r Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Ni\u00f1o Fernando, Legeais Jean Fran\u00e7ois, Oelsmann Julius, Benveniste J\u00e9r\u00f4me (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE.  https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean Fran\u00e7ois, Oelsmann Julius, Restano Marco, Benveniste J\u00e9r\u00f4me (2022). Sea level along the world\u2019s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z -  Benveniste J\u00e9r\u00f4me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean Fran\u00e7ois, L\u00e9ger Fabien, Ni\u00f1o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002\u20132018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w", "instruments": ["POSEIDON-2"], "keywords": ["centre-national-de-la-recherche-scientific", "centre-national-detudes-spatiales", "dif10", "earth-science>oceans>sea-surface-topography>sea-surface-height", "earth-science>spectral/engineering>radar", "esa-cci", "european-space-agency", "indicator", "jason-1", "jason-2", "jason-3", "laboratoire-detudes-en-geodesie-et-oceanographie-spatiales", "mean-sea-level-trends", "merged", "month", "orthoimagery", "poseidon-2", "poseidon-3", "poseidon-3b", "sea-level", "sla", "xtrack-ales-sla-trends-selectedsites-v2.2"], "license": "other", "platform": "Jason-1,JASON-3", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2"}, "XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V3.0": {"description": "This dataset contains a 19.5-year-long (January 2002 to June 2021), high-resolution (20 Hz), along-track sea level dataset in most of the world coastal zones, including tropical islands. It has been developed within the sea level project of the European Space Agency (ESA) Climate Change Initiative (SL_cci). The main objective of this dataset is to analyze the sea level trends as well as the inter-annual variability at local scale at an average of less than 2.5km from the coastline. It provides essential information in areas devoid of other sources of measurements, and it also allows filling the gaps in existing timeseries of tide gauges located nearby the stations.This dataset of coastal sea level anomalies is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called \u2018retracking\u2019) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 1634 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span.The new updated version (v3.0; June 2025) of along-track coastal sea level time series and associated trends from January 2002 to June 2021 differs from the previous v2.4 product (released in November 2024) by a spatial extension. It also uses the new improved FES22 ocean tide model instead of FES14 model in the previous versions. The data editing (outlier removal) has slightly evolved, and a new variable has been added (sla_mean_10pts_filt). We strongly recommend the users to use this latest v3.0 product.For the latest version of the documentation, see the 'Technical Coastal Sea Level' Key Documents section of the project's website (https://climate.esa.int/en/projects/sea-level/).This dataset is v3.0 of the data and is a copy of the v3.0 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#122284).The dataset should be cited as: Cazenave Anny, Gouzenes Yvan, Leclercq Lancelot, Birol Florence, Leg\u00e9r Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Ni\u00f1o Fernando, Legeais Jean Fran\u00e7ois, Oelsmann Julius, Benveniste J\u00e9r\u00f4me, Connors Sarah (2025). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354In addition, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :- Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean Fran\u00e7ois, Oelsmann Julius, Restano Marco, Benveniste J\u00e9r\u00f4me (2022). Sea level along the world\u2019s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z- Benveniste J\u00e9r\u00f4me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean Fran\u00e7ois, L\u00e9ger Fabien, Ni\u00f1o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002\u20132018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w", "keywords": ["esa-cci", "orthoimagery", "sla", "xtrack-ales-sla-trends-selectedsites-v3.0"], "license": "other", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2021, v3.0"}}, "providers_config": {"AATSR_ADV_L2_V2.31": {"_collection": "9f6324ebe92940b989ebf273d5f8bf33", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ADV Algorithm), Version 2.31 (45683)"}, "AATSR_ADV_L3_V2.31": {"_collection": "ab90030e26c54ba495b1cbec51e137e1", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31 (3578)"}, "AATSR_ENS_L2_V2.6": {"_collection": "cdcb0605afa74885a66d8be0fdd2ed24", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ensemble product), Version 2.6 (3456)"}, "AATSR_ENS_L3_V2.6": {"_collection": "c183044b88734442b6d37f5c4f6b0092", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6 (3745)"}, "AATSR_ORAC_L2_V4.01": {"_collection": "8b63d36f6f1e4efa8aea302b924bc46b", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ORAC Algorithm), Version 4.01 (46466)"}, "AATSR_ORAC_L3_V4.01": {"_collection": "da2b8512312a4f14a928766f7f632d36", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01 (3115)"}, "AATSR_SU_L2_V4.3": {"_collection": "b03b3887ad2f4d5481e7a39344239ab2", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (SU Algorithm), Version 4.3 (45507)"}, "AATSR_SU_L3_V4.3": {"_collection": "d12fc40e4f254ce38303157fa460f01c", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (SU algorithm), Version 4.3 (3652)"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V03.0": {"_collection": "67a3f8c8dc914ef99f7f08eb0d997e23", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost active layer thickness for the Northern Hemisphere, v3.0 (26)"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V04.0": {"_collection": "d34330ce3f604e368c06d76de1987ce5", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0 (26)"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_ANTARCTICA": {"_collection": "9580c512bb474d00b1e0ee554e219bb7", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for Antarctica, v5.0 (28)"}, "ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"_collection": "a6fbedd8ee5b472c8e84e55f746c1704", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v5.0 (28)"}, "AGB_MAPS_V2.0": {"_collection": "84403d09cef3485883158f4df2989b0c", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2 (1710)"}, "AGB_MAPS_V3.0": {"_collection": "5f331c418e9f4935b8eb1b836f8a91b8", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3 (2916)"}, "AGB_MAPS_V4.0": {"_collection": "af60720c1e404a9e9d2c145d2b2ead4e", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4 (5186)"}, "AGB_MAPS_V5.01": {"_collection": "bf535053562141c6bb7ad831f5998d77", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01 (8978)"}, "AGB_MAPS_V6.0": {"_collection": "95913ffb6467447ca72c4e9d8cf30501", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022, v6.0 (11442)"}, "AQUA_MODIS_L3C_0.01_V3.00_DAILY": {"_collection": "6babb8d9a8d247bcb3da6aed42f4b59a", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00 (12009)"}, "AQUA_MODIS_L3C_0.01_V3.00_MONTHLY": {"_collection": "fe98aa1c666d42b9a2a0d19a72bb8a36", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018),... (401)"}, "AQUA_MODIS_L3C_0.01_V4.00_DAILY": {"_collection": "a92c495827ef4c81a901f878adb6ef70", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021),... (14211)"}, "ARCTIC_MSLA_20161024": {"_collection": "b98db73607354c7b8ba9bc9347ceb9ed", "title": "ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML) (3715)"}, "ATSR2_ADV_L2_V2.31": {"_collection": "a7e11745933a4f37b5aa1d4b23d71a83", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31 (29805)"}, "ATSR2_ADV_L3_V2.31": {"_collection": "302939d341fa4013b6d96d231d6d4f40", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ADV algorithm), Version 2.31 (2379)"}, "ATSR2_ENS_L2_V2.6": {"_collection": "4afb736dc395442aa9b327c11f0d704b", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ensemble product), Version 2.6 (2256)"}, "ATSR2_ENS_L3_V2.6": {"_collection": "bf5eae2a052848aab2abf93d96e7e9aa", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6 (2341)"}, "ATSR2_ORAC_L2_V4.01": {"_collection": "04bc222136f7429eb04d3eb3543ef3e8", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01 (31882)"}, "ATSR2_ORAC_L3_V4.01": {"_collection": "d2ed0c005761475d92ca444666156c4a", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01 (2334)"}, "ATSR2_SU_L2_V4.3": {"_collection": "a8b8191d62504acdb218d4767b446280", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (SU algorithm), Version 4.3 (35869)"}, "ATSR2_SU_L3_V4.3": {"_collection": "39909dc233b34118a80dd6fa8a7af553", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (SU algorithm), Version 4.3 (2754)"}, "BURNED_AREA_AVHRR-LTDR_GRID_V1.1": {"_collection": "62866635ab074e07b93f17fbf87a2c1a", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1 (472)"}, "BURNED_AREA_AVHRR-LTDR_PIXEL_V1.1": {"_collection": "b1bd715112ca43ab948226d11d72b85e", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1 (2199)"}, "BURNED_AREA_MODIS_GRID_V5.1": {"_collection": "3628cb2fdba443588155e15dee8e5352", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1 (297)"}, "BURNED_AREA_MODIS_PIXEL_V5.1": {"_collection": "58f00d8814064b79a0c49662ad3af537", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1 (1681)"}, "BURNED_AREA_SENTINEL3_SYN_GRID_V1.1": {"_collection": "da8e669a74334c82a56e0b470bc4ef04", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.1 (74)"}, "BURNED_AREA_SENTINEL3_SYN_PIXEL_V1.1": {"_collection": "d441079fc77f49fabeb41330612b252f", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.1 (2234)"}, "BURNED_AREA_SFDL_V1.0_PIXEL": {"_collection": "593397b5f9654d76b5d37761e7566ca6", "title": "ESA Fire Climate Change Initiative (Fire_cci): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia, Africa and Siberia, version 1.0 (323997)"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V1.1": {"_collection": "4b0773a84e8142c688a628c9ce62d4ec", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 1.1 (17)"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V2.0": {"_collection": "01b00854797d44a59d57c8cce08821eb", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 2.0 (26)"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V1.1": {"_collection": "065f6040ef08485db989cbd89d536167", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1 (1396)"}, "BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V2.0": {"_collection": "4c5feb539f1f44308ca7ec26e0bb7316", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 2.0 (6962)"}, "CCI_PLUS_CH4_GO2_SRFP_V1.0": {"_collection": "fdd90615b0df45489d9ca47708d98325", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm, version 1.0.0 (249)"}, "CCI_PLUS_CH4_GO2_SRFP_V2.0.2": {"_collection": "c14874e943cc453a8e63ce5841ecc9b0", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.2 (1001)"}, "CCI_PLUS_CH4_GO2_SRFP_V2.0.3": {"_collection": "9d3304ed60884e4c8ec4719b6e5c57b1", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.3 (1730)"}, "CCI_PLUS_CH4_GO2_SRPR_V1.0": {"_collection": "722fe4748da2487ead0a755f6d09e6ab", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm, version 1.0.0 (249)"}, "CCI_PLUS_CH4_GO2_SRPR_V2.0.2": {"_collection": "3fc7927499fa49e0b6ace6c807972259", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.2 (1001)"}, "CCI_PLUS_CH4_GO2_SRPR_V2.0.3": {"_collection": "1097a848fd464ac8af3e8ab93032a19c", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.3 (1731)"}, "CCI_PLUS_CH4_S5P_WFMD_V1.8": {"_collection": "8175ede3a1d642deba8f4cce49d7bda8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8,  November 2017 - October 2023 (2136)"}, "CCI_PLUS_CH4_S5P_WFMD_V1.8_EXTENDED_JUNE2024": {"_collection": "2b8c6a8f1abd40a6b0ce07c40b1c57ff", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8,  November 2017 - June 2024 (2379)"}, "CCI_PLUS_CO2_GO2_SRFP_V1.0": {"_collection": "f1b19872c12d477abfeb229f060a0969", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm, version 1.0.0 (249)"}, "CCI_PLUS_CO2_GO2_SRFP_V2.0.2": {"_collection": "875f25069b5d4bd9a7101ca1206ee4f0", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.2 (1001)"}, "CCI_PLUS_CO2_GO2_SRFP_V2.0.3": {"_collection": "e95903ef0e0a4bfa92f528b86c4a8b0f", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.3 (1730)"}, "CCI_PLUS_CO2_OC2_FOCA_V10.1": {"_collection": "2c1cb1d606c4421e9339a3028839a41f", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10.1 (2550)"}, "CCI_PLUS_CO2_OC2_FOCA_V11.0": {"_collection": "198d56f5fbbf4144b8fd4932328be462", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 11.0 (3255)"}, "CCI_PLUS_CO2_TAN_OCFP_V1.0": {"_collection": "2cc63301f1854239aa61c70e58c61207", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0 (327)"}, "CCI_PLUS_CO2_TAN_OCFP_V1.0_GLOBAL_LAND": {"_collection": "9252ff9ddeb249a2bd8433e9ae9dfe13", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0 (60)"}, "CCI_PLUS_CO2_TAN_OCFP_V1.2": {"_collection": "b0beb88af3b748b98afc4b35f77bebf8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.2 (417)"}, "CDR_V2_ANALYSIS_L4_V2.1": {"_collection": "62c0f97b1eac4e0197a674870afe1ee6", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1 (25817)"}, "CDR_V2_ATSR_L2P_V2.1": {"_collection": "916b93aaf1474ce793171a33ca4c5026", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1 (100625)"}, "CDR_V2_ATSR_L3C_V2.1": {"_collection": "5db2099606b94e63879d841c87e654ae", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record, version 2.1 (14667)"}, "CDR_V2_ATSR_L3U_V2.1": {"_collection": "2282b4aeb9f24bc3a1e0961e4d545427", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 (100625)"}, "CDR_V2_AVHRR_L2P_V2.1": {"_collection": "373638ed9c434e78b521cbe01ace5ef7", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1 (364782)"}, "CDR_V2_AVHRR_L3C_V2.1": {"_collection": "7db4459605da4665b6ab9a7102fb4875", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1 (50993)"}, "CDR_V2_AVHRR_L3U_V2.1": {"_collection": "42f7230ab55641cdac1bba84eabd446a", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 (364782)"}, "CDR_V2_CLIMATOLOGY_L4_V2.2": {"_collection": "dc11996a68c446abb342e917efdaac30", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.2 (367)"}, "CRDP_4_EMMA_CH4_V1.2": {"_collection": "9ed2813d2eda4d958e92ab3ce1ab1fe6", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2 (1820)"}, "CRDP_4_EMMA_CO2_V2.2": {"_collection": "9f002827ba7d48f59019fcfd3577a57e", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2 (3894)"}, "CRDP_4_GOSAT_CH4_GOS_OCFP_V2.1": {"_collection": "56f81895cb094bd8a1638aa12d6c7499", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1 (2389)"}, "CRDP_4_GOSAT_CH4_GOS_OCPR_V7.0": {"_collection": "f9154243fd8744bdaf2a59c39033e659", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0 (2396)"}, "CRDP_4_GOSAT_CH4_GOS_SRFP_V2.3.8": {"_collection": "46d136149d0a4f1cb8de7efbe8abf4b2", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8 (2390)"}, "CRDP_4_GOSAT_CH4_GOS_SRPR_V2.3.8": {"_collection": "96d5b75ea29946c5aab8214ddbab252b", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8 (2398)"}, "CRDP_4_GOSAT_CO2_GOS_OCFP_V7.0": {"_collection": "9255faeb392f41debf5402caa40dada8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0 (2388)"}, "CRDP_4_GOSAT_CO2_GOS_SRFP_V2.3.8": {"_collection": "e61704b00267405082fbd41bb710dd74", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8 (2390)"}, "CRDP_4_SCIAMACHY_CH4_SCI_IMAP_V7.2": {"_collection": "8f5623a85d2e4b9b8ab5313f65a7c994", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2 (2896)"}, "CRDP_4_SCIAMACHY_CH4_SCI_WFMD_V4.0": {"_collection": "aa09603e91b44f3cb1573c9dd415e8a8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0 (3211)"}, "CRDP_4_SCIAMACHY_CO2_SCI_BESD_V02.01.02": {"_collection": "294b4075ddbc4464bb06742816813bdc", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02 (3113)"}, "CRDP_4_SCIAMACHY_CO2_SCI_WFMD_V4.0": {"_collection": "e493802d83c846c8b76f817866fb74cc", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0 (3301)"}, "DAILY_FILES_ACTIVE_V05.2": {"_collection": "dd3da2570363429791b51120bdd29c02", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2 (10378)"}, "DAILY_FILES_ACTIVE_V05.3": {"_collection": "1da8dadcdfb642f4aad2384f02efe756", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.3 (10744)"}, "DAILY_FILES_ACTIVE_V06.1": {"_collection": "c2af8764c84744de87a69db7fecf7af9", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1 (10746)"}, "DAILY_FILES_ACTIVE_V06.2": {"_collection": "898c950f441e400d8b569216ebe41cab", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2 (11109)"}, "DAILY_FILES_ACTIVE_V07.1": {"_collection": "e235d2980d6a441895f7221ff4787a6f", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 07.1 (11110)"}, "DAILY_FILES_ACTIVE_V08.1": {"_collection": "b0f5fc3a10cf4806ab57326edd8daf65", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 08.1 (11474)"}, "DAILY_FILES_ACTIVE_V09.1": {"_collection": "5b1caf9095d7412282f5ba6b558034e3", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.1 (11838)"}, "DAILY_FILES_ACTIVE_V09.2": {"_collection": "2600f842cfeb481bb21f032a5741c353", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.2 (12124)"}, "DAILY_FILES_BREAK_ADJUSTED_COMBINED_V06.1": {"_collection": "3bfe0c2d51544f72837a99306a74e359", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1 (15406)"}, "DAILY_FILES_BREAK_ADJUSTED_COMBINED_V07.1": {"_collection": "0ae6b18caf8a4aeba7359f11b8ad49ae", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 07.1 (15770)"}, "DAILY_FILES_COMBINED_V05.2": {"_collection": "057dd6c36f0741d3b97f9eee688b7835", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2 (15038)"}, "DAILY_FILES_COMBINED_V05.3": {"_collection": "e43aead9947549078c2d108b2c3632b2", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3 (15404)"}, "DAILY_FILES_COMBINED_V06.1": {"_collection": "43d73291472444e6b9c2d2420dbad7d6", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1 (15406)"}, "DAILY_FILES_COMBINED_V06.2": {"_collection": "e83e62dd493447c5808f80c36b5acac7", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.2 (15769)"}, "DAILY_FILES_COMBINED_V07.1": {"_collection": "c7e974411cfe4cf99cb077f7cb4d75d4", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 07.1 (15770)"}, "DAILY_FILES_COMBINED_V08.1": {"_collection": "6f99cdb86a9e4d3da2d47c79612c00a2", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 08.1 (16134)"}, "DAILY_FILES_COMBINED_V09.1": {"_collection": "0e346e1e1e164ac99c60098848537a29", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.1 (16498)"}, "DAILY_FILES_COMBINED_V09.2": {"_collection": "d4e66299f5054129b8076fb7502949e1", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.2 (16864)"}, "DAILY_FILES_PASSIVE_V05.2": {"_collection": "f0580e34da524770b0a5d43c033b33dc", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2 (15038)"}, "DAILY_FILES_PASSIVE_V05.3": {"_collection": "31137897d305407c9b83d49d124e4d1d", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3 (15404)"}, "DAILY_FILES_PASSIVE_V06.1": {"_collection": "f5ffbd016e6b44858a33ae38ed2a149e", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.1 (15406)"}, "DAILY_FILES_PASSIVE_V06.2": {"_collection": "4dd145a7060143cd875325390d3b01c8", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.2 (15769)"}, "DAILY_FILES_PASSIVE_V07.1": {"_collection": "63e14c1e66124ccc857ce4e73ab601ed", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 07.1 (15770)"}, "DAILY_FILES_PASSIVE_V08.1": {"_collection": "16dc7da110324e5196e922191d962157", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 08.1 (16134)"}, "DAILY_FILES_PASSIVE_V09.1": {"_collection": "ca55ac11fc814b0d95e68a34a10539c1", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.1 (16498)"}, "DAILY_FILES_PASSIVE_V09.2": {"_collection": "c93c99588d5848b1ac7833e3bc6d5c2d", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.2 (16864)"}, "DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_CRYOSAT_V1.0_NH": {"_collection": "c7cd8115391340638bdd9adb0fbf28e9", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from CryoSat-2, v1.0 (1101)"}, "DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_ENVISAT_V1.0_NH": {"_collection": "97791d46330b42838af744301c74a9fb", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from Envisat, v1.0 (1213)"}, "DTU_TUM_ARCTIC_ANTARCTIC_MSLA_20170720": {"_collection": "f24175ec280b4e1296bb80eb86f88f68", "title": "ESA Sea Level Climate Change Initiative  (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM) (6594)"}, "ENVISAT_ATSR_L3C_0.01_V3.00_DAILY": {"_collection": "1115d8946ba74c7f8a9fc3bfee5513a0", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00 (6911)"}, "ENVISAT_ATSR_L3C_0.01_V3.00_MONTHLY": {"_collection": "2ac9a3e7bdeb41b58b226a2fa612a4a3", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00 (235)"}, "ENVISAT_ATSR_L3C_0.01_V4.00_DAILY": {"_collection": "3c05b3af81f540718faeb6fa1b2046e6", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 4.00 (6973)"}, "ERS-2_ATSR_L3C_0.01_V3.00_DAILY": {"_collection": "f1445bde2f1249c99bb5a59b71e9a9d7", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2013), version 3.00 (4915)"}, "ERS-2_ATSR_L3C_0.01_V3.00_MONTHLY": {"_collection": "16c633f003ef4d8481420f052356c11c", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2013), version 3.00 (171)"}, "ERS-2_ATSR_L3C_0.01_V4.00_DAILY": {"_collection": "030157c0dda24ae28fc0f56d39cf1ed1", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2),  level 3 collated (L3C) global product (1995-2003), version 4.00 (5433)"}, "FCDR_V2.0": {"_collection": "2785ee1ec6274be39d11e7e7ce51b381", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Fundamental Climate Data Records of sea level anomalies and altimeter standards, Version 2.0 (1665)"}, "GIEMS_METHANE_CENTRIC_V1.0": {"_collection": "1d01d0efb6c24c218489605b5aa44cf5", "title": "ESA RECCAP-2 Climate Change Initiative (RECCAP2_cci): Methane-Centric Wetland Dataset Based on GIEMS (1992-2020), v1.0 (2)"}, "GMPE_CDR_V2_L4_V2.0": {"_collection": "a963d9415bb74247830f8704f825aa90", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): GHRSST Multi-Product ensemble (GMPE), v2.0 (12907)"}, "GOES_IMAGER_ABI_L3C_V1.00_MONTHLY": {"_collection": "c01275d56cba4970bda78ca79b5b1273", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Geostationary Operational Environmental Satellite (GOES) level 3C (L3C) product (2009-2020), version 1.00 (1585)"}, "GOES_IMAGER_ABI_L3U_V1.00": {"_collection": "1fb8f831da5b45a59213da1d8a4503b8", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3U (L3U) product (2009-2020), version 1.00 (41838)"}, "GOMOS_AERGOM_L3_V3.00": {"_collection": "7fb8fd2761484b1eae4f7df4a3e65f75", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from GOMOS (AERGOM algorithm), Version 3.00 (739)"}, "GRAVIMETRIC_MASS_BALANCE_BASIN_V3.0": {"_collection": "e1dfd0ee655944b8a82ce0479c518747", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance basin product, v3.0, 2002-2020 (3)"}, "GRAVIMETRIC_MASS_BALANCE_GRIDDED_V3.0": {"_collection": "36dae49c76f845a18062fa96599be719", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0,  2002 - 2020 (3)"}, "GREENLAND_CALVING_FRONT_LOCATIONS_V3.0": {"_collection": "8889dfe3de45406e815bce13ae8a0c92", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v3.0 (4369)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.4": {"_collection": "35ea8189e75e4b6f95e7c86812080ecb", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space,  v1.4 (47)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.5": {"_collection": "ff4bfe39b7fe42fc993341d3cebdabb5", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5 (45)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V2.2": {"_collection": "48cd535e93574c8da8e80b91e06c7d51", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2 (48)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V3.0": {"_collection": "bf5f4731e8bd456082e2e43f97f4430e", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v3.0 (54)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.2": {"_collection": "b017235a8e544d6fbad21387ebfbf0d8", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.2 (36)"}, "GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.3": {"_collection": "5970b33c92ef444793fb6d7e54d1230e", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3 (36)"}, "GREENLAND_GROUNDING_LINE_LOCATIONS_V1.3": {"_collection": "ea7a4cbe7b83450bb7a00bf3761c40d7", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations from SAR Interferometry,  v1.3 (74)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2013_2014_V1.0": {"_collection": "9bdeb99d91a743fe84623264587ad043", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2013-2014, v1.0 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2014_2015_V1.0": {"_collection": "82e4ede59fe746ba810009d9a30e0153", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2014-2015, v1.0 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2015_2016_V1.2": {"_collection": "302f379334e84664bd3409d08eca6565", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2015-2016, v1.2 (7)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2016_2017_V1.0": {"_collection": "24dc5d5429434ccdb349db04a1a3233d", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2016-2017, v1.0 (7)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2017_2018_V1.0": {"_collection": "eaed9fba86c44e9c854dfbdec9d16b99", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2017-2018, v1.0 (7)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_CSK_JAKOBSHAVN_V1.0": {"_collection": "2e54b40f184b44c797db36e192d2b679", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0 (7)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_79FJORD_V1.1": {"_collection": "41e2300068b44fa190f24272dc08dcd0", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the 79-Fjord Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HAGEN_V1.1": {"_collection": "5c9935b8b8854baeb7a256446293c03b", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HELHEIM_V1.1": {"_collection": "0e289294f2c141bca545cd9d7fcb62d0", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_JAKOBSHAVN_V1.1": {"_collection": "e1c0c34e0cc942898b3626efd1dcc095", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_KANGERLUSSUAQ_V1.1": {"_collection": "925e3f0e807243e2936cc492f5207af6", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq Glacier for 2015-2017 from Sentinel-1, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_PETERMANN_V1.1": {"_collection": "81332b9a10f14bda8a1a83b6463bb6de", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_STORSTROEMMEN_V1.1": {"_collection": "1dd4c30a78d84e628cd8097bae3148fd", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Storstroemmen Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_UPERNAVIK_V1.1": {"_collection": "ef5c6596cae548c6aea9dea181c7624c", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_ZACHARIAE_V1.1": {"_collection": "e3dbdc32f7b6476e949d52d8d3990205", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_79FJORD_V1.1": {"_collection": "f31e8e988c4144bebe13892b53d08e42", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_DOCKER_SMITH_V1.0": {"_collection": "88d02eb5a6c14952aa88028894d8a69c", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the D\u00c3\u00b8cker Smith Glacier between 2016-05-08 and 2016-05-18, generated using Sentinel-2 data, v1.0 (7)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HAGEN_V1.1": {"_collection": "e7fa45e785a64481960c3b140038c948", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Hagen Glacier between 2017-06-30 and 2017-08-14, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HELHEIM_V1.1": {"_collection": "1e3fcdc14e2246c69fc54f0e1fe7a6ca", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Helheim Glacier between 2017-05-01 and 2017-08-29, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_JAKOBSHAVN_V1.1": {"_collection": "cfe3102659f34d33b123b2a0043e4068", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Jakobshavn Glacier between 2017-06-03 and 2017-09-08, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_KANGERDLUGSSUAQ_V1.1": {"_collection": "aae643e1a7614c24b6b604dea82cad93", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_PETERMANN_V1.1": {"_collection": "94f3670150de4bac90773806e26646f2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Petermann Glacier between 2017-05-01 and 2017-09-14, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_UPERNAVIK_V1.1": {"_collection": "84faf575c8e841a3a16476b05cbd657d", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Upernavik Glacier between 2017-07-15 and 2017-08-14, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_ZACHARIAE_V1.1": {"_collection": "ada968fd392d49fbbb07ac84eeb23ac6", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_HAGEN_TIMESERIES_V1.1": {"_collection": "27fc79c6e65f4302a18ec9788605c246", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_HELHEIM_TIMESERIES_V1.1": {"_collection": "17767027aa484505b7b732aee6619c74", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim glacier from ERS-1, ERS-2 and Envisat data for 1996-2010, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_JAKOBSHAVN_TIMESERIES_V1.2": {"_collection": "a0d9764a3068439b997c42928ef739d2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series  for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2 (12)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_KANGERLUSSUAQ_TIMESERIES_V1.0": {"_collection": "723067f77b8b43609079d721e3b4a3c7", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq glacier from ERS-1, ERS-2, Envisat for 1992-2008, v1.0 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_PETERMANN_TIMESERIES_V1.1": {"_collection": "22254b5608ab430fa360d0ff7e71c34e", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_STORSTROMMEN_TIMESERIES_V1.1": {"_collection": "8381d3f3998143fd9b53c7086b7061e3", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series of the Storstrommen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_UPERNAVIK_TIMESERIES_V1.2": {"_collection": "8d475d7d92894765ad1ddda16de0e610", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_IV_ZACHARIAE_79FJORD_TIMESERIES_V1.1": {"_collection": "2457272c747f4d6ca33cb40833bd9cc2", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae and 79Fjord area from ERS-1, ERS-2 and Envisat data for 1991-2011, v1.1 (8)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_ERS2_1995_1996_V1.1": {"_collection": "0b23b3c771db4fff8958196432d978cb", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release) (10)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_PALSAR_TIMESERIES_2006_2011_V1.1": {"_collection": "84b5cf8380894d719b61deac5abf3bae", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from the PALSAR instrument for 2006-2011, v1.1 (June 2016 version) (21)"}, "GREENLAND_ICE_VELOCITY_GREENLAND_NORTHERN_DRAINAGE_BASINS_ERS1_WINTER_1991_1992_V1.1": {"_collection": "e4f39152bc50466f8887bd2a343cac93", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Northern Drainage basin from ERS-1 for winter 1991-1992, v1.1 (June 2016 release) (10)"}, "GREENLAND_ML_CALVING_FRONT_LOCATIONS_V1.0": {"_collection": "35a3e7e5ad2946859ac31c36605486f0", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Machine Learning Generated Greenland Calving Front Locations v1.0 (16)"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_CRYOSAT2_V2.2": {"_collection": "41e9783d4caa447b99f653c065805579", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.2 (46)"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_SARAL-ALTIKA_V0.1": {"_collection": "0470e96f2d8245549ef2ba81842cdfd8", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change grid from SARAL-AltiKa for 2013-2017, v0.1 (8)"}, "GREENLAND_SURFACE_ELEVATION_CHANGE_V1.2": {"_collection": "a7b87a912c494c03b4d2fa5ab8479d1c", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2 (86)"}, "GROUNDING_LINE_LOCATIONS_KEY_GLACIERS_V2.0": {"_collection": "7b3bddd5af4945c2ac508a6d25537f0a", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci):  Grounding line location for key glaciers, Antarctica, 1994-2020, v2.0 (6)"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V03.0": {"_collection": "b25d4a6174de4ac78000d034f500a268", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost Ground Temperature for the Northern Hemisphere, v3.0 (26)"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V04.0": {"_collection": "20ec12f5d1f94e99aff2ed796264ee65", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0 (26)"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_ANTARCTICA": {"_collection": "88380c00ad79414ab1a30d3cd4cb7ad9", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for Antarctica, v5.0 (28)"}, "GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"_collection": "5675b0be944f45a8af0e7ddbeb47a011", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v5.0 (28)"}, "IASI_NH3_L3_V4R_MERGED": {"_collection": "2ec5b1fb653445bdbbd0dcd69dd223da", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI NH3 L3 merged product, version 4R (214)"}, "IASI_NH3_L3_V4R_METOP-A": {"_collection": "559901afef54425f8e755f9305d0af3a", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-A NH3 L3 product, version 4R (170)"}, "IASI_NH3_L3_V4R_METOP-B": {"_collection": "c152a24620504209b59e07c6f97f26e0", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-B NH3 L3 product, version 4R (149)"}, "IASI_NH3_L3_V4R_METOP-C": {"_collection": "c1cee19b22ae4c6187605ada036477e4", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): IASI/Metop-C NH3 L3 product, version 4R (71)"}, "ICE_VELOCITY_ANTARCTIC_ICE_SHEET_SENTINEL_1_MONTHLY_V1.0": {"_collection": "00fe090efc58446e8980992a617f632f", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1 (45)"}, "IIML_GREENLAND_V1_2017": {"_collection": "7ea7540135f441369716ef867d217519", "title": "ESA Glaciers Climate Change Initiative (Glaciers_cci):  2017 inventory of ice marginal lakes in Greenland (IIML), v1 (10)"}, "IND_V2.0": {"_collection": "3ac333b828b54e3495c7749f5bce2fe3", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Oceanic Indicators of Mean Sea Level Changes,  Version 2.0 (11)"}, "L3S_VP_PRODUCTS_V1.0": {"_collection": "34e4bfe402c048c783e64eac0f0bca37", "title": "ESA Vegetation Parameters Climate Change Initiative (Vegetation_Parameters_cci): LAI and fAPAR, Version 1.0 (54006)"}, "L4_MSLA_V2.0": {"_collection": "142052b9dc754f6da47a631e35ec4609", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0 (553)"}, "LAKE_PRODUCTS_L3S_V1.0": {"_collection": "3c324bb4ee394d0d876fe2e1db217378", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.0 (19919)"}, "LAKE_PRODUCTS_L3S_V1.1": {"_collection": "ef1627f523764eae8bbb6b81bf1f7a0a", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.1 (9970)"}, "LAKE_PRODUCTS_L3S_V2.0.2": {"_collection": "a07deacaffb8453e93d57ee214676304", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.0.2 (10325)"}, "LAKE_PRODUCTS_L3S_V2.1": {"_collection": "7fc9df8070d34cacab8092e45ef276f1", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.1 (11058)"}, "LAKE_PRODUCTS_L3S_V3.0": {"_collection": "a56dba09df8a42ec9fba8b8c7a5e1f69", "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 3.0 (11438)"}, "LAND_COVER_MAPS_A01_AFRICA_HISTORICAL_V1.2_GEOTIFF": {"_collection": "a3fb75aa46db4711ab587f3fa3ca01fe", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Africa (Eastern Sahel region) at 30m spatial resolution in GeoTiff format,... (11055)"}, "LAND_COVER_MAPS_A01_AFRICA_STATIC_V1.2_GEOTIFF_HRLC10": {"_collection": "f107a4ce186844bb8adf8cd1f2f6d552", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Africa (Eastern Sahel region) at 10m spatial resolution for 2019 in Geotiff format, v1.2 (3906)"}, "LAND_COVER_MAPS_A02_AMAZONIA_HISTORICAL_V1.2_GEOTIFF": {"_collection": "b053b51e854d484a9657f6bfb5ebd516", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Amazonia (Eastern Amazonas region) at 30m spatial resolution in GeoTiff... (9401)"}, "LAND_COVER_MAPS_A02_AMAZONIA_STATIC_V1.2_GEOTIFF_HRLC10": {"_collection": "0bc7042123984c69aa45cb6788bfdaa0", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Amazonia (Eastern Amazonas region) at 10m spatial resolution for 2019 in Geotiff format, v1.2 (2845)"}, "LAND_COVER_MAPS_A03_SIBERIA_HISTORICAL_V1.2_GEOTIFF": {"_collection": "854cc98dbc634cdb8afa8835994428f5", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Siberia (Norther Siberia region) at 30m spatial resolution in GeoTiff... (7319)"}, "LAND_COVER_MAPS_A03_SIBERIA_STATIC_V1.2_GEOTIFF_HRLC10": {"_collection": "e7864129084c4baaa34be3a1cfaaa13d", "title": "ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Siberia (Northern Siberia region) at 10m spatial resolution for 2019 in Geotiff format, v1.2 (1971)"}, "LAND_COVER_MAPS_V2.0.7": {"_collection": "b382ebe6679d44b8b0e68ea4ef4b701c", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Land Cover Maps, Version 2.0.7 (59)"}, "LIMB_PROFILES_L3_ACE_FTS_SCISAT_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "ccbeb356a88847058159049678fe5c35", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (8)"}, "LIMB_PROFILES_L3_GOMOS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "b431fbecf73c4442ad5d7bcf80929b03", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (11)"}, "LIMB_PROFILES_L3_MEGRIDOP_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "e0329fa69dfa49d0903e132dca1c4890", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001 (0)"}, "LIMB_PROFILES_L3_MERGED_MERGED_MONTHLY_ZONAL_MEAN_V0002": {"_collection": "00a0da5d759d4d69ae866a23b84011be", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 2 (25)"}, "LIMB_PROFILES_L3_MERGED_MERGED_SEMI_MONTHLY_MEAN_V0002": {"_collection": "1f9f3e03137c48a086d008ba7b5cd48b", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Semi-Monthly Mean Profiles, Version 2 (3)"}, "LIMB_PROFILES_L3_MERGED_SAGE_CCI_OMPS_MONTHLY_ZONAL_MEAN_V0002": {"_collection": "8220babd91534bb3b83b87141c4ccc14", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles, v0002 (0)"}, "LIMB_PROFILES_L3_MIPAS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "4e106bb70a6b42d8a5a86c4635c855b9", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): MIPAS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (8)"}, "LIMB_PROFILES_L3_OSIRIS_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "f428fffb26cf4cd5b97dfb6381cb16bb", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (13)"}, "LIMB_PROFILES_L3_SCIAMACHY_ENVISAT_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "cb54bd70826842a9acf658ebabe4a104", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (11)"}, "LIMB_PROFILES_L3_SMR_ODIN_544_6_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "add104f4c4454b629dbc7648efaa1b50", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR (544.6 GHz) Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (14)"}, "LIMB_PROFILES_L3_SMR_ODIN_MONTHLY_ZONAL_MEAN_V0001": {"_collection": "fe651dbef5d44248bef70906f4b3d12b", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 (13)"}, "LT_ANALYSIS_L4_V01.1": {"_collection": "c65ce27928f34ebd92224c451c2a8bed", "title": "ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1 (7063)"}, "MERGED_CO_V1.0": {"_collection": "6242532d87d442a3acf0171d35c02e56", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): Merged CO product, version 1.0 (205)"}, "MERIS_ALAMO_L2_V2.2": {"_collection": "a6efcb0868664248b9cb212aba44313d", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2 (5261)"}, "MERIS_ALAMO_L3_V2.2": {"_collection": "11c5f6df1abc41968d0b28fe36393c9d", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2 (381)"}, "METOPA_AVHRR_L3C_0.01_V1.10_DAILY": {"_collection": "b94cbe2ae4bf45cfa8dc58e98170c07c", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10 (10363)"}, "METOPA_AVHRR_L3C_0.01_V2.00_DAILY": {"_collection": "c836ded4f8154c3ba86ee58ec0a7a7b1", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version... (9979)"}, "MS_UVAI_L3_V1.7": {"_collection": "2e656d34d016414c8d6bced18634772c", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products  from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7 (13079)"}, "MTSAT_JAMI_L3C_V1.00_MONTHLY": {"_collection": "56742f742ea14edf872c3eca99f4a61a", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci):  Monthly Multi-Functional Transport Satellite (MTSAT) level 3C (L3C) product (2009-2015), version 1.00 (609)"}, "MULTISENSOR_IRCDR_L3S_0.01_V2.00_DAILY": {"_collection": "ef8ce37b6af24469a2a4bdc31d3db27d", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version... (17403)"}, "MULTISENSOR_IRCDR_L3S_0.01_V2.00_MONTHLY": {"_collection": "785ef9d3965442669bff899540747e28", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020),... (591)"}, "MULTISENSOR_IRCDR_L3S_0.01_V3.00_DAILY": {"_collection": "e67b02b814bf4a78b9ec90b4259f0741", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024),... (20259)"}, "MULTISENSOR_IRCDR_L3S_0.01_V3.00_MONTHLY": {"_collection": "b8be3f3993e54d309fddf61ea5d3674f", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024),... (689)"}, "MULTISENSOR_IRMGP_L3S_0.05_V1.00_DAILY": {"_collection": "6775e27575124407afeebb4bb1dfaaf5", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product... (35065)"}, "MULTISENSOR_IRMGP_L3S_0.05_V1.00_MONTHLY": {"_collection": "98ca52a0bcf94fc98155b7e914aa22a0", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global... (1156)"}, "MULTISENSOR_IRMGP_L3S_0.05_V3.00_DAILY": {"_collection": "6ab837fc79e4487a9930a221b294df01", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S)... (58239)"}, "NADIR_PROFILES_L3_MERGED_V0002": {"_collection": "4eb4e801424a47f7b77434291921f889", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Nadir Ozone Profile Merged Data Product, version 2 (37)"}, "NOAA15_AVHRR_L3C_0.05_V1.50_DAILY": {"_collection": "d1929f1241e24d3cade8e933ed1f1ff8", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-15  (National Oceanic and Atmospheric Administration), level... (8085)"}, "NOAA16_AVHRR_L3C_0.05_V1.50_DAILY": {"_collection": "52ded3b1bad9432ba3f70006eb29595b", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-16  (National Oceanic and Atmospheric Administration), level... (6547)"}, "NOAA17_AVHRR_L3C_0.05_V1.50_DAILY": {"_collection": "6a22d0dd7f90487f8bbed3be85de3624", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-17  (National Oceanic and Atmospheric Administration), level... (5555)"}, "NOAA18_AVHRR_L3C_0.05_V1.50_DAILY": {"_collection": "d43fc038423246f6b5d077ad034f04cd", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-18  (National Oceanic and Atmospheric Administration), level... (11163)"}, "NOAA19_AVHRR_L3C_0.05_V1.50_DAILY": {"_collection": "3f97dca2913d47d4948dfba324f1604a", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR-3 (Advanced Very High Resolution Radiometer-3) on NOAA-19  (National Oceanic and Atmospheric Administration), level... (8451)"}, "NOAA20_VIIRS": {"_collection": "5a3b2face5604687b5bb9fd32fe96461", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on NOAA-20  (National Oceanic and Atmospheric Administration), level 3... (5096)"}, "OBS4MIPS_DWD_ESACCI-CLOUD-ATSR2-AATSR-3-0_MON": {"_collection": "be40716c1036498cb6b16b0ef25c5535", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from ATSR2 and AATSR, version 3 (140)"}, "OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-AM-3-0_MON": {"_collection": "ff5f152a3f194ab1be33543f291e65cd", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-AM), version 3 (197)"}, "OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-PM-3-0_MON": {"_collection": "919157930d7447caac6d42e84e377289", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-PM), version 3 (259)"}, "OBS4MIPS_UREADING_ESA-CCI-SST-V2-1_MON_TOS_GN_V20201130": {"_collection": "5e5da31f2ae047b997ddbbdd372d31cd", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Obs4MIPS monthly-averaged sea surface temperature data, v2.1 (2)"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V03.0": {"_collection": "6e2091cb0c8b4106921b63cd5357c97c", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost extent for the Northern Hemisphere, v3.0 (26)"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V04.0": {"_collection": "93444bc1c4364a59869e004bf9bfd94a", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0 (26)"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_ANTARCTICA": {"_collection": "25447c253cbe435b937347756fe47146", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for Antarctica, v5.0 (28)"}, "PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE": {"_collection": "d235665772ec4b558e9a89ac85595e71", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v5.0 (28)"}, "PFT_V2.0.81": {"_collection": "313854aedcb04a5eb56f711401a87396", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Plant Functional Types (PFT) Dataset, v2.0.81 (30)"}, "PHASE-2_L3C_MERIS-AATSR_V2.0": {"_collection": "2f423ac3eb244567a12b283894b869de", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MERIS+AATSR monthly gridded cloud properties,  Version 2.0 (110)"}, "PHASE-2_L3C_MODIS-AQUA_V2.0": {"_collection": "7dd46ee62153409f8e1b2b7b251177c1", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-AQUA monthly gridded cloud properties, version 2.0 (151)"}, "PHASE-2_L3C_MODIS-TERRA_V2.0": {"_collection": "f1ab07b5292f4813bd3090b51d270aa8", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0 (180)"}, "RANDOLPH_GLACIER_INVENTORY_GRIDDED_V5.0": {"_collection": "5daf1ff8dd2941a18bb4120ceea95721", "title": "ESA Glaciers Climate Change Initiative (Glaciers CCI): Randolph Glacier Inventory gridded data product, v5.0 (4)"}, "RD_RD-ALTI_V1.0": {"_collection": "44c930e1388f40728884fbdf7e28c109", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Altimetry-based River Discharge product, v1.0 (101)"}, "RD_RD-COMBINED_V1.0": {"_collection": "d32244e674dd438ca4d321560daad755", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Combined river discharge product, v1.0 (0)"}, "RD_RD-MULTI_V1.2": {"_collection": "a8422dd3766c447d8b5fa80920649f31", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2 (0)"}, "SCFG_AATSR_V1.0": {"_collection": "e7e31b86b2644e0da69090bc37360c97", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 snow on ground (SCFG) from AATSR (2002 \u00e2\u0080\u0093 2012), version 1.0 (3559)"}, "SCFG_ATSR-2_V1.0": {"_collection": "0aeba0c203c2447b9553a78f99d3a276", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 snow on ground (SCFG) from ATSR-2 (1995 \u00e2\u0080\u0093 2003), version 1.0 (2602)"}, "SCFG_AVHRR_MERGED_V2.0": {"_collection": "3f034f4a08854eb59d58e1fa92d207b6", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0 (13406)"}, "SCFG_AVHRR_SINGLE_V3.0": {"_collection": "56ff07acabab42888afe2d20b488ec49", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0 (40711)"}, "SCFG_AVHRR_SINGLE_V4.0": {"_collection": "80d96e3a14854420b6f742d70877c431", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0 (37062)"}, "SCFG_CRYOCLIM_V1.0": {"_collection": "f4654030223445b0bac63a23aaa60620", "title": "ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0 (13697)"}, "SCFG_MODIS_V2.0": {"_collection": "8847a05eeda646a29da58b42bdf2a87c", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0 (7561)"}, "SCFG_MODIS_V3.0": {"_collection": "80567d38de3f4b038ee6e6e53ed1af8a", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0 (8278)"}, "SCFG_MODIS_V4.0": {"_collection": "375ffdb8f0a445e380b4b9548655f5f9", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2023), version 4.0 (8644)"}, "SCFG_SLSTR_V1.0": {"_collection": "38a71d034b5c4097821de29ee3bc2498", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0 (853)"}, "SCFV_AATSR_V1.0": {"_collection": "d7773cb976d64b1c900a518773428df6", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 viewable snow (SCFV) from AATSR (2002 \u00e2\u0080\u0093 2012), version 1.0 (3559)"}, "SCFV_ATSR-2_V1.0": {"_collection": "70061acca284432ca31fd8a5cbd604d0", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction \u00e2\u0080\u0093 viewable snow (SCFV) from ATSR-2 (1995 \u00e2\u0080\u0093 2003), version 1.0 (2602)"}, "SCFV_AVHRR_MERGED_V2.0": {"_collection": "763eb87e0682446cafa8c74488dd5fb8", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0 (13406)"}, "SCFV_AVHRR_SINGLE_V3.0": {"_collection": "7491427f8c3442ce825ba5472c224322", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0 (40711)"}, "SCFV_AVHRR_SINGLE_V4.0": {"_collection": "3c71c04cf08a410fac2c680cbf88cfd7", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2023), version 4.0 (37062)"}, "SCFV_MODIS_V2.0": {"_collection": "ebe625b6f77945a68bda0ab7c78dd76b", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2020), version 2.0 (7560)"}, "SCFV_MODIS_V3.0": {"_collection": "e955813b0e1a4eb7af971f923010b4a3", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0 (8278)"}, "SCFV_MODIS_V4.0": {"_collection": "bc13bb02a958449aac139853c4638f32", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2023), version 4.0 (8644)"}, "SCFV_SLSTR_V1.0": {"_collection": "f5dce1f7bec2447093cf460a4d3ba2c2", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0 (853)"}, "SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.0": {"_collection": "34a15b96f1134d9e95b9e486d74e49cf", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.0 (2055)"}, "SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.1": {"_collection": "8978580336864f6d8282656d58771b32", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.1 (2274)"}, "SEA_ICE_CONCENTRATION_L4_AMSR_25KM_V2.1": {"_collection": "f17f146a31b14dfd960cde0874236ee5", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1 (10279)"}, "SEA_ICE_CONCENTRATION_L4_AMSR_50KM_V2.1": {"_collection": "5f75fcb0c58740d99b07953797bc041e", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50km grid spacing, version 2.1 (10277)"}, "SEA_ICE_CONCENTRATION_L4_SSMI_SSMIS_12.5KM_V3.0": {"_collection": "eade27004395466aaa006135e1b2ad1a", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS) (21813)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_NH": {"_collection": "5b6033bfb7f241e89132a83fdc3d5364", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from CryoSat-2   on the satellite swath (L2P), v2.0 (1429)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_SH": {"_collection": "fbfae06e787b4fefb4b03cba2fd04bc3", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0 (2339)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_NH": {"_collection": "c6504378f78c4ecd9f839b0434023eff", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from CryoSat-2   on the satellite swath (L2P), v3.0 (2077)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_SH": {"_collection": "861ad3c7f3a34ebd8be6f618a92bd8e3", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0 (3453)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V4.0_NH": {"_collection": "5487186f657644f798a1e6828d8bed3c", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0 (2939)"}, "SEA_ICE_THICKNESS_L2P_CRYOSAT2_V4.0_SH": {"_collection": "edfda165965749cbb8ba7c2e0d30a6b4", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0 (4910)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_NH": {"_collection": "54e2ee0803764b4e84c906da3f16d81b", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0 (2064)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_SH": {"_collection": "550d938da3184d0ca44a06a4c0c14ffa", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0 (3542)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_NH": {"_collection": "92eb2ba942074bec804af6a8b5436bee", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0 (2063)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_SH": {"_collection": "af96a1ec493f49caa39dc912d15f2b17", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0 (3446)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V4.0_NH": {"_collection": "5e58f03c4d664b2b9e4e16f2d12e0c4f", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0 (2076)"}, "SEA_ICE_THICKNESS_L2P_ENVISAT_V4.0_SH": {"_collection": "1ba530ada3bc4d61a2141e6fa68315f1", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0 (3446)"}, "SEA_ICE_THICKNESS_L2P_ERS2_V4.0_NH": {"_collection": "17c6b09a5466406c800c2978f17b8372", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from ERS-2 on the satellite swath (L2P), v4.0 (1661)"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3A_V4.0_NH": {"_collection": "1ac5ba6a41d34007bd64bd83b91dc09b", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3A on the satellite swath (L2P), v4.0 (1699)"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3A_V4.0_SH": {"_collection": "fe7025b9938346dc99f31a61ad7d4e01", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3A on the satellite swath (L2P), v4.0 (2770)"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3B_V4.0_NH": {"_collection": "26b398778ac441f889e5b873c73a6343", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3B on the satellite swath (L2P), v4.0 (1235)"}, "SEA_ICE_THICKNESS_L2P_SENTINEL3B_V4.0_SH": {"_collection": "b8359626272e4f7ba9e2e821b5eab032", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3B on the satellite swath (L2P), v4.0 (2000)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_NH": {"_collection": "ff79d140824f42dd92b204b4f1e9e7c2", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0 (51)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_SH": {"_collection": "48fc3d1e8ada405c8486ada522dae9e8", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0 (81)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_NH": {"_collection": "45b5b1e556da448089e2b57452f277f5", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0 (70)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_SH": {"_collection": "67b003a864cd4e9ebeccd29fbdf4447e", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0 (115)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V4.0_NH": {"_collection": "887ebbe3aa4345f3bbcbe2dd1834d955", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0 (99)"}, "SEA_ICE_THICKNESS_L3C_CRYOSAT2_V4.0_SH": {"_collection": "5cd23b8adfe149e59f218e9b1c9364b3", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0 (163)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_NH": {"_collection": "f4c34f4f0f1d4d0da06d771f6972f180", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0 (72)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_SH": {"_collection": "b1f1ac03077b4aa784c5a413a2210bf5", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0 (121)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_NH": {"_collection": "83b11005a3d7472eb57df4f90933c462", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0 (70)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH": {"_collection": "ab6a05baacce4c848d137a0bc9921e6e", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0 (115)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V4.0_NH": {"_collection": "a0120f31bcd94f8da74ddfebe658773d", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0 (70)"}, "SEA_ICE_THICKNESS_L3C_ENVISAT_V4.0_SH": {"_collection": "f4631b76dbae432ead809836805944d7", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0 (115)"}, "SEA_ICE_THICKNESS_L3C_ERS2_V4.0_NH": {"_collection": "dfc03980decc4e50b111d5981358930e", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from ERS-2 on a monthly grid (L3C), v4.0 (57)"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3A_V4.0_NH": {"_collection": "87623591b26042a2aa1bac231f657923", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3A on a monthly grid (L3C), v4.0 (57)"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3A_V4.0_SH": {"_collection": "f4d89a6e59724ecbb7a4b8d4a8df622d", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3A on a monthly grid (L3C), v4.0 (92)"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3B_V4.0_NH": {"_collection": "e33393b68592473cab41701219c74fd7", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Sentinel-3B on a monthly grid (L3C), v4.0 (42)"}, "SEA_ICE_THICKNESS_L3C_SENTINEL3B_V4.0_SH": {"_collection": "db0aee3dbdc54ab0981bca2e632f3be3", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Sentinel-3B on a monthly grid (L3C), v4.0 (67)"}, "SENTINEL3A_SLSTR_L3C_0.01_V3.00_DAILY": {"_collection": "330b7c922a37420fabb3425671d7d7c6", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version... (3291)"}, "SENTINEL3A_SLSTR_L3C_0.01_V3.00_MONTHLY": {"_collection": "aa8268e2ca0e48d98aee372795722253", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020),... (115)"}, "SENTINEL3A_SLSTR_L3C_0.01_V4.00_DAILY": {"_collection": "4a40c6fe12cc4c0786608065da06d287", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023),... (5425)"}, "SENTINEL3B_SLSTR_L3C_0.01_V3.00_DAILY": {"_collection": "5f66a881adf846bfaad58b0e6068f0ea", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version... (1475)"}, "SENTINEL3B_SLSTR_L3C_0.01_V3.00_MONTHLY": {"_collection": "b54d5f1c08594879a05929ce09951c56", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020),... (53)"}, "SENTINEL3B_SLSTR_L3C_0.01_V4.00_DAILY": {"_collection": "49b4836314db4fec8d05d14b85a8614d", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023),... (3659)"}, "SNPP_VIIRS": {"_collection": "511a4ab3100b4010aab910b5fdaa7c4e", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on Suomi National Polar-orbiting Partnership (SNPP), level 3 collated (L3C)... (9345)"}, "SSMI_SSMIS_L3C_V2.33": {"_collection": "a7e811fe11d34df5abac6f18c920bbeb", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33 (18917)"}, "SWE_MERGED_V2.0": {"_collection": "4647cc9ad3c044439d6c643208d3c494", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP)  (1979 \u00e2\u0080\u0093 2020), version 2.0 (7938)"}, "SWE_MERGED_V3.1": {"_collection": "9d9bfc488ec54b1297eca2c9662f9c81", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.1 (8972)"}, "SWE_MERGED_V4.0": {"_collection": "edf8abd23f4a40aabd4d52e48dec06ea", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2023), version 4.0 (9576)"}, "TCWV-LAND_L3_V3.2_0.05DEG_DAILY": {"_collection": "fc3310aaa2fe4a26a29a63642e1164f2", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.2 (5664)"}, "TCWV-LAND_L3_V3.2_0.05DEG_MONTHLY": {"_collection": "d1c2018e17d54df384fdbf562d2a9e8b", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.05 degree resolution, version 3.2 (187)"}, "TCWV-LAND_L3_V3.2_0.5DEG_DAILY": {"_collection": "5bd687c7ea624e3aa68c51e242ae828a", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.5 degree resolution, version 3.2 (5664)"}, "TCWV-LAND_L3_V3.2_0.5DEG_MONTHLY": {"_collection": "80ad33b237084a8dbfb81ec5414e68dd", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.5 degree resolution, version 3.2 (187)"}, "TERRA_MODIS_L3C_0.01_V3.00_DAILY": {"_collection": "58a01734f841466daa1837353aee5ff8", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version... (13651)"}, "TERRA_MODIS_L3C_0.01_V3.00_MONTHLY": {"_collection": "32d7bc64c7b740e9ad7a43589ab91592", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018),... (455)"}, "TERRA_MODIS_L3C_0.01_V4.00_DAILY": {"_collection": "d56a6215ce394ddd8dff6bea5dbb0780", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2021),... (15839)"}, "TIMESERIES_SLB_ELEMENTS_V2.2": {"_collection": "17c2ce31784048de93996275ee976fff", "title": "ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.2 (2)"}, "TOTAL_COLUMNS_L3_MERGED_V0100": {"_collection": "0d2260ad4e2c42b6b14fe5b3308f5eaa", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01 (187)"}, "TROPOMI_HCHO_L3_V2.0_MONTHLY": {"_collection": "6081f484b28e42a08e75438c9384e877", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0 (0)"}, "V02.31_30DAYS": {"_collection": "7813eb75a131474a8d908f69c716b031", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v2.31, for 2010 to 2019 (243)"}, "V02.31_7DAYS": {"_collection": "eacb7580e1b54afeaabb0fd2b0a53828", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019 (3655)"}, "V03.21_30DAYS": {"_collection": "7da8723b16e94771be1a2717d8a6e2fe", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020 (259)"}, "V03.21_7DAYS": {"_collection": "fad2e982a59d44788eda09e3c67ed7d5", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v03.21, for 2010 to 2020 (3918)"}, "V04.41_GLOBALV4.41_30DAYS": {"_collection": "7cc16c0d8d2f49278ed5ebf8341ed40b", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a global grid, v04.41, for 2010 to 2022 (309)"}, "V04.41_GLOBALV4.41_7DAYS": {"_collection": "0d0f4a942a144d9cab9263de3949a5d6", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a global grid, v04.41, for 2010 to 2022 (4687)"}, "V04.41_NHV4.41_30DAYS": {"_collection": "ecc355e395ed4c5597c613ae7f9c53b0", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022 (309)"}, "V04.41_NHV4.41_7DAYS": {"_collection": "471698c7db45451cbf3b7d834ecab9fd", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022 (4687)"}, "V04.41_SHV4.41_30DAYS": {"_collection": "249d2907975a45a8b078e1a8bd4a7343", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022 (309)"}, "V04.41_SHV4.41_7DAYS": {"_collection": "a601f52222ba486aa9f0a18db3a009b7", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022 (4687)"}, "V05.5_GLOBALV5.5_30DAYS": {"_collection": "3339dec1fbd94599802aba7f1c665679", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023 (336)"}, "V05.5_GLOBALV5.5_7DAYS": {"_collection": "4321d9b540fe48f8943179aa3ef06c79", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023 (5105)"}, "V05.5_NHV5.5_30DAYS": {"_collection": "3d3e52e597fe40b0ab5984cc0ee82de3", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023 (336)"}, "V05.5_NHV5.5_7DAYS": {"_collection": "5b48a34fb4134bbd99acace7767e5b3e", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023 (5105)"}, "V05.5_SHV5.5_30DAYS": {"_collection": "0397ca6f625e47f489684bad2bd533bb", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023 (336)"}, "V05.5_SHV5.5_7DAYS": {"_collection": "c302b1dceb8a4c39a31d18b60f236d09", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023 (5105)"}, "V3_RELEASE_ALTIMETER_L2P": {"_collection": "8cb46a5efaa74032bf1833438f499cc3", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetry, L2P product, version 3 (709369)"}, "V3_RELEASE_ALTIMETER_L3_V3.0": {"_collection": "e6af67fca81c40b7bb3eddaadde06909", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height from altimetry, L3 product, version 3 (6928)"}, "V3_RELEASE_ALTIMETER_L4_V3.0": {"_collection": "9c350d4ff7ee438f9f1fc7252cbb2282", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height from altimetry,  L4 product, version 3 (225)"}, "V3_RELEASE_INSITU_MICROSEISM_V1.0": {"_collection": "2fa69cb20ac84cec9e16739e72fd7da1", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Seismic spectrograms from broadband seismometers, release version 3 (14220)"}, "V3_RELEASE_SAR_L2P_ENVISAT_ISSP_V1.1": {"_collection": "8cd1a9beabc648c8a2c91fa68791d9d7", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, release version 3 (166980)"}, "V3_RELEASE_SAR_L2P_SENTINEL1_ISSP_V1.0": {"_collection": "fe02d5eef9ef4ad889d1917ccad3b35f", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3 (119659)"}, "V3_RELEASE_SAR_L2P_SENTINEL1_SWH_V1.0": {"_collection": "81bab8b378db443db4482a5961a58ec1", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3. (127451)"}, "V6.0-RELEASE_CLIMATOLOGY_NETCDF_MONTHLY_V6.0": {"_collection": "690fdf8f229c4d04a2aa68de67beb733", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0 (14)"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_ALL_PRODUCTS": {"_collection": "0875b4675f1e46ebadb526e0b95505c5", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0 (12576)"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_CHLOR_A": {"_collection": "b0ec72a28b6a4829a33ed9adc215d5bc", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0 (12576)"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_IOP": {"_collection": "90682bac7d0e4e418085f30eba43dfba", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0 (12576)"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_KD": {"_collection": "8ecae26f390b4938b67a97cbce3ecd8b", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0 (12577)"}, "V6.0-RELEASE_GEOGRAPHIC_NETCDF_RRS": {"_collection": "a0782135bcd04d77a1dae4aa71fba47c", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0 (12575)"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_ALL_PRODUCTS": {"_collection": "86d360431f3b4184b89cdd1cd707bb33", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0 (12576)"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_CHLOR_A": {"_collection": "474ac06235e54e6cb0ec6eed635e1213", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0 (12577)"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_IOP": {"_collection": "024292dcda5d42ceb326850f89f8b40d", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0 (12576)"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_KD": {"_collection": "3bdb21a4cd004e5f8cc148fea5f1d4e3", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0 (12577)"}, "V6.0-RELEASE_SINUSOIDAL_NETCDF_RRS": {"_collection": "8b9d461f245b4efd8ea9fa080366e3b1", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0 (12576)"}, "VERSION3_L3C_ATSR2-AATSR_V3.0": {"_collection": "326bf808aedd41fd85594fc06678d20a", "title": "ESA Cloud Climate Change Initiative (Cloud_cci):  ATSR2-AATSR monthly gridded cloud properties, version 3.0 (199)"}, "VERSION3_L3C_AVHRR-AM_V3.0": {"_collection": "fb3750f5b2544403873f8788b3ed7817", "title": "ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0 (306)"}, "VERSION3_L3C_AVHRR-PM_V3.0": {"_collection": "004fd44ff5124174ad3c03dd2c67d548", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): AVHRR-PM monthly gridded cloud properties, version 3.0 (417)"}, "VERTICAL_LAND_MOTIONS_TUM_MAPS_V1": {"_collection": "91d1fbc572224b9c86f6c14ba9109479", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania,  v1 (1)"}, "WATER_BODIES_V4.0": {"_collection": "7e139108035142a9a1ddd96abcdfff36", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Water Bodies Map, v4.0 (5)"}, "WL_V1.1": {"_collection": "c5f0aa806ec444b4a4209b49efc4bb65", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Nadir radar altimeters Water Level product, v1.1 (747)"}, "WL_V2.0": {"_collection": "c5e585820d1240e89eea85ff2c9b4569", "title": "ESA River Discharge Climate Change Initiative (RD_cci):  Nadir radar altimeters Water Level product, v2.0 (871)"}, "WV-STRATO_L3S_V3.3": {"_collection": "92824e3ec2e44a58b10048df3209b99c", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3 (2)"}, "XTRACK_ALES_SLA_ENVISAT_SARAL_SLA_V1.1": {"_collection": "2e3a3408af2f4e918458c09d4e5f7460", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions from ENVISAT (2002-2010) and SARAL (2013-2016) satellite altimetry, v1.1 (1743)"}, "XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V2.2": {"_collection": "90049a6555d1480bb5ce9637051dede8", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2 (757)"}, "XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V3.0": {"_collection": "4be5091f70f04e50b1c1da79d2d89a97", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2021, v3.0 (1547)"}}}, "geodes": {"collections_config": {"FLATSIM_AUXILIARYDATA": {"constellation": "sentinel-1", "description": "This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-10-09T00:00:00Z", "2025-07-13T00:00:00Z"]]}}, "instruments": ["SAR-C"], "keywords": ["amplitude", "auxiliary-data", "auxiliarydata:l2", "auxiliarydata:l3", "average", "burst-selection", "coherence", "deformation", "elevation", "flatsim-auxiliarydata", "ground-to-radar", "insar", "landslides", "lookup-tables", "radar-to-ground", "sar-c", "satellite", "sentinel-1", "spectral-diversity", "tectonics", "unit-vector", "unwrapping", "volcanology"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "AUXILIARYDATA:L2,AUXILIARYDATA:L3", "title": "FormaTerre S1 Auxiliary Data"}, "FLATSIM_INTERFEROGRAM": {"constellation": "sentinel-1", "description": "This set of products contains interferograms. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-10-09T23:02:19Z", "2025-07-13T23:53:08Z"]]}}, "instruments": ["SAR-C"], "keywords": ["atmospheric-phase-screen", "coherence", "deformation", "flatsim-interferogram", "ground-geometry", "hydrology", "insar", "interferogram", "interferogram:l2", "landslides", "radar-geometry", "rock-glaciers", "sar-c", "satellite", "sentinel-1", "subsidence", "tectonics", "unwrapped", "volcanology", "wrapped"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "INTERFEROGRAM:L2", "title": "FormaTerre S1 Interferograms"}, "FLATSIM_TIMESERIE": {"constellation": "sentinel-1", "description": "This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-10-09T00:00:00Z", "2025-07-13T00:00:00Z"]]}}, "instruments": ["SAR-C"], "keywords": ["data-cube", "deformation", "flatsim-timeserie", "ground-geometry", "insar", "landslides", "mean-los-velocity", "mean-velocity:l3", "quality-maps", "radar-geometry", "sar-c", "satellite", "sentinel-1", "stack-list", "tectonics", "time-serie", "timeserie:l3", "volcanology"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "TIMESERIE:L3,MEAN VELOCITY:L3", "title": "FormaTerre S1 Time Series"}, "L1B_OMP_SAGSA_GGM_AOD_3HOURS": {"description": "The ERA-Interim/TUGO dealiasing products provide the time-variable 3D gravitational potential associated with atmospheric and oceanic masses. A mean value (2001\u20132010) has been removed, so they only represent the temporal component. They are computed from ERA-Interim reanalysis (atmosphere) and the TUGO barotropic model (ocean), with integration of the gravitational potential over pressure levels. The solid Earth response is included through Love numbers.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [[null, null]]}}, "keywords": ["atmosphere", "dealiasing", "gravitationnal-attraction", "l1b", "l1b-omp-sagsa-ggm-aod-3hours", "non-tidal-variability", "ocean", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L1B", "title": "L1B GAA/GAB 3H"}, "L2A_CNES_SAGSA_GGM_CHO_1MONTH": {"constellation": "GRACE,GRACE-FO", "description": "CNES/GRGS monthly gravitationnal geopotential solutions obtained through a Cholesky decompositition. Solution provided as part of the COST-G gravity field solution combination exercise. This solution is not the CNES reference solution, which is obtained through a singular value decomposition.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2025-10-31T23:59:59Z"]]}}, "keywords": ["cholesky", "geopotential", "global-gravity-field-model", "grace", "grace-fo", "l2a", "l2a-cnes-sagsa-ggm-cho-1month", "monthly", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Geopotential Solution, Cholesky, monthly"}, "L2A_CNES_SAGSA_GGM_SVD_10DAYS": {"constellation": "GRACE,GRACE-FO", "description": "CNES/GRGS 10-days gravitationnal geopotential solutions obtained through a singular value decomposition", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-07-29T00:00:00Z", "2025-10-25T23:59:59Z"]]}}, "keywords": ["10-days", "geopotential", "global-gravity-field-model", "grace", "grace-fo", "l2a", "l2a-cnes-sagsa-ggm-svd-10days", "satellite", "svd"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Geopotential solution, Singular Value Decomposition, 10 Days"}, "L2A_CNES_SAGSA_GGM_SVD_1MONTH": {"constellation": "GRACE,GRACE-FO", "description": "CNES/GRGS monthly gravitationnal geopotential solutions obtained through a singular value decomposition", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2025-10-31T23:59:59Z"]]}}, "keywords": ["geopotential", "global-gravity-field-model", "grace", "grace-fo", "l2a", "l2a-cnes-sagsa-ggm-svd-1month", "monthly", "satellite", "svd"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Geopotential solution, Singular Value Decomposition, monthly"}, "L2A_OMP_SAGSA_GGM_GAA_10DAYS": {"description": "The GAA 10-days dealiasing products contain the time-variable 3D gravitational potential of the atmosphere averaged over 10-day periods from the ERA-Interim 3-hourly dealiasing products. A mean value of the original grids based on 10 years of data (2001-2010) has been subtracted from the time series, therefore these products represent only the time-variable part of the atmosphere attraction.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-03-31T00:00:00Z", "2017-07-09T23:59:59Z"]]}}, "keywords": ["atmosphere", "dealiasing", "gravitationnal-attraction", "l2a", "l2a-omp-sagsa-ggm-gaa-10days", "non-tidal-variability", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Atmospheric dealiasing GAA 10 Days"}, "L2A_OMP_SAGSA_GGM_GAA_1MONTH": {"description": "The GAA monthly dealiasing products contain the time-variable 3D gravitational potential of the atmosphere averaged over calendar months from the ERA-Interim 3-hourly dealiasing products. A mean value of the original grids based on 10 years of data (2001-2010) has been subtracted from the time series, therefore these products represent only the time-variable part of the atmosphere attraction.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2017-07-31T23:59:59Z"]]}}, "keywords": ["atmosphere", "dealiasing", "gravitationnal-attraction", "l2a", "l2a-omp-sagsa-ggm-gaa-1month", "non-tidal-variability", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Atmospheric dealiasing GAA monthly"}, "L2A_OMP_SAGSA_GGM_GAB_10DAYS": {"description": "The GAB 10-days dealiasing products contain the time-variable 3D gravitational potential of the ocean averaged over 10-day periods from the TUGO 3-hourly dealiasing products. A mean value of the original grids based on 10 years of data (2001-2010) has been subtracted from the time series, therefore these products represent only the time-variable part of the ocean attraction.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-03-31T00:00:00Z", "2017-07-09T23:59:59Z"]]}}, "keywords": ["dealiasing", "gravitationnal-attraction", "l2a", "l2a-omp-sagsa-ggm-gab-10days", "non-tidal-variability", "ocean", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Oceanic dealiasing GAB 10 Days"}, "L2A_OMP_SAGSA_GGM_GAB_1MONTH": {"description": "The GAB monthly dealiasing products contain the time-variable 3D gravitational potential of the ocean averaged over each calendar month from the TUGO 3-hourly dealiasing products. A mean value of the original grids based on 10 years of data (2001-2010) has been subtracted from the time series, therefore these products represent only the time-variable part of the ocean attraction.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2017-07-31T23:59:59Z"]]}}, "keywords": ["dealiasing", "gravitationnal-attraction", "l2a", "l2a-omp-sagsa-ggm-gab-1month", "non-tidal-variability", "ocean", "satellite"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2A", "title": "Oceanic dealiasing GAB monthly"}, "L2B_CNES_SAGSA_GFQ_EWH_SVD_10DAYS": {"constellation": "GRACE,GRACE-FO", "description": "10-days period grids of the surface mass anomalies relative to a static reference field expressed in equivalent water height", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-07-29T00:00:00Z", "2025-10-25T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-ewh-svd-10days", "satellite", "star-camera-assembly", "water-cycle"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Equivalent Water Height, Singular Value Decomposition, 10-days"}, "L2B_CNES_SAGSA_GFQ_EWH_SVD_1MONTH": {"constellation": "GRACE,GRACE-FO", "description": "Monthly grids of the surface mass anomalies relative to a static reference field expressed in equivalent water height", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2025-10-31T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS", "SLR"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-ewh-svd-1month", "satellite", "slr", "star-camera-assembly", "water-cycle"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Equivalent Water Height, Singular Value Decomposition, monthly"}, "L2B_CNES_SAGSA_GFQ_GGA_SVD_10DAYS": {"constellation": "GRACE,GRACE-FO", "description": "10-days period grids of the Earth gravity anomalies, relative to a static reference field.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-07-29T00:00:00Z", "2025-10-25T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-gga-svd-10days", "satellite", "star-camera-assembly"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Gravity anomalies, Singular Value Decomposition, 10-days"}, "L2B_CNES_SAGSA_GFQ_GGA_SVD_1MONTH": {"constellation": "GRACE,GRACE-FO", "description": "Monthly grids of the Earth gravity anomalies, relative to a static reference field.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2025-10-31T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS", "SLR"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-gga-svd-1month", "satellite", "slr", "star-camera-assembly"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Gravity anomalies, Singular Value Decomposition, monthly"}, "L2B_CNES_SAGSA_GFQ_GGH_SVD_10DAYS": {"constellation": "GRACE,GRACE-FO", "description": "10-days period grids of the Earth geoid heights variations, relative to a static reference field.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-07-29T00:00:00Z", "2025-10-25T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-ggh-svd-10days", "satellite", "star-camera-assembly"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Geoid Height anomalies , Singular Value Decomposition, 10-days"}, "L2B_CNES_SAGSA_GFQ_GGH_SVD_1MONTH": {"constellation": "GRACE,GRACE-FO", "description": "Monthly grids of the Earth geoid heights variations, relative to a static reference field.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-01T00:00:00Z", "2025-10-31T23:59:59Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS", "SLR"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l2b", "l2b-cnes-sagsa-gfq-ggh-svd-1month", "satellite", "slr", "star-camera-assembly"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L2B", "title": "Geoid Height anomalies, Singular Value Decomposition, monthly"}, "L3_CNES_SAGSA_ENSEMBLE_1MONTH_expert": {"constellation": "GRACE,GRACE-FO", "description": "CNES ensemble including 120 gravity field solutions obtained from the combination of 5 production centers, 3 geocenter corrections, 2 glacial isostatic adjustment corrections, 2 SLR corrections for low degrees (C20 dring the full observation period and C30 after August 2016) and 2 DDK filters.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-16T00:00:00Z", "2025-08-16T12:00:00Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS", "SLR7"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l3", "l3-cnes-sagsa-ensemble-1month-expert", "satellite", "slr7", "star-camera-assembly", "water-cycle"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L3", "title": "SAGSA GRACE Ensemble"}, "L3_CNES_SAGSA_ENSEMBLE_1MONTH_public": {"constellation": "GRACE,GRACE-FO", "description": "Average of the CNES ensemble, with uncertainties obtained from the CNES ensemble distribution.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-04-16T00:00:00Z", "2025-08-16T12:00:00Z"]]}}, "instruments": ["Star Camera Assembly", "Accelerometer", "K-Band Ranging", "GPS", "SLR8"], "keywords": ["accelerometer", "gps", "grace", "grace-fo", "gravity-field", "k-band-ranging", "l3", "l3-cnes-sagsa-ensemble-1month-public", "satellite", "slr8", "star-camera-assembly", "water-cycle"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "L3", "title": "SAGSA GRACE Ensemble average"}, "PEPS_S1_L1": {"constellation": "sentinel-1", "description": "Sentinel-1 Level-1 products are the baseline products for the majority of users from which higher levels are derived. From data in each acquisition mode, the Instrument Processing Facility (IPF)  generates focused Level-1 Single Look Complex (SLC) products and Level-1 Ground Range Detected (GRD) products.  SAR parameters that vary with the satellite position in orbit, such as azimuth FM rate, Doppler centroid frequency and terrain height, are periodically updated to ensure the homogeneity of the scene when processing a complete data take. Similarly, products generated from WV data can contain any number of vignettes, potentially up to an entire orbit's worth. All Level-1 products are geo-referenced and time tagged with zero Doppler time at the centre of the swath. Geo-referencing is corrected for the azimuth bi-static bias by taking into account the pulse travel time delta between the centre of the swath and the range of each geo-referenced point. A Level-1 product can be one of the following two types: Single Look Complex (SLC) products or Ground Range Detected (GRD) products Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["0088-06-27T14:37:21.817Z", "2026-05-11T10:06:01.458Z"]]}}, "instruments": ["SAR-C"], "keywords": ["backscatter", "csar", "grd", "imagingradars", "level1", "peps-s1-l1", "s1", "sar", "sar-c", "satellite", "sentinel-1", "sentinel1", "slc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1", "title": "PEPS Sentinel-1 Level1"}, "PEPS_S1_L2": {"constellation": "sentinel-1", "description": "Sentinel-1 Level-2 consists of geolocated geophysical products derived from Level-1. There is only one standard Level-2 product for wind, wave and currents applications - the Level-2 Ocean (OCN) product. The OCN product may contain the following geophysical components derived from the SAR data: - Ocean Wind field (OWI) - Ocean Swell spectra (OSW) - Surface Radial Velocity (RVL). OCN products are generated from all four Sentinel-1 imaging modes. From SM mode, the OCN product will contain all three components. From IW and EW modes, the OCN product will only contain OWI and RVL. From WV modes, the OCN product will only contain OSW and RVL.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-30T13:31:51.933Z", "2026-05-11T10:06:01.458Z"]]}}, "instruments": ["SAR-C"], "keywords": ["csar", "level2", "oceans", "oceanswellspectra", "oceanwindfield", "ocn", "peps-s1-l2", "s1", "sar", "sar-c", "satellite", "sentinel-1", "sentinel1", "surfaceradialvelocity", "wavedirection", "waveheight", "wavelength", "waveperiod", "windstress"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level2", "title": "PEPS Sentinel-1 Level2"}, "PEPS_S2_L1C": {"constellation": "sentinel-2", "description": "Sentinel-2 L1C tiles acquisition and storage from PEPS. Data are provided per S2 tile.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-07-04T10:10:06.027Z", "2026-05-11T09:07:11.024Z"]]}}, "instruments": ["MSI"], "keywords": ["l1c", "msi", "peps-s2-l1c", "reflectance", "s2", "satellite", "sentinel-2", "sentinel2", "toareflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "PEPS Sentinel-2 L1C tiles"}, "PEPS_S3_L1": {"constellation": "sentinel-3", "description": "Sea surface topography measurements to at least the level of quality of the ENVISAT altimetry system, including an along track SAR capability of CRYOSAT heritage for improved measurement quality in coastal zones and over sea-ice", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2022-04-20T18:46:06.819Z", "2026-05-09T23:40:31.588Z"]]}}, "instruments": ["SRAL"], "keywords": ["altimetry", "l1", "level1", "peps-s3-l1", "s3", "satellite", "sentinel-3", "sentinel3", "sral", "ssh", "stm", "swh", "windspeed"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1", "title": "GDH Sentinel-3 L1 STM Level-1 products"}, "SWH_SPOT123_L1": {"constellation": "spot-1,spot-2,spot-3", "description": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1986-02-23T08:53:16Z", "2009-07-25T10:01:17Z"]]}}, "instruments": ["HRV1", "HRV2"], "keywords": ["biosphere", "clouds", "earth-observation-satellites", "glacier", "habitat", "hrv1", "hrv2", "lake", "level1a", "river", "satellite", "satellite-image", "spot-1", "spot-2", "spot-3", "swh-spot123-l1", "vegetation", "volcano"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1a", "title": "SWH SPOT1-2-3 Level1A"}, "SWH_SPOT4_L1": {"constellation": "spot-4", "description": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1998-03-27T09:34:32Z", "2013-06-19T17:17:42Z"]]}}, "instruments": ["HRVIR1", "HRVIR2"], "keywords": ["biosphere", "clouds", "earth-observation-satellites", "glacier", "habitat", "hrvir1", "hrvir2", "lake", "level1a", "river", "satellite", "satellite-image", "spot-4", "swh-spot4-l1", "vegetation", "volcano"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1a", "title": "SWH SPOT4 Level1A"}, "SWH_SPOT5_L1": {"constellation": "spot-5", "description": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-06-19T10:21:30Z", "2015-03-29T17:12:31Z"]]}}, "instruments": ["HRG1", "HRG2", "HRS"], "keywords": ["biosphere", "clouds", "earth-observation-satellites", "glacier", "habitat", "hrg1", "hrg2", "hrs", "lake", "level1a", "river", "satellite", "satellite-image", "spot-5", "swh-spot5-l1", "vegetation", "volcano"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1a", "title": "SWH SPOT5 Level1A"}, "THEIA_BIOPHY_SENTINEL2_L2B": {"constellation": "sentinel-2", "description": "Biophysical variables characteristic of vegetation : Leaf Area Index (LAI) or  GAI (Green Area Index) - CCC (Canopy Chlorophyll Content) - fAPAR (fraction of Absorbed Photosynthetically Active Radiation) - green fraction of vegetation fCover or  FVC (Fractional Vegetation Cover) NDVI (Normalized Difference Vegetation Index) - LAI and FAPAR have been recognized as ECVs (Essential Climate Variables) by the Global Climate Observing System - GCOS", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2026-03-28T10:19:02.371Z", "2026-05-08T11:27:55.421Z"]]}}, "instruments": ["MSI"], "keywords": ["bio", "biosphere", "canopy-chlorophyll-content", "ccc", "fapar", "fraction-of-absorbed-photosynthetically-active-radiation", "fractional-vegetation-cover", "fvc", "gai", "green-area-index", "l2b", "lai", "leaf-area-index", "msi", "ndvi", "normalized-difference-vegetation-index", "s2", "satellite", "sentinel", "sentinel-2", "sentinel2", "theia-biophy-sentinel2-l2b", "vegetation"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2b", "title": "THEIA SENTINEL2 BIOPHY L2B"}, "THEIA_OSO_RASTER_L3B": {"constellation": "sentinel-2", "description": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The classification raster is a single raster covering the whole French metropolitan territory. It has a spatial resolution of 10 m. It results from the processing of the complete Sentinel-2 time series of the reference year using the iota\u00b2 processing chain. A Random Forest classification model is calibrated using a training dataset derived from a combination of several national and international vector data sources (BD TOPO IGN, Corine Land Cover, Urban Atlas, R\u00e9f\u00e9rentiel Parcellaire Graphique, etc.).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["l3b", "l3b-oso", "oso", "raster", "satellite", "sentinel-2", "theia-oso-raster-l3b"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l3b", "title": "THEIA OSO RASTER"}, "THEIA_OSO_VECTOR_L3B": {"constellation": "sentinel-2", "description": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The Vector format  is a product with a minimum collection unit of 0.1 ha derived from the 20 m raster with a procedure of regularization and a simplification of the polygons obtained. In order to preserve as much information as possible from the raster product, each polygon is characterized by a set of attributes: - The majority class, with the same nomenclature of the raster product. - The average number of cloud-free images used for classification and the standard deviation. These attributes are named validmean and validstd. - The confidence of the majority class obtained from the Random Forest classifier (value between 0 and 100). - The percentage of the area covered by each class of the classification. This percentage is calculated on the 10m raster, even if the simplified polygons are derived from the 20m raster. - The area of the polygon. - The product is clipped according to the administrative boundaries of the departments and stored in a zip archive containing the 4 files that make up the \u201cESRI Shapefile\u201d format.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["l3b", "l3b-oso", "oso", "satellite", "sentinel-2", "theia-oso-vector-l3b", "vector"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l3b", "title": "THEIA OSO VECTOR"}, "THEIA_POSTEL_LANDCOVER_GLOBCOVER": {"constellation": "envisat", "description": "A land cover map associates to each pixel of the surface a labelling characterizing the surface (ex : deciduous forest, agriculture area, etc) following a predefined nomenclature. A commonly used nomenclature is the LCCS (Land Cover Classification System) used by FAO and UNEP, and comprising 22 classes. POSTEL produces and makes available the global land cover map at 300 m resolution of the GLOBCOVER / ESA project, which can be viewed with a zooming capacity. Regional maps are also available with classes adapted to each bioclimatic area.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-12-01T00:00:00Z", "2006-07-01T00:00:00Z"]]}}, "keywords": ["classification", "envisat", "land-cover", "land-surface", "postel", "satellite", "theia-postel-landcover-globcover"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL global land cover"}, "THEIA_POSTEL_RADIATION_BRDF": {"constellation": "parasol,adeos-1,adeos-2", "description": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Bidirectional Reflectance Distribution Function (FDRB) describes how terrestrial surfaces reflect the sun radiation. Its potential has been demonstrated for several applications in land surface studies (see Bicheron and Leroy, 2000). The space-borne POLDER-1/ADEOS-1 instrument (November 1996 \u2013 June 1997) has provided the first opportunity to sample the BRDF of every point on Earth for viewing angles up to 60\u00b0-70\u00b0, and for the full azimuth range, at a spatial resolution of about 6km, when the atmospheric conditions are favorable (Hautecoeur et Leroy, 1998). From April to October 2003, the land surface BRDF was sampled by the POLDER-2/ADEOS-2 sensor. From March 2005, the POLDER-3 sensor onboard the PARASOL microsatellite measures the bi-driectional reflectance of the continental ecosystems. These successive observations allowed building : 1- a BRDF database from the 8 months of POLDER-1 mesurements : The POLDER-1 BRDF data base compiles 24,857 BRDFs acquired by ADEOS-1/POLDER-1 during 8 months, from November, 1996 to June, 1997, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-1 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms developed for the processing line of the ADEOS-2/POLDER-2 data. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000. 2- a BRDF database from the 7 months of POLDER-2 measurements : The POLDER-2 BRDF data base compiles 24,090 BRDFs acquired by ADEOS-2/POLDER-2 from April ro October 2003, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-2 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms described on the CNES scientific Web site. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000 land cover map. 3- 4 BRDF databases from one year of POLDER-3 measurements :The LSCE, one of the POSTEL Expertise Centre, defined a new method to select the BRDFs from POLDER-3/PARASOL data acquired from November 2005 to October 2006 in order to build 4 BRDF databases. 2 MONTHLY databases gathering the best quality BRDFs for each month, independently : one based upon the IGBP land cover map, the second based upon the GLC2000 land cover map. 2 YEARLY databases designed to monitor the annual cycle of surface reflectance and its directional signature. The selection of high quality pixels is based on the full year. The first database is based upon the IGBP land cover map, the second one is based upon the GLC2000 land cover map.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1996-11-01T00:00:00Z", "2006-10-01T00:00:00Z"]]}}, "instruments": ["POLDER-1", "POLDER-2", "POLDER-3"], "keywords": ["adeos-1", "adeos-2", "bidirectional-reflectance-distribution-function", "brdf", "land", "land-surface", "parasol", "polder", "polder-1", "polder-2", "polder-3", "postel", "radiation", "reflectance", "satellite", "theia-postel-radiation-brdf"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Radiation BRDF"}, "THEIA_POSTEL_RADIATION_DLR": {"constellation": "meteosat", "description": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Downwelling Longwave Radiation (W.m-2) (DLR) is defined as the thermal irradiance reaching the surface in the thermal infrared spectrum (4 \u2013 100 \u00b5m). It is determined by the radiation that originates from a shallow layer close to the surface, about one third being emitted by the lowest 10 meters and 80% by the 500-meter layer. The DLR is derived from several sensors (Meteosat, MSG) using various approaches, in the framework of the Geoland project.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-01-01T00:00:00Z", "2000-12-31T00:00:00Z"]]}}, "instruments": ["MVIRI"], "keywords": ["geoland", "irradiance", "land", "land-surface", "long-wave-radiation-descending-flux", "longwave", "meteosat", "mviri", "postel", "radiation", "satellite", "theia-postel-radiation-dlr", "thermal"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Downwelling Longwave Radiation"}, "THEIA_POSTEL_RADIATION_SURFACEALBEDO": {"constellation": "adeos-1,adeos-2,parasol,spot-4", "description": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The albedo is the fraction of the incoming solar radiation reflected by the land surface, integrated over the whole viewing directions. The albedo can be directional (calculated for a given sun zenith angle, also called \u201cblack-sky albedo\u201d) or hemispheric (integrated over all illumination directions, also called \u201cwhite-sky albedo\u201d), spectral (for each narrow band of the sensor) or broadband (integrated over the solar spectrum). The surface albedos are derived from many sensors (Vegetation, Polder, Meteosat) in the frame of different projects, namely Geoland and Amma.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1996-01-01T00:00:00Z", "2012-05-01T00:00:00Z"]]}}, "instruments": ["VGT", "POLDER-1", "POLDER-2", "POLDER-3"], "keywords": ["adeos-1", "adeos-2", "albedo", "amma", "bio", "geoland", "land-surface-albedo", "parasol", "polder", "polder-1", "polder-2", "polder-3", "postel", "radiation", "satellite", "spot-4", "surface", "theia-postel-radiation-surfacealbedo", "vgt"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Radiation Surface Albedo"}, "THEIA_POSTEL_RADIATION_SURFACEREFLECTANCE": {"constellation": "adeos-1,adeos-2,parasol", "description": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1996-11-01T00:00:00Z", "2011-12-31T00:00:00Z"]]}}, "instruments": ["POLDER-1", "POLDER-2", "POLDER-3"], "keywords": ["adeos-1", "adeos-2", "boa-reflectance", "land", "parasol", "polder", "polder-1", "polder-2", "polder-3", "postel", "radiation", "reflectance", "satellite", "surface", "surface-reflectance", "theia-postel-radiation-surfacereflectance"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Radiation Surface Reflectance"}, "THEIA_POSTEL_VEGETATION_FAPAR": {"constellation": "adeos-1,adeos-2,aqua,noaa,metop-b", "description": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FAPAR is defined as the fraction of photosynthetically active radiation absorbed by vegetation for photosynthesis activity. The FAPAR can be instantaneous or daily. FAPAR is assessed using various approaches and algorithms applied to many sensors (Vegetation, Polder, Modis, AVHRR) in the frame of Polder and Amma projects.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "instruments": ["POLDER1", "POLDER2", "MODIS", "AVHRR"], "keywords": ["adeos-1", "adeos-2", "amma", "aqua", "avhrr", "bio", "biosphere", "fapar", "fraction-of-absorbed-photosynthetically-active-radiation", "geoland", "metop-b", "modis", "noaa", "polder", "polder1", "polder2", "postel", "satellite", "theia-postel-vegetation-fapar", "vegetation"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Vegetation FAPAR"}, "THEIA_POSTEL_VEGETATION_FCOVER": {"constellation": "noaa,metop-b,spot-4", "description": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FCover is the fraction of ground surface covered by vegetation. Fcover is assessed using various approaches and algorithms applied to Vegetation, and Polder, data in the frame of the Cyclopes project.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-08-25T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "instruments": ["AVHRR", "VGT"], "keywords": ["avhrr", "bio", "biosphere", "cyclopes", "fcover", "geoland", "metop-b", "noaa", "postel", "satellite", "spot-4", "theia-postel-vegetation-fcover", "vegetation", "vegetation-cover-fraction", "vgt"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Vegetation FCover"}, "THEIA_POSTEL_VEGETATION_LAI": {"constellation": "adeos-1,adeos-2,aqua,noaa,metop-b", "description": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation LAI is defined as half the total foliage area per unit of ground surface (Chen and Black, 1992). It is assessed using various approaches and algorithms applied to many sensors data (Vegetation, Polder, Modis, AVHRR) in the frame of the Amma project.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}, "instruments": ["POLDER1", "POLDER2", "MODIS", "AVHRR"], "keywords": ["adeos-1", "adeos-2", "amma", "aqua", "avhrr", "bio", "biosphere", "geoland", "lai", "leaf-area-index", "metop-b", "modis", "noaa", "polder1", "polder2", "postel", "satellite", "theia-postel-vegetation-lai", "vegetation"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Vegetation LAI"}, "THEIA_POSTEL_VEGETATION_NDVI": {"constellation": "parasol,msg", "description": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. Postel Vegetation NDVI (Normalized Difference Vegetation Index) is calculated as the normalized ratio of the difference between the reflectances measured in the red and near-infrared sensor bands. The NDVI is the most frequently used vegetation index to assess the quantity of vegetation on the surface, and to monitor the temporal ecosystems variations. Postel provides NDVI, derived from observations of various sensors (Polder, AVHRR, Seviri) in the frame of different projects : Polder \u2013 Parasol and Amma.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", "2012-05-01T00:00:00Z"]]}}, "instruments": ["POLDER-3", "SEVERI"], "keywords": ["amma", "bio", "biosphere", "msg", "ndvi", "normalized-difference-vegetation-index", "parasol", "polder-3", "postel", "satellite", "severi", "theia-postel-vegetation-ndvi", "vegetation"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Vegetation NDVI"}, "THEIA_POSTEL_VEGETATION_SURFACEREFLECTANCE": {"constellation": "spot-4", "description": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-01-01T00:00:00Z", "2006-03-31T00:00:00Z"]]}}, "instruments": ["vgt"], "keywords": ["boa-reflectance", "cyclopes", "geoland", "globcover", "land", "postel", "reflectance", "satellite", "spot-4", "surface", "surface-reflectance", "theia-postel-vegetation-surfacereflectance", "vegetation", "vgt"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Vegetation Surface Reflectance"}, "THEIA_POSTEL_WATER_PRECIP": {"constellation": "noaa,meteosat", "description": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, IMP (University of Vienna) and EARS assess the precipitation amount from geo-stationnary sensors images using various approaches for applications of the Observatory Natural Carbon (ONC) and of the Observatory Food Security and Crop Monitoring (OFM). Postel Water PRECIP is global scale daily precipitation product based on existing multi-satellite products and bias-corrected precipitation gauge analyses.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-01-01T00:00:00Z", "2005-12-31T00:00:00Z"]]}}, "instruments": ["TOVS", "MVIRI"], "keywords": ["athmosphere", "geoland", "meteosat", "mviri", "noaa", "postel", "precipitation", "satellite", "theia-postel-water-precip", "tovs", "water"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Water Precipitation"}, "THEIA_POSTEL_WATER_SOILMOISTURE": {"constellation": "aqua", "description": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, University of Bonn assess soil moisture parameters from passive micro-wave sensors measurements. Postel Water Soil Moisture is water column in mm, in the upper meter of soil.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", "2004-12-31T00:00:00Z"]]}}, "instruments": ["MASR-E"], "keywords": ["aqua", "geoland", "humidity", "masr-e", "moisture", "postel", "satellite", "soil", "theia-postel-water-soilmoisture", "water", "water-surface"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Water Soil Moisture"}, "THEIA_POSTEL_WATER_SURFWET": {"constellation": "ers", "description": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel SurfWet (Surface Wetness) is Soil moisture content in the 1-5 centimetre layer of the soil in relative units ranging between 0 wetness and total water capacity.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1992-01-01T00:00:00Z", "2000-12-31T00:00:00Z"]]}}, "instruments": ["AMI"], "keywords": ["ami", "ers", "geoland", "postel", "satellite", "soil", "soil-moisture", "surface", "surface-wetness", "surfwet", "theia-postel-water-surfwet", "water", "water-surface"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Water Surface wet"}, "THEIA_POSTEL_WATER_SWI": {"constellation": "ers", "description": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel Water SWI (Soil Water Index) is soil moisture content in the 1st meter of the soil in relative units ranging between wilting level and field capacity.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1992-01-01T00:00:00Z", "2000-12-31T00:00:00Z"]]}}, "instruments": ["AMI"], "keywords": ["ami", "ers", "geoland", "humidity", "moisture", "postel", "satellite", "soil", "soil-water-index", "swi", "theia-postel-water-swi", "water", "water-surface"], "license": "Apache-2.0", "platform": "satellite", "title": "POSTEL Water Soil Water Index"}, "THEIA_REFLECTANCE_LANDSAT5_L2A": {"constellation": "landsat-5", "description": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are  a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2009-01-09T00:00:00Z", "2011-11-14T00:00:00Z"]]}}, "instruments": ["MSS"], "keywords": ["boa-reflectance", "l2a", "l5", "landsat", "landsat-5", "landsat5", "mss", "n2a", "reflectance", "satellite", "satellite-image", "surface", "theia-reflectance-landsat5-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA LANDSAT5 L2A"}, "THEIA_REFLECTANCE_LANDSAT7_L2A": {"constellation": "landsat-7", "description": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are  a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2009-01-03T10:42:49Z", "2011-12-29T10:36:24Z"]]}}, "instruments": ["ETM+"], "keywords": ["boa-reflectance", "etm+", "l2a", "l7", "landsat", "landsat-7", "landsat7", "n2a", "reflectance", "satellite", "satellite-image", "surface", "theia-reflectance-landsat7-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA LANDSAT7 L2A"}, "THEIA_REFLECTANCE_LANDSAT8_L2A": {"constellation": "landsat-8", "description": "The level 2A products correct the data for atmospheric effects along with a mask of clouds and their shadows, as well as a mask of water and snow. Landsat products are provided by Theia in surface reflectance (level 2A) with cloud masks, the processing being performed with the MAJA algorithm. They are orthorectified and cut on the same tiles as Sentinel-2 products.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-04-11T10:13:56Z", "2021-08-31T22:51:30Z"]]}}, "instruments": ["OLI"], "keywords": ["boa-reflectance", "l2a", "l8", "landsat", "landsat-8", "landsat8", "n2a", "oli", "reflectance", "satellite", "satellite-image", "surface", "theia-reflectance-landsat8-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA LANDSAT8 L2A"}, "THEIA_REFLECTANCE_SENTINEL2_L2A": {"constellation": "sentinel-2", "description": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA (before called MACCS) for THEIA land data center.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-07-04T10:10:35.881Z", "2026-05-08T20:10:05.137Z"]]}}, "instruments": ["MSI"], "keywords": ["boa-reflectance", "l2a", "msi", "reflectance", "s2", "satellite", "satellite-image", "sentinel", "sentinel-2", "sentinel2", "surface", "theia-reflectance-sentinel2-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA SENTINEL2 L2A"}, "THEIA_REFLECTANCE_SENTINEL2_L3A": {"constellation": "sentinel-2", "description": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-07-15T00:00:00Z", "2026-03-15T00:00:00Z"]]}}, "instruments": ["MSI"], "keywords": ["boa-reflectance", "l3a", "msi", "reflectance", "s2", "satellite", "satellite-image", "sentinel", "sentinel-2", "sentinel2", "surface", "theia-reflectance-sentinel2-l3a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l3a", "title": "THEIA SENTINEL2 L3A"}, "THEIA_REFLECTANCE_SPOT4_TAKE5_L2A": {"constellation": "spot-4", "description": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-01-31T07:07:32Z", "2013-06-19T17:17:41Z"]]}}, "instruments": ["HRV", "HRVIR"], "keywords": ["boa-reflectance", "hrv", "hrvir", "image", "l2a", "satellite", "spot", "spot-4", "spot4", "surface-reflectance", "take5", "theia-reflectance-spot4-take5-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "TAKE5 SPOT4 LEVEL2A"}, "THEIA_REFLECTANCE_SPOT5_TAKE5_L2A": {"constellation": "spot-5", "description": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-04-08T00:31:16Z", "2015-09-15T12:06:24Z"]]}}, "instruments": ["HRG1", "HRG2"], "keywords": ["boa-reflectance", "hrg1", "hrg2", "image", "l2a", "reflectance", "satellite", "spot", "spot-5", "spot5", "take5", "theia-reflectance-spot5-take5-l2a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "TAKE5 SPOT5 LEVEL2A"}, "THEIA_REFLECTANCE_VENUS_VM1_L2A": {"constellation": "venus", "description": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-11-01T10:06:54Z", "2020-10-31T05:53:43Z"]]}}, "instruments": ["VSSC"], "keywords": ["boa-reflectance", "l2a", "reflectance", "satellite", "satellite-image", "surface", "theia-reflectance-venus-vm1-l2a", "venus", "vm1", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA VENUS VM1 L2A"}, "THEIA_REFLECTANCE_VENUS_VM1_L3A": {"constellation": "venus", "description": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-12-01T00:00:00Z", "2020-10-16T00:00:00Z"]]}}, "instruments": ["VSSC"], "keywords": ["boa-reflectance", "l3a", "reflectance", "satellite", "synthesis", "theia-reflectance-venus-vm1-l3a", "venus", "vm1", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l3a", "title": "THEIA VENUS VM1 L3A"}, "THEIA_REFLECTANCE_VENUS_VM5_L2A": {"constellation": "venus", "description": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2022-03-09T11:42:13Z", "2024-07-18T05:58:23Z"]]}}, "instruments": ["VSSC"], "keywords": ["boa-reflectance", "l2a", "reflectance", "satellite", "satellite-image", "surface", "theia-reflectance-venus-vm5-l2a", "venus", "vm5", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2a", "title": "THEIA VENUS VM5 L2A"}, "THEIA_REFLECTANCE_VENUS_VM5_L3A": {"constellation": "venus", "description": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-03-01T00:00:00Z", "2023-07-01T00:00:00Z"]]}}, "instruments": ["VSSC"], "keywords": ["boa-reflectance", "l3a", "reflectance", "satellite", "synthesis", "theia-reflectance-venus-vm5-l3a", "venus", "vm5", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l3a", "title": "THEIA VENUS VM5 L3A"}, "THEIA_S1TILING_SENTINEL1_L1": {"constellation": "sentinel-1", "description": "S1TILING L1 products are based on Sentinel-1 Level-1 Ground Range Detected (GRD) products which are ortho-rectified on Sentinel-2 grid to promote joint use of both missions.\n\nThese products are supplied in 110x110 km\u00b2 tiles, spaced every 100 km. The tiles therefore have an intersection zone of 10km on each side with neighboring tiles. This breakdown into tiles is the one used by Sentinel-2 level 2 products. Each product is an ortho image rectified to 10m x 10m pixels on the tile, calibrated in sigma0, with thermal noise subtracted.\n\nFor a given tile, there is one product per relative orbit, polarization and date. Ortho rectification is calculated using Copernicus DEM.\n\nAuxiliary data will be supplied to obtain gamma0-calibrated products in post-processing.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2026-01-01T03:33:03Z", "2026-05-07T23:49:53Z"]]}}, "instruments": ["SAR"], "keywords": ["ard", "l1", "level1", "radar", "s1", "sar", "satellite", "satellite-image", "sentinel", "sentinel-1", "theia-s1tiling-sentinel1-l1"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "level1", "title": "THEIA S1TILING SENTINEL L1"}, "THEIA_SPIRIT_SPOT5_L1A": {"constellation": "spot-5", "description": "SPOT 5 stereoscopic survey of polar ice. The objectives of the SPIRIT project were to build a large archive of Spot 5 HRS images of polar ice and, for certain regions, to produce digital terrain models (DEMs) and high-resolution images for free distribution to the community. . scientist. The target areas were the coastal regions of Greenland and Antarctica as well as all other glacial masses (Alaska, Iceland, Patagonia, etc.) surrounding the Arctic Ocean and Antarctica. The SPIRIT project made it possible to generate an archive of DEMs at 40m planimetric resolution from the HRS instrument.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-08-06T12:54:00Z", "2014-10-04T13:36:29Z"]]}}, "instruments": ["HRG1", "HRG2"], "keywords": ["1a", "dem", "glacier", "hrg1", "hrg2", "ice", "l1a", "satellite", "spirit", "spot", "spot-5", "spot5", "theia-spirit-spot5-l1a"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1a", "title": "THEIA Spirit SPOT5 L1A"}, "THEIA_SPOT4_TAKE5_L1C": {"constellation": "spot-4", "description": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L1C products are data orthorectified reflectance at the top of the atmosphere.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-01-31T01:57:43Z", "2013-06-19T17:17:41Z"]]}}, "instruments": ["HRV", "HRVIR"], "keywords": ["hrv", "hrvir", "image", "l1c", "reflectance", "satellite", "spot", "spot-4", "spot4", "take5", "theia-spot4-take5-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "TAKE5 SPOT4 LEVEL1C"}, "THEIA_SPOT5_TAKE5_L1C": {"constellation": "spot-5", "description": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L1C products are data orthorectified reflectance at the top of the atmosphere.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-04-08T00:31:16Z", "2015-09-15T15:21:32Z"]]}}, "instruments": ["HRG1", "HRG2"], "keywords": ["hrg1", "hrg2", "image", "l1c", "reflectance", "satellite", "spot", "spot-5", "spot5", "take5", "theia-spot5-take5-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "TAKE5 SPOT5 LEVEL1C"}, "THEIA_SWH-PHASE1_SPOT1_L1C": {"constellation": "spot-1", "description": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1986-03-18T20:21:50Z", "2003-09-18T10:35:00Z"]]}}, "instruments": ["HRV1", "HRV2"], "keywords": ["hrv1", "hrv2", "l1c", "reflectance", "satellite", "satellite-image", "spot", "spot-1", "spot1", "theia-swh-phase1-spot1-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA SPOTWORLDHERITAGE SPOT1 L1C"}, "THEIA_SWH-PHASE1_SPOT2_L1C": {"constellation": "spot-2", "description": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-02-23T08:22:06Z", "2009-06-29T11:28:12Z"]]}}, "instruments": ["HRV1", "HRV2"], "keywords": ["hrv1", "hrv2", "l1c", "reflectance", "satellite", "satellite-image", "spot", "spot-2", "spot2", "theia-swh-phase1-spot2-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA SPOTWORLDHERITAGE SPOT2 L1C"}, "THEIA_SWH-PHASE1_SPOT3_L1C": {"constellation": "spot-3", "description": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-10-02T13:56:34Z", "1996-11-13T10:48:00Z"]]}}, "instruments": ["HRV1", "HRV2"], "keywords": ["hrv1", "hrv2", "l1c", "reflectance", "satellite", "satellite-image", "spot", "spot-3", "spot3", "theia-swh-phase1-spot3-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA SPOTWORLDHERITAGE SPOT3 L1C"}, "THEIA_SWH-PHASE1_SPOT4_L1C": {"constellation": "spot-4", "description": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015  : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1998-03-27T11:19:29Z", "2013-06-19T07:09:44Z"]]}}, "instruments": ["HRVIR1", "HRVIR2"], "keywords": ["hrvir1", "hrvir2", "l1c", "reflectance", "satellite", "satellite-image", "spot", "spot-4", "spot4", "theia-swh-phase1-spot4-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA SPOTWORLDHERITAGE SPOT4 L1C"}, "THEIA_SWH-PHASE1_SPOT5_L1C": {"constellation": "spot-5", "description": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Nowadays, two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-06-21T09:52:57Z", "2015-09-06T05:04:25Z"]]}}, "instruments": ["HRG1", "HRG2"], "keywords": ["hrg1", "hrg2", "l1c", "reflectance", "satellite", "satellite-image", "spot", "spot-5", "spot5", "theia-swh-phase1-spot5-l1c", "toa-reflectance"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA SPOTWORLDHERITAGE SPOT5 L1C"}, "THEIA_VENUS_VM1_L1C": {"constellation": "venus", "description": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date  and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13,  bad pixel mask - channel 14, and cloudy pixels - channel 15).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-11-01T10:06:54Z", "2020-10-31T05:53:43Z"]]}}, "instruments": ["VSSC"], "keywords": ["l1c", "reflectance", "satellite", "satellite-image", "theia-venus-vm1-l1c", "toa-reflectance", "venus", "vm1", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA VENUS VM1 L1C"}, "THEIA_VENUS_VM5_L1C": {"constellation": "venus", "description": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date  and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13,  bad pixel mask - channel 14, and cloudy pixels - channel 15).", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2022-03-09T11:42:13Z", "2024-07-18T05:58:23Z"]]}}, "instruments": ["VSSC"], "keywords": ["l1c", "reflectance", "satellite", "satellite-image", "theia-venus-vm5-l1c", "toa-reflectance", "venus", "vm5", "vssc"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l1c", "title": "THEIA VENUS VM5 L1C"}, "THEIA_WATERQUAL_SENTINEL2_L2B": {"constellation": "sentinel-2", "description": "The processing chain outputs rasters of the concentration of SPM estimated in the Bands B4 and B8. The concentration is given in mg/L. So a pixel value of 21.34 corresponds to 21.34 mg/L estimated at this point. The value -10000 signifies that there is no- or invalid data available. The concentration is always calculated only over the pixels classified as water. An RGB raster for the given ROI is also included. The values correspond to reflectance TOC (Top-Of-Canopy), which is unitless. Several masks generated by the Temporal-Synthesis are also included.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-01-10T10:30:07Z", "2018-12-28T03:52:31Z"]]}}, "instruments": [], "keywords": ["", "l2b", "l2b-water", "s2", "satellite", "sentinel", "sentinel-2", "sentinel2", "sentinel2a", "sentinel2b", "theia-waterqual-sentinel2-l2b"], "license": "Apache-2.0", "platform": "satellite", "processing:level": "l2b", "title": "THEIA WaterQual SENTINEL2 L2B"}}, "providers_config": {"FLATSIM_AUXILIARYDATA": {"_collection": "FLATSIM_AUXILIARYDATA"}, "FLATSIM_INTERFEROGRAM": {"_collection": "FLATSIM_INTERFEROGRAM"}, "FLATSIM_TIMESERIE": {"_collection": "FLATSIM_TIMESERIE"}, "L1B_OMP_SAGSA_GGM_AOD_3HOURS": {"_collection": "L1B_OMP_SAGSA_GGM_AOD_3HOURS"}, "L2A_CNES_SAGSA_GGM_CHO_1MONTH": {"_collection": "L2A_CNES_SAGSA_GGM_CHO_1MONTH"}, "L2A_CNES_SAGSA_GGM_SVD_10DAYS": {"_collection": "L2A_CNES_SAGSA_GGM_SVD_10DAYS"}, "L2A_CNES_SAGSA_GGM_SVD_1MONTH": {"_collection": "L2A_CNES_SAGSA_GGM_SVD_1MONTH"}, "L2A_OMP_SAGSA_GGM_GAA_10DAYS": {"_collection": "L2A_OMP_SAGSA_GGM_GAA_10DAYS"}, "L2A_OMP_SAGSA_GGM_GAA_1MONTH": {"_collection": "L2A_OMP_SAGSA_GGM_GAA_1MONTH"}, "L2A_OMP_SAGSA_GGM_GAB_10DAYS": {"_collection": "L2A_OMP_SAGSA_GGM_GAB_10DAYS"}, "L2A_OMP_SAGSA_GGM_GAB_1MONTH": {"_collection": "L2A_OMP_SAGSA_GGM_GAB_1MONTH"}, "L2B_CNES_SAGSA_GFQ_EWH_SVD_10DAYS": {"_collection": "L2B_CNES_SAGSA_GFQ_EWH_SVD_10DAYS"}, "L2B_CNES_SAGSA_GFQ_EWH_SVD_1MONTH": {"_collection": "L2B_CNES_SAGSA_GFQ_EWH_SVD_1MONTH"}, "L2B_CNES_SAGSA_GFQ_GGA_SVD_10DAYS": {"_collection": "L2B_CNES_SAGSA_GFQ_GGA_SVD_10DAYS"}, "L2B_CNES_SAGSA_GFQ_GGA_SVD_1MONTH": {"_collection": "L2B_CNES_SAGSA_GFQ_GGA_SVD_1MONTH"}, "L2B_CNES_SAGSA_GFQ_GGH_SVD_10DAYS": {"_collection": "L2B_CNES_SAGSA_GFQ_GGH_SVD_10DAYS"}, "L2B_CNES_SAGSA_GFQ_GGH_SVD_1MONTH": {"_collection": "L2B_CNES_SAGSA_GFQ_GGH_SVD_1MONTH"}, "L3_CNES_SAGSA_ENSEMBLE_1MONTH_expert": {"_collection": "L3_CNES_SAGSA_ENSEMBLE_1MONTH_expert"}, "L3_CNES_SAGSA_ENSEMBLE_1MONTH_public": {"_collection": "L3_CNES_SAGSA_ENSEMBLE_1MONTH_public"}, "PEPS_S1_L1": {"_collection": "PEPS_S1_L1"}, "PEPS_S1_L2": {"_collection": "PEPS_S1_L2"}, "PEPS_S2_L1C": {"_collection": "PEPS_S2_L1C"}, "PEPS_S3_L1": {"_collection": "PEPS_S3_L1"}, "SWH_SPOT123_L1": {"_collection": "SWH_SPOT123_L1"}, "SWH_SPOT4_L1": {"_collection": "SWH_SPOT4_L1"}, "SWH_SPOT5_L1": {"_collection": "SWH_SPOT5_L1"}, "THEIA_BIOPHY_SENTINEL2_L2B": {"_collection": "THEIA_BIOPHY_SENTINEL2_L2B"}, "THEIA_OSO_RASTER_L3B": {"_collection": "THEIA_OSO_RASTER_L3B"}, "THEIA_OSO_VECTOR_L3B": {"_collection": "THEIA_OSO_VECTOR_L3B"}, "THEIA_POSTEL_LANDCOVER_GLOBCOVER": {"_collection": "THEIA_POSTEL_LANDCOVER_GLOBCOVER"}, "THEIA_POSTEL_RADIATION_BRDF": {"_collection": "THEIA_POSTEL_RADIATION_BRDF"}, "THEIA_POSTEL_RADIATION_DLR": {"_collection": "THEIA_POSTEL_RADIATION_DLR"}, "THEIA_POSTEL_RADIATION_SURFACEALBEDO": {"_collection": "THEIA_POSTEL_RADIATION_SURFACEALBEDO"}, "THEIA_POSTEL_RADIATION_SURFACEREFLECTANCE": {"_collection": "THEIA_POSTEL_RADIATION_SURFACEREFLECTANCE"}, "THEIA_POSTEL_VEGETATION_FAPAR": {"_collection": "THEIA_POSTEL_VEGETATION_FAPAR"}, "THEIA_POSTEL_VEGETATION_FCOVER": {"_collection": "THEIA_POSTEL_VEGETATION_FCOVER"}, "THEIA_POSTEL_VEGETATION_LAI": {"_collection": "THEIA_POSTEL_VEGETATION_LAI"}, "THEIA_POSTEL_VEGETATION_NDVI": {"_collection": "THEIA_POSTEL_VEGETATION_NDVI"}, "THEIA_POSTEL_VEGETATION_SURFACEREFLECTANCE": {"_collection": "THEIA_POSTEL_VEGETATION_SURFACEREFLECTANCE"}, "THEIA_POSTEL_WATER_PRECIP": {"_collection": "THEIA_POSTEL_WATER_PRECIP"}, "THEIA_POSTEL_WATER_SOILMOISTURE": {"_collection": "THEIA_POSTEL_WATER_SOILMOISTURE"}, "THEIA_POSTEL_WATER_SURFWET": {"_collection": "THEIA_POSTEL_WATER_SURFWET"}, "THEIA_POSTEL_WATER_SWI": {"_collection": "THEIA_POSTEL_WATER_SWI"}, "THEIA_REFLECTANCE_LANDSAT5_L2A": {"_collection": "THEIA_REFLECTANCE_LANDSAT5_L2A"}, "THEIA_REFLECTANCE_LANDSAT7_L2A": {"_collection": "THEIA_REFLECTANCE_LANDSAT7_L2A"}, "THEIA_REFLECTANCE_LANDSAT8_L2A": {"_collection": "THEIA_REFLECTANCE_LANDSAT8_L2A"}, "THEIA_REFLECTANCE_SENTINEL2_L2A": {"_collection": "THEIA_REFLECTANCE_SENTINEL2_L2A"}, "THEIA_REFLECTANCE_SENTINEL2_L3A": {"_collection": "THEIA_REFLECTANCE_SENTINEL2_L3A"}, "THEIA_REFLECTANCE_SPOT4_TAKE5_L2A": {"_collection": "THEIA_REFLECTANCE_SPOT4_TAKE5_L2A"}, "THEIA_REFLECTANCE_SPOT5_TAKE5_L2A": {"_collection": "THEIA_REFLECTANCE_SPOT5_TAKE5_L2A"}, "THEIA_REFLECTANCE_VENUS_VM1_L2A": {"_collection": "THEIA_REFLECTANCE_VENUS_VM1_L2A"}, "THEIA_REFLECTANCE_VENUS_VM1_L3A": {"_collection": "THEIA_REFLECTANCE_VENUS_VM1_L3A"}, "THEIA_REFLECTANCE_VENUS_VM5_L2A": {"_collection": "THEIA_REFLECTANCE_VENUS_VM5_L2A"}, "THEIA_REFLECTANCE_VENUS_VM5_L3A": {"_collection": "THEIA_REFLECTANCE_VENUS_VM5_L3A"}, "THEIA_S1TILING_SENTINEL1_L1": {"_collection": "THEIA_S1TILING_SENTINEL1_L1"}, "THEIA_SPIRIT_SPOT5_L1A": {"_collection": "THEIA_SPIRIT_SPOT5_L1A"}, "THEIA_SPOT4_TAKE5_L1C": {"_collection": "THEIA_SPOT4_TAKE5_L1C"}, "THEIA_SPOT5_TAKE5_L1C": {"_collection": "THEIA_SPOT5_TAKE5_L1C"}, "THEIA_SWH-PHASE1_SPOT1_L1C": {"_collection": "THEIA_SWH-PHASE1_SPOT1_L1C"}, "THEIA_SWH-PHASE1_SPOT2_L1C": {"_collection": "THEIA_SWH-PHASE1_SPOT2_L1C"}, "THEIA_SWH-PHASE1_SPOT3_L1C": {"_collection": "THEIA_SWH-PHASE1_SPOT3_L1C"}, "THEIA_SWH-PHASE1_SPOT4_L1C": {"_collection": "THEIA_SWH-PHASE1_SPOT4_L1C"}, "THEIA_SWH-PHASE1_SPOT5_L1C": {"_collection": "THEIA_SWH-PHASE1_SPOT5_L1C"}, "THEIA_VENUS_VM1_L1C": {"_collection": "THEIA_VENUS_VM1_L1C"}, "THEIA_VENUS_VM5_L1C": {"_collection": "THEIA_VENUS_VM5_L1C"}, "THEIA_WATERQUAL_SENTINEL2_L2B": {"_collection": "THEIA_WATERQUAL_SENTINEL2_L2B"}}}, "planetary_computer": {"collections_config": {"3dep-lidar-classification": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-classification", "classification", "cog", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Classification"}, "3dep-lidar-copc": {"description": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-copc", "cog", "point-cloud", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Point Cloud"}, "3dep-lidar-dsm": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-dsm", "cog", "dsm", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Digital Surface Model"}, "3dep-lidar-dtm": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-dtm", "cog", "dtm", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model"}, "3dep-lidar-dtm-native": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-dtm-native", "cog", "dtm", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model (Native)"}, "3dep-lidar-hag": {"description": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-hag", "cog", "elevation", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Height above Ground"}, "3dep-lidar-intensity": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-intensity", "cog", "intensity", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Intensity"}, "3dep-lidar-pointsourceid": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-pointsourceid", "cog", "pointsourceid", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Point Source"}, "3dep-lidar-returns": {"description": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "extent": {"spatial": {"bbox": [[-166.8546920006028, 17.655357747708283, -64.56116757979399, 71.39330810146807], [144.60180842809473, 13.21774453924126, 146.08202179248926, 18.18369664008955]]}, "temporal": {"interval": [["2012-01-01T00:00:00Z", "2022-01-01T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-lidar-returns", "cog", "numberofreturns", "usgs"], "license": "proprietary", "title": "USGS 3DEP Lidar Returns"}, "3dep-seamless": {"description": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions.  The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data.  \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "extent": {"spatial": {"bbox": [[-174.001666666983, -15.00166666667, 164.0016666666, 84.00166666666]]}, "temporal": {"interval": [["1925-01-01T00:00:00Z", "2020-05-06T00:00:00Z"]]}}, "keywords": ["3dep", "3dep-seamless", "dem", "elevation", "ned", "usgs"], "license": "PDDL-1.0", "title": "USGS 3DEP Seamless DEMs"}, "alos-dem": {"description": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2016-12-07T00:00:00Z", "2016-12-07T00:00:00Z"]]}}, "instruments": ["prism"], "keywords": ["alos", "alos-dem", "dem", "dsm", "elevation", "jaxa", "prism"], "license": "proprietary", "platform": "alos", "title": "ALOS World 3D-30m"}, "alos-fnf-mosaic": {"description": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "extent": {"spatial": {"bbox": [[-180.0, 85.0, 180.0, -56.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "instruments": ["PALSAR", "PALSAR-2"], "keywords": ["alos", "alos-2", "alos-fnf-mosaic", "forest", "global", "jaxa", "land-cover", "palsar", "palsar-2"], "license": "proprietary", "platform": "ALOS,ALOS-2", "title": "ALOS Forest/Non-Forest Annual Mosaic"}, "alos-palsar-mosaic": {"description": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "extent": {"spatial": {"bbox": [[-180.0, 85.0, 180.0, -56.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}, "instruments": ["PALSAR", "PALSAR-2"], "keywords": ["alos", "alos-2", "alos-palsar-mosaic", "global", "jaxa", "palsar", "palsar-2", "remote-sensing"], "license": "proprietary", "platform": "ALOS,ALOS-2", "title": "ALOS PALSAR Annual Mosaic"}, "aster-l1t": {"description": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution.  ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006.  L1T images have been terrain-corrected and rotated to a north-up UTM projection.  Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-03-04T12:00:00Z", "2006-12-31T12:00:00Z"]]}}, "instruments": ["aster"], "keywords": ["aster", "aster-l1t", "global", "nasa", "satellite", "terra", "usgs"], "license": "proprietary", "platform": "terra", "title": "ASTER L1T"}, "chesapeake-lc-13": {"description": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "extent": {"spatial": {"bbox": [[-81.14658496196135, 36.21291717905733, -73.27357561029186, 44.77821441524524]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}, "keywords": ["chesapeake-bay-watershed", "chesapeake-conservancy", "chesapeake-lc-13", "land-cover"], "license": "proprietary", "title": "Chesapeake Land Cover (13-class)"}, "chesapeake-lc-7": {"description": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "extent": {"spatial": {"bbox": [[-81.14658496196135, 36.21291717905733, -73.27357561029186, 44.77821441524524]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}, "keywords": ["chesapeake-bay-watershed", "chesapeake-conservancy", "chesapeake-lc-7", "land-cover"], "license": "proprietary", "title": "Chesapeake Land Cover (7-class)"}, "chesapeake-lu": {"description": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "extent": {"spatial": {"bbox": [[-81.14648244566828, 36.18730972451623, -73.11082086653798, 44.7781991560751]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}, "keywords": ["chesapeake-bay-watershed", "chesapeake-conservancy", "chesapeake-lu", "land-use"], "license": "proprietary", "title": "Chesapeake Land Use"}, "chloris-biomass": {"description": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "extent": {"spatial": {"bbox": [[-179.95, -60, 179.95, 90]]}, "temporal": {"interval": [["2003-07-31T00:00:00Z", "2019-07-31T00:00:00Z"]]}}, "keywords": ["biomass", "carbon", "chloris", "chloris-biomass", "modis"], "license": "CC-BY-NC-SA-4.0", "title": "Chloris Biomass"}, "cil-gdpcir-cc-by": {"description": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40               |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40               |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40               |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n  under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2100-12-31T00:00:00Z"]]}}, "keywords": ["cil-gdpcir-cc-by", "climate-impact-lab", "cmip6", "precipitation", "rhodium-group", "temperature"], "license": "CC-BY-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"}, "cil-gdpcir-cc-by-sa": {"description": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40]              |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40]              |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40]              |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40]              |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]              |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40]              |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40]              |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-SA-40]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2100-12-31T00:00:00Z"]]}}, "keywords": ["cil-gdpcir-cc-by-sa", "climate-impact-lab", "cmip6", "precipitation", "rhodium-group", "temperature"], "license": "CC-BY-SA-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)"}, "cil-gdpcir-cc0": {"description": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40               |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40               |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40               |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n  under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2100-12-31T00:00:00Z"]]}}, "keywords": ["cil-gdpcir-cc0", "climate-impact-lab", "cmip6", "precipitation", "rhodium-group", "temperature"], "license": "CC0-1.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"}, "conus404": {"description": "[CONUS404](https://www.usgs.gov/data/conus404-four-kilometer-long-term-regional-hydroclimate-reanalysis-over-conterminous-united) is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\n\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.", "extent": {"spatial": {"bbox": [[-137.873, 17.631, -58.463, 56.704]]}, "temporal": {"interval": [["1979-10-01T00:00:00Z", "2022-09-30T23:00:00Z"]]}}, "keywords": ["climate", "conus404", "hydroclimate", "hydrology", "inland-waters", "precipitation", "weather"], "license": "CC-BY-4.0", "title": "CONUS404"}, "cop-dem-glo-30": {"description": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-04-22T00:00:00Z", "2021-04-22T00:00:00Z"]]}}, "keywords": ["cop-dem-glo-30", "copernicus", "dem", "dsm", "elevation", "tandem-x"], "license": "proprietary", "platform": "tandem-x", "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"description": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-04-22T00:00:00Z", "2021-04-22T00:00:00Z"]]}}, "keywords": ["cop-dem-glo-90", "copernicus", "dem", "elevation", "tandem-x"], "license": "proprietary", "platform": "tandem-x", "title": "Copernicus DEM GLO-90"}, "daymet-annual-hi": {"description": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "extent": {"spatial": {"bbox": [[-160.3056, 17.9539, -154.772, 23.5186]]}, "temporal": {"interval": [["1980-07-01T12:00:00Z", "2020-07-01T12:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-annual-hi", "hawaii", "precipitation", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Annual Hawaii"}, "daymet-annual-na": {"description": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "extent": {"spatial": {"bbox": [[-178.1333, 14.0749, -53.0567, 82.9143]]}, "temporal": {"interval": [["1980-07-01T12:00:00Z", "2020-07-01T12:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-annual-na", "north-america", "precipitation", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Annual North America"}, "daymet-annual-pr": {"description": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "extent": {"spatial": {"bbox": [[-67.9927, 16.8444, -64.1196, 19.9382]]}, "temporal": {"interval": [["1980-07-01T12:00:00Z", "2020-07-01T12:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-annual-pr", "precipitation", "puerto-rico", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Annual Puerto Rico"}, "daymet-daily-hi": {"description": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "extent": {"spatial": {"bbox": [[-160.3056, 17.9539, -154.772, 23.5186]]}, "temporal": {"interval": [["1980-01-01T12:00:00Z", "2020-12-30T12:00:00Z"]]}}, "keywords": ["daymet", "daymet-daily-hi", "hawaii", "precipitation", "temperature", "vapor-pressure", "weather"], "license": "proprietary", "title": "Daymet Daily Hawaii"}, "daymet-daily-na": {"description": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "extent": {"spatial": {"bbox": [[-178.1333, 14.0749, -53.0567, 82.9143]]}, "temporal": {"interval": [["1980-01-01T12:00:00Z", "2020-12-30T12:00:00Z"]]}}, "keywords": ["daymet", "daymet-daily-na", "north-america", "precipitation", "temperature", "vapor-pressure", "weather"], "license": "proprietary", "title": "Daymet Daily North America"}, "daymet-daily-pr": {"description": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "extent": {"spatial": {"bbox": [[-67.9927, 16.8444, -64.1196, 19.9382]]}, "temporal": {"interval": [["1980-01-01T12:00:00Z", "2020-12-30T12:00:00Z"]]}}, "keywords": ["daymet", "daymet-daily-pr", "precipitation", "puerto-rico", "temperature", "vapor-pressure", "weather"], "license": "proprietary", "title": "Daymet Daily Puerto Rico"}, "daymet-monthly-hi": {"description": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "extent": {"spatial": {"bbox": [[-160.3056, 17.9539, -154.772, 23.5186]]}, "temporal": {"interval": [["1980-01-16T12:00:00Z", "2020-12-16T00:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-monthly-hi", "hawaii", "precipitation", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Monthly Hawaii"}, "daymet-monthly-na": {"description": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "extent": {"spatial": {"bbox": [[-178.1333, 14.0749, -53.0567, 82.9143]]}, "temporal": {"interval": [["1980-01-16T12:00:00Z", "2020-12-16T00:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-monthly-na", "north-america", "precipitation", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Monthly North America"}, "daymet-monthly-pr": {"description": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "extent": {"spatial": {"bbox": [[-67.9927, 16.8444, -64.1196, 19.9382]]}, "temporal": {"interval": [["1980-01-16T12:00:00Z", "2020-12-16T00:00:00Z"]]}}, "keywords": ["climate", "daymet", "daymet-monthly-pr", "precipitation", "puerto-rico", "temperature", "vapor-pressure"], "license": "proprietary", "title": "Daymet Monthly Puerto Rico"}, "deltares-floods": {"description": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "extent": {"spatial": {"bbox": [[-180.0, 90.0, 180.0, -90.0]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2018-12-31T00:00:00Z"], ["2050-01-01T00:00:00Z", "2050-12-31T00:00:00Z"]]}}, "keywords": ["deltares", "deltares-floods", "flood", "global", "sea-level-rise", "water"], "license": "CDLA-Permissive-1.0", "title": "Deltares Global Flood Maps"}, "deltares-water-availability": {"description": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n| Name   | Scale                    | Period    | Number of basins |\n|--------|--------------------------|-----------|------------------|\n| ERA5   | Global                   | 1967-2020 | 3236             |\n| CHIRPS | Global (+/- 50 latitude) | 1981-2020 | 2951             |\n| EOBS   | Europe/North Africa      | 1979-2020 | 682              |\n| NLDAS  | USA                      | 1979-2020 | 1090             |\n| BOM    | Australia                | 1979-2020 | 116              |\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "extent": {"spatial": {"bbox": [[-180.0, 90.0, 180.0, -90.0]]}, "temporal": {"interval": [["1970-01-01T00:00:00Z", "2020-12-31T00:00:00Z"]]}}, "keywords": ["deltares", "deltares-water-availability", "precipitation", "reservoir", "water", "water-availability"], "license": "CDLA-Permissive-1.0", "title": "Deltares Global Water Availability"}, "drcog-lulc": {"description": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "extent": {"spatial": {"bbox": [[-105.93962510864995, 39.10438697007073, -103.66801443832743, 40.320593119647256], [-105.54671456161505, 39.54013841830152, -104.46335720577567, 39.94430501943824]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["drcog-lulc", "land-cover", "land-use", "naip", "usda"], "license": "proprietary", "title": "Denver Regional Council of Governments Land Use Land Cover"}, "eclipse": {"description": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "extent": {"spatial": {"bbox": [[-87.94011408252348, 41.64454312178303, -87.5241371038952, 42.023038586147585]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", null]]}}, "keywords": ["air-pollution", "eclipse", "pm25"], "license": "proprietary", "title": "Urban Innovation Eclipse Sensor Data"}, "ecmwf-forecast": {"description": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows *forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* **stream** is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n  * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n  * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n  * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n  * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n  * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n  * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n  * `wave`: wave model\n* **type** is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n  * `fc`: forecast\n  * `ef`: ensemble forecast\n  * `pf`: ensemble probabilities\n  * `tf`: trajectory forecast for tropical cyclone tracks\n* **reference time** is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the *reference time* changes and `step` as the *forecast time* changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast *was made*, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "extent": {"spatial": {"bbox": [[-180, 90, 180, -90]]}, "temporal": {"interval": [[null, null]]}}, "keywords": ["ecmwf", "ecmwf-forecast", "forecast", "weather"], "license": "CC-BY-4.0", "title": "ECMWF Open Data (real-time)"}, "era5-pds": {"description": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An **analysis**, of the atmospheric conditions, is a blend of observations\n  with a previous forecast. An analysis can only provide\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n  parameters (parameters valid at a specific time, e.g temperature at 12:00),\n  but not accumulated parameters, mean rates or min/max parameters.\n* A **forecast** starts with an analysis at a specific time (the 'initialization\n  time'), and a model computes the atmospheric conditions for a number of\n  'forecast steps', at increasing 'validity times', into the future. A forecast\n  can provide\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n  parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", null]]}}, "keywords": ["ecmwf", "era5", "era5-pds", "precipitation", "reanalysis", "temperature", "weather"], "license": "proprietary", "title": "ERA5 - PDS"}, "esa-cci-lc": {"description": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1992-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["cci", "esa", "esa-cci-lc", "global", "land-cover"], "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)"}, "esa-cci-lc-netcdf": {"description": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1992-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["cci", "esa", "esa-cci-lc-netcdf", "global", "land-cover"], "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)"}, "esa-worldcover": {"description": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n  - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n  - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n  - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n  - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "extent": {"spatial": {"bbox": [[-180.0, -60.0, 180.0, 82.75]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}, "instruments": ["c-sar", "msi"], "keywords": ["c-sar", "esa", "esa-worldcover", "global", "land-cover", "msi", "sentinel", "sentinel-1a", "sentinel-1b", "sentinel-2a", "sentinel-2b"], "license": "CC-BY-4.0", "platform": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "title": "ESA WorldCover"}, "fia": {"description": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's  [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928.  FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "extent": {"spatial": {"bbox": [[138.06, 0.92, 163.05, 9.78], [165.28, 4.57, 172.03, 14.61], [131.13, 2.95, 134.73, 8.1], [-124.763068, 24.523096, -66.949895, 49.384358], [-179.148909, 51.214183, -129.974167, 71.365162], [172.461667, 51.357688, 179.77847, 53.01075], [-178.334698, 18.910361, -154.806773, 28.402123], [144.618068, 13.234189, 144.956712, 13.654383], [-67.945404, 17.88328, -65.220703, 18.515683], [144.886331, 14.110472, 146.064818, 20.553802], [-65.085452, 17.673976, -64.564907, 18.412655], [-171.089874, -14.548699, -168.1433, -11.046934], [-178.334698, 18.910361, -154.806773, 28.402123]]}, "temporal": {"interval": [["2020-06-01T00:00:00Z", null]]}}, "keywords": ["biomass", "carbon", "fia", "forest", "forest-service", "species", "usda"], "license": "CC0-1.0", "title": "Forest Inventory and Analysis"}, "fws-nwi": {"description": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\n**NOTE:** Due to the variation in use and analysis of this data by the end user, each  state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "extent": {"spatial": {"bbox": [[-64.54958, 13.16667, 144.6, 71.99633], [144.6, 13.16667, 180.0, 71.99633], [-180.0, 13.16667, -64.54958, 71.99633]]}, "temporal": {"interval": [["2022-10-01T00:00:00Z", "2022-10-01T00:00:00Z"]]}}, "keywords": ["fws-nwi", "united-states", "usfws", "wetlands"], "license": "proprietary", "title": "FWS National Wetlands Inventory"}, "gap": {"description": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States.  These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "extent": {"spatial": {"bbox": [[-127.9710481801793, 22.797789263564383, -65.26634281147894, 51.64692620669362], [-178.13166387448902, 49.09079265233118, 179.87849702345594, 71.43382483774205], [-160.26640694607218, 18.851824447510786, -154.66974350173518, 22.295114188194738], [-67.9573345827195, 17.874066536543, -65.21836408976736, 18.5296513469496]]}, "temporal": {"interval": [["1999-01-01T00:00:00Z", "2011-12-31T00:00:00Z"]]}}, "keywords": ["gap", "land-cover", "landfire", "united-states", "usgs"], "license": "proprietary", "title": "USGS Gap Land Cover"}, "gbif": {"description": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below.  Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc.  Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2021-04-13T00:00:00Z", null]]}}, "keywords": ["biodiversity", "gbif", "species"], "license": "proprietary", "title": "Global Biodiversity Information Facility (GBIF)"}, "gnatsgo-rasters": {"description": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent.  These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "extent": {"spatial": {"bbox": [[-170.8513, -14.3799, -169.4152, -14.1432], [138.0315, 5.116, 163.1902, 10.2773], [144.6126, 13.2327, 144.9658, 13.6572], [-159.7909, 18.8994, -154.7815, 22.2464], [170.969, 6.0723, 171.9169, 8.71933], [145.0127, 14.1086, 145.9242, 18.8172], [130.8048, 2.9268, 134.9834, 8.0947], [157.3678, 49.0546, -117.2864, 71.4567], [-67.9506, 17.014, -64.3973, 19.3206], [-127.8881, 22.8782, -65.2748, 51.6039]]}, "temporal": {"interval": [["2020-07-01T00:00:00Z", "2020-07-01T00:00:00Z"]]}}, "keywords": ["gnatsgo-rasters", "natsgo", "rss", "soils", "ssurgo", "statsgo2", "united-states", "usda"], "license": "CC0-1.0", "title": "gNATSGO Soil Database - Rasters"}, "gnatsgo-tables": {"description": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent.  These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "extent": {"spatial": {"bbox": [[-170.8513, -14.3799, -169.4152, -14.1432], [138.0315, 5.116, 163.1902, 10.2773], [144.6126, 13.2327, 144.9658, 13.6572], [-159.7909, 18.8994, -154.7815, 22.2464], [170.969, 6.0723, 171.9169, 8.71933], [145.0127, 14.1086, 145.9242, 18.8172], [130.8048, 2.9268, 134.9834, 8.0947], [157.3678, 49.0546, -117.2864, 71.4567], [-67.9506, 17.014, -64.3973, 19.3206], [-127.8881, 22.8782, -65.2748, 51.6039]]}, "temporal": {"interval": [["2020-07-01T00:00:00Z", "2020-07-01T00:00:00Z"]]}}, "keywords": ["gnatsgo-tables", "natsgo", "rss", "soils", "ssurgo", "statsgo2", "united-states", "usda"], "license": "CC0-1.0", "title": "gNATSGO Soil Database - Tables"}, "goes-cmi": {"description": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis.  Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "extent": {"spatial": {"bbox": [[-180.0, -81.33, 6.3, 81.33], [141.7, -81.33, 180.0, 81.33]]}, "temporal": {"interval": [["2017-02-28T00:16:52Z", null]]}}, "instruments": ["ABI"], "keywords": ["abi", "cloud", "goes", "goes-16", "goes-17", "goes-18", "goes-19", "goes-cmi", "moisture", "nasa", "noaa", "satellite"], "license": "proprietary", "platform": "GOES-16,GOES-17,GOES-18,GOES-19", "title": "GOES-R Cloud & Moisture Imagery"}, "goes-glm": {"constellation": "GOES", "description": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "extent": {"spatial": {"bbox": [[-180.0, -66.56, -8.44, 66.56], [156.44, -66.56, 180.0, 66.56]]}, "temporal": {"interval": [["2018-02-13T16:10:00Z", null]]}}, "instruments": ["FM1", "FM2"], "keywords": ["fm1", "fm2", "goes", "goes-16", "goes-17", "goes-18", "goes-19", "goes-glm", "l2", "lightning", "nasa", "noaa", "satellite", "weather"], "license": "proprietary", "platform": "GOES-16,GOES-17,GOES-18,GOES-19", "processing:level": "L2", "title": "GOES-R Lightning Detection"}, "gpm-imerg-hhr": {"description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-06-01T00:00:00Z", "2021-05-31T23:30:00Z"]]}}, "keywords": ["gpm", "gpm-imerg-hhr", "imerg", "precipitation"], "license": "proprietary", "title": "GPM IMERG"}, "gridmet": {"description": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "extent": {"spatial": {"bbox": [[-124.76666663333334, 25.066666666666666, -67.05833330000002, 49.400000000000006]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", "2020-12-31T00:00:00Z"]]}}, "keywords": ["climate", "gridmet", "precipitation", "temperature", "vapor-pressure", "water"], "license": "CC0-1.0", "title": "gridMET"}, "hgb": {"description": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "extent": {"spatial": {"bbox": [[-180.0, -61.002778, 180.0, 84.0]]}, "temporal": {"interval": [["2010-12-31T00:00:00Z", "2010-12-31T00:00:00Z"]]}}, "keywords": ["biomass", "carbon", "hgb", "ornl"], "license": "proprietary", "title": "HGB: Harmonized Global Biomass for 2010"}, "hls2-l30": {"description": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance (SR)\nand top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the\njoint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI)\naboard the ESA (European Space Agency) Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\nThe combined measurement enables global observations of the land every 2-3 days at 30 meter\n(m) spatial resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution and\nMilitary Grid Reference System (MGRS) tiling system and are \"stackable\" for time series analysis.\nThis dataset is in the form of cloud-optimized GeoTIFFs. The HLS v2.0 data is generated by NASA's\nIMPACT team at Marshall Space Flight Center. The product latency is 1.7 days, from the satellite\noverpass to the HLS data availability at NASA's Land Processes Distributed Active Archive Center\n(LP DAAC). For more information see LP DAAC's\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\n\nThis collection contains HLS data collected from Landsat-8 and Landsat-9.\nHLS data generated from Sentinel-2 satellites can be found in a separate collection.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", null]]}}, "keywords": ["global", "hls", "hls2-l30", "imagery", "landsat", "landsat-8", "landsat-9", "satellite", "sentinel"], "license": "proprietary", "platform": "Landsat 8,Landsat 9", "title": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0, Landsat Data"}, "hls2-s30": {"description": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance (SR)\nand top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the\njoint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI)\naboard the ESA (European Space Agency) Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\nThe combined measurement enables global observations of the land every 2-3 days at 30 meter\n(m) spatial resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution and\nMilitary Grid Reference System (MGRS) tiling system and are \"stackable\" for time series analysis.\nThis dataset is in the form of cloud-optimized GeoTIFFs. The HLS v2.0 data is generated by NASA's\nIMPACT team at Marshall Space Flight Center. The product latency is 1.7 days, from the satellite\noverpass to the HLS data availability at NASA's Land Processes Distributed Active Archive Center\n(LP DAAC). For more information see LP DAAC's\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\n\nThis collection contains HLS data collected from Sentinel-2A, Sentinel-2B, and Sentinel-2C.\nHLS data generated from Landsat can be found in a separate collection.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", null]]}}, "keywords": ["global", "hls", "hls2-s30", "imagery", "landsat", "satellite", "sentinel", "sentinel-2a", "sentinel-2b", "sentinel-2c"], "license": "proprietary", "platform": "Sentinel-2A,Sentinel-2B,Sentinel-2C", "title": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data"}, "hrea": {"description": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. **Access**: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. **Reliability**: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. **Usage**: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. **Nighttime Lights**: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "extent": {"spatial": {"bbox": [[-117.413972, -55.54235, -53.092722, 32.718434], [-25.361528, -34.838027, 50.759908, 37.552639], [34.957638, -11.655904, 157.037723, 38.612083], [155.392502, -20.251178, 172.171458, 14.721388]]}, "temporal": {"interval": [["2012-12-31T00:00:00Z", "2019-12-31T00:00:00Z"]]}}, "keywords": ["electricity", "hrea", "viirs"], "license": "CC-BY-4.0", "title": "HREA: High Resolution Electricity Access"}, "io-biodiversity": {"description": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["biodiversity", "global", "io-biodiversity"], "license": "CC-BY-4.0", "title": "Biodiversity Intactness"}, "io-lulc": {"description": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution.  This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification.  The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2021-01-01T00:00:00Z"]]}}, "keywords": ["global", "io-lulc", "land-cover", "land-use", "sentinel"], "license": "CC-BY-4.0", "title": "Esri 10-Meter Land Cover (10-class)"}, "io-lulc-9-class": {"description": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model.  The Esri 2020 Land Cover map was also produced by Impact Observatory.  The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2023-01-01T00:00:00Z"]]}}, "keywords": ["global", "io-lulc-9-class", "land-cover", "land-use", "sentinel"], "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V1"}, "io-lulc-annual-v02": {"description": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}, "keywords": ["global", "io-lulc-annual-v02", "land-cover", "land-use", "sentinel"], "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V2"}, "jrc-gsw": {"description": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery.  Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020.  Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "extent": {"spatial": {"bbox": [[-180.0, -56.0, 180.0, 78.0]]}, "temporal": {"interval": [["1984-03-01T00:00:00Z", "2020-12-31T11:59:59Z"]]}}, "keywords": ["global", "jrc-gsw", "landsat", "water"], "license": "proprietary", "title": "JRC Global Surface Water"}, "kaza-hydroforecast": {"description": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n    \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n    * Zambezi at Chavuma\n    * Luanginga at Kalabo\n* Kwando basin sites\n    * Kwando at Kongola -- total basin flows\n    * Kwando Sub-basin 1\n    * Kwando Sub-basin 2 \n    * Kwando Sub-basin 3\n    * Kwando Sub-basin 4\n    * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "extent": {"spatial": {"bbox": [[21.04494, -17.792517, 23.343421, -13.08062], [23.343421, -17.792517, 23.343421, -17.792517], [23.343421, -17.792517, 23.343421, -17.792517], [21.04494, -15.13158, 21.04494, -15.13158], [21.80157, -16.01209, 21.80157, -16.01209], [22.91715, -17.38856, 22.91715, -17.38856], [23.00610530305009, -17.347398048598034, 23.00610530305009, -17.347398048598034], [22.669092, -14.971843, 22.669092, -14.971843], [22.675487, -13.08062, 22.675487, -13.08062]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", null]]}}, "keywords": ["hydroforecast", "hydrology", "kaza-hydroforecast", "streamflow", "upstream-tech", "water"], "license": "CDLA-Sharing-1.0", "title": "HydroForecast - Kwando & Upper Zambezi Rivers"}, "landsat-c2-l1": {"description": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1972-07-25T00:00:00Z", "2013-01-07T23:23:59Z"]]}}, "instruments": ["mss"], "keywords": ["global", "imagery", "landsat", "landsat-1", "landsat-2", "landsat-3", "landsat-4", "landsat-5", "landsat-c2-l1", "mss", "nasa", "satellite", "usgs"], "license": "proprietary", "platform": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "title": "Landsat Collection 2 Level-1"}, "landsat-c2-l2": {"description": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}, "instruments": ["tm", "etm+", "oli", "tirs"], "keywords": ["etm+", "global", "imagery", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2-l2", "nasa", "oli", "reflectance", "satellite", "temperature", "tirs", "tm", "usgs"], "license": "proprietary", "platform": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "title": "Landsat Collection 2 Level-2"}, "met-office-global-deterministic-height": {"description": "This collection offers 1 parameter at 33 available height levels (5m to 6000m) from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). \n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Height Levels\nAvailable height levels, in metres (m) above ground, are: \n* 5.0 10.0 20.0 30.0 50.0 75.0 100.0 150.0 200.0 250.0 300.0 400.0 500.0 600.0 700.0 800.0 1000.0 1250.0 1500.0 1750.0 2000.0 2250.0 2500.0 2750.0 3000.0 3250.0 3500.0 3750.0 4000.0 4500.0 5000.0 5500.0 6000.0\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cloud", "forecast", "global", "met-office", "met-office-global-deterministic-height", "weather"], "license": "proprietary", "title": "Height levels collection Met Office Global 10km deterministic weather forecast"}, "met-office-global-deterministic-near-surface": {"description": "This collection offers 48 parameters at near-surface level from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points).\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Data collection height\nThere are 3 forecast heights used within the near-surface this collection:\n* Surface: the default collection height\n* Screen level: 1.5m above the surface\n* Wind parameters: 10m above the surface\n\n## Timesteps\nFor most parameters, the following time steps are available, see exceptions below:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\nExceptions (for `accumulation`, `min`, `max` and `mean` parameters):\n* Height of orography (height_of_orography) is only available at 0H\n* Hourly latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT01H) is only available every hour from 1 to 54 hours\n* 3H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT03H) is only available every three hours from 57 to 144 hours\n* 6H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 144 hours\n* 6H precipitation accumulation (precipitation_accumulation-PT06H) is only available every six hours from 150 to 168 hours\n* Radiation flux in longwave downward at surface (radiation_flux_in_longwave_downward_at_surface) has six hourly timesteps from 150 to 162 hours\n* Radiation flux in uv downward at surface (radiation_flux_in_uv_downward_at_surface) has three hourly timesteps from 0 to 144 hours and six hourly timesteps from 150 to 162 hours\n* Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 144 hours\n* 6H rainfall accumulation (rainfall_accumulation-PT06H) is only available every six hours from 150 to 168 hours \n* Hourly rainfall rate from convection max (rainfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall rate from convection max (rainfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H rainfall rate from convection max (rainfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly snowfall rate from convection max (snowfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall rate from convection max (snowfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H snowfall rate from convection max (snowfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT03H) is only available every three hours from 57 to 144 hours\n* 6H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly temperature at screen level max (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H temperature at screen level max (temperature_at_screen_level_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H temperature at screen level max (temperature_at_screen_level_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly temperature at screen level min (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 54 hours\n* 3H remperature at screen level min (temperature_at_screen_level_min-PT03H) is only available every three hours from 57 to 144 hours\n* 6H temperature at screen level min (temperature_at_screen_level_min-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly wind gust at 10m max (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours \n* 3H wind gust at 10m max (wind_gust_at_10m_max-PT03H) is only available every three hours from 3 to 144 hours\n* 6H wind gust at 10m max (wind_gust_at_10m_max-PT06H) is only available every six hours from 54 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cloud", "fog", "forecast", "global", "heat-flux", "humidity", "met-office", "met-office-global-deterministic-near-surface", "precipitation", "pressure", "radiation", "rainfall", "snow", "temperature", "wind"], "license": "proprietary", "title": "Near-surface level collection Met Office global deterministic 10km forecast"}, "met-office-global-deterministic-pressure": {"description": "This collection offers 8 parameters at 33 available pressure levels (from 100000Pa to 1000Pa) from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points).\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Pressure Levels\nAvailable pressure levels, in Pascals (Pa), are:\n* 100000.0 97500.0 95000.0 92500.0 90000.0 85000.0 80000.0 75000.0 70000.0 65000.0 60000.0 55000.0 50000.0 45000.0 40000.0 37500.0 35000.0 32500.0 30000.0 27500.0 25000.0 22500.0 20000.0 17500.0 15000.0 12500.0 10000.0 7000.0 5000.0 4000.0 3000.0 2000.0 1000.0 500.0 200.0 100.0 40.0\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions)\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cloud", "global", "met-office-global-deterministic-pressure", "metoffice"], "license": "proprietary", "title": "Pressure levels collection Met Office Global 10km deterministic weather forecast"}, "met-office-global-deterministic-whole-atmosphere": {"description": "This collection offers 14 whole-atmosphere parameters from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). \n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions)\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cloud", "global", "met-office-global-deterministic-whole-atmosphere", "metoffice"], "license": "proprietary", "title": "Whole Atmosphere collection Met Office Global 10km deterministic weather forecast"}, "met-office-uk-deterministic-height": {"description": "This collection offers 4 parameters at 33 available height levels (5m to 6000m) from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files.\n\n## Height Levels\nAvailable height levels, in metres (m) above ground, are: \n* 5.0 10.0 20.0 30.0 40.0 50.0 60.0 75.0 100.0 125.0 150.0 175.0 200.0 225.0 250.0 275.0 300.0 350.0 400.0 450.0 500.0 600.0 700.0 800.0 900.0 1000.0 1125.0 1250.0 1375.0 1500.0 1625.0 1750.0 1875.0 2000.0 2125.0 2250.0 2375.0 2500.0 2625.0 2750.0 2875.0 3000.0 3125.0 3250.0 3375.0 3500.0 3625.0 3750.0 3875.0 4000.0 4500.0 5000.0 5500.0 6000.0 6750.0 7500.0\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643\u00b0N, 10.6734\u00b0W \n* Northwest: 61.3322\u00b0N, 13.7254\u00b0W \n* Northeast: 61.6102\u00b0N, 4.3408\u00b0E \n* Southeast: 49.0594\u00b0N, 2.4654\u00b0E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours \n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-13.7254, 48.8643, 4.3408, 61.6102]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cloud", "forecast", "height", "met-office", "met-office-uk-deterministic-height", "temperature", "uk", "weather", "wind"], "license": "proprietary", "title": "Height levels collection Met Office UKV 2km deterministic forecast"}, "met-office-uk-deterministic-near-surface": {"description": "This collection offers 35 parameters at near-surface level from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid.\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. \n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area: \n* Southwest: 48.8643\u00b0N, 10.6734\u00b0W \n* Northwest: 61.3322\u00b0N, 13.7254\u00b0W \n* Northeast: 61.6102\u00b0N, 4.3408\u00b0E \n* Southeast: 49.0594\u00b0N, 2.4654\u00b0E \n\n## Data collection height\nThere are 3 forecast heights used within the near-surface this collection:\n* Surface: the default collection height\n* Screen level: 1.5m above the surface\n* Wind parameters: 10m above the surface\n\n## Timesteps\nFor most parameters, the following time steps are available, see exceptions below:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours\n\nExceptions (for `rate`, `accumulation`, `min` and `max` parameters):\n* Hourly hail fall accumulation (hail_fall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* Hail fall rate (hail_fall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Precipitation rate (precipitation_rate) has 15 minutely timesteps from 0 to 54 hours\n* Pressure at mean sea level (pressure_at_mean_sea_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Rainfall rate (rainfall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Hourly snowfall accumulation (snowfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall accumulation (snowfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Snowfall rate (snowfall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Temperature at screen level (temperature_at_screen_level) has 15 minutely timesteps from 0 to 12 hours and hourly timesteps from 13 to 120 hours\n* Hourly temperature at screen level maximum (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 120 hours\n* Hourly temperature at screen level minimum (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 120 hours\n* Dew point temperature at screen level (temperature_of_dew_point_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Visibility at screen level (visibility_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Wind direction at 10m (wind_direction_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Wind gust at 10m (wind_gust_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly wind gust at 10m maximum (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H wind gust at 10m maximum(wind_gust_at_10m_max-PT03H) is only available every three hours from 57 to 120 hours\n* Wind speed at 10m (wind_speed_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. \n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: \n* variables store the data \n* dimensions give relevant dimension information for the variables \n* attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.  \n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. \n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.  \n\n## How to cite\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). \n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-13.7254, 48.8643, 4.3408, 61.6102]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["forecast", "humidity", "met-office", "met-office-uk-deterministic-near-surface", "precipitation", "pressure", "temperature", "uk", "weather", "wind"], "license": "proprietary", "title": "Near-surface level collection Met Office UKV 2km deterministic forecast"}, "met-office-uk-deterministic-pressure": {"description": "This collection offers 7 parameters at 33 available pressure levels (from 100000Pa to 1000Pa) from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid.\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Pressure Levels\nAvailable pressure levels, in Pascals (Pa), are:\n* 100000.0 97500.0 95000.0 92500.0 90000.0 85000.0 80000.0 75000.0 70000.0 65000.0 60000.0 55000.0 50000.0 45000.0 40000.0 37500.0 35000.0 32500.0 30000.0 27500.0 25000.0 22500.0 20000.0 17500.0 15000.0 12500.0 10000.0 7000.0 5000.0 4000.0 3000.0 2000.0 1000.0\nExceptions:\n* Wet bulb potential temperature on pressure levels (wet_bulb_potential_temperature_on_pressure_levels) available pressure levels, in Pascals (Pa), are 85000.0 70000.0 50000.0\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643\u00b0N, 10.6734\u00b0W \n* Northwest: 61.3322\u00b0N, 13.7254\u00b0W \n* Northeast: 61.6102\u00b0N, 4.3408\u00b0E \n* Southeast: 49.0594\u00b0N, 2.4654\u00b0E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours \n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-13.7254, 48.8643, 4.3408, 61.6102]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["forecast", "humidity", "met-office", "met-office-uk-deterministic-pressure", "pressure", "temperature", "uk", "weather", "wind"], "license": "proprietary", "title": "Pressure levels collection Met Office UKV 2km deterministic forecast"}, "met-office-uk-deterministic-whole-atmosphere": {"description": "This collection offers 11 whole-atmosphere parameters from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files.\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643\u00b0N, 10.6734\u00b0W \n* Northwest: 61.3322\u00b0N, 13.7254\u00b0W \n* Northeast: 61.6102\u00b0N, 4.3408\u00b0E \n* Southeast: 49.0594\u00b0N, 2.4654\u00b0E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours\n\nExceptions:\n* CAPE surface (CAPE_surface) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly lightning flash accumulation (lightning_flash_accumulation-PT01H) is only available every hour from 1 to 54 hours\n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency:\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon \u2013 Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n", "extent": {"spatial": {"bbox": [[-13.7254, 48.8643, 4.3408, 61.6102]]}, "temporal": {"interval": [["2023-12-15T00:00:00Z", null]]}}, "keywords": ["cape", "cloud", "forecast", "freezing", "lightning", "met-office", "met-office-uk-deterministic-whole-atmosphere", "uk", "weather", "wet-bulb"], "license": "proprietary", "title": "Whole Atmosphere collection Met Office UKV 2km deterministic forecast"}, "mobi": {"description": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity.  For each type, this data set includes five different layers &ndash; one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) &ndash; for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "extent": {"spatial": {"bbox": [[-130.23751729209377, 21.736663926419077, -63.66198266211968, 49.184200486300604]]}, "temporal": {"interval": [["2020-04-14T00:00:00Z", "2020-04-14T00:00:00Z"]]}}, "keywords": ["biodiversity", "mobi", "natureserve", "united-states"], "license": "proprietary", "title": "MoBI: Map of Biodiversity Importance"}, "modis-09A1-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "imagery", "mod09a1", "modis", "modis-09a1-061", "myd09a1", "nasa", "reflectance", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Surface Reflectance 8-Day (500m)"}, "modis-09Q1-061": {"description": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "imagery", "mod09q1", "modis", "modis-09q1-061", "myd09q1", "nasa", "reflectance", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Surface Reflectance 8-Day (250m)"}, "modis-10A1-061": {"description": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-24T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod10a1", "modis", "modis-10a1-061", "myd10a1", "nasa", "satellite", "snow", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Snow Cover Daily"}, "modis-10A2-061": {"description": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod10a2", "modis", "modis-10a2-061", "myd10a2", "nasa", "satellite", "snow", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Snow Cover 8-day"}, "modis-11A1-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-24T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod11a1", "modis", "modis-11a1-061", "myd11a1", "nasa", "satellite", "temperature", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Land Surface Temperature/Emissivity Daily"}, "modis-11A2-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod11a2", "modis", "modis-11a2-061", "myd11a2", "nasa", "satellite", "temperature", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Land Surface Temperature/Emissivity 8-Day"}, "modis-13A1-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod13a1", "modis", "modis-13a1-061", "myd13a1", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Vegetation Indices 16-Day (500m)"}, "modis-13Q1-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod13q1", "modis", "modis-13q1-061", "myd13q1", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Vegetation Indices 16-Day (250m)"}, "modis-14A1-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "fire", "global", "mod14a1", "modis", "modis-14a1-061", "myd14a1", "nasa", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Thermal Anomalies/Fire Daily"}, "modis-14A2-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "fire", "global", "mod14a2", "modis", "modis-14a2-061", "myd14a2", "nasa", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Thermal Anomalies/Fire 8-Day"}, "modis-15A2H-061": {"description": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-07-04T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mcd15a2h", "mod15a2h", "modis", "modis-15a2h-061", "myd15a2h", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Leaf Area Index/FPAR 8-Day"}, "modis-15A3H-061": {"description": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2002-07-04T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mcd15a3h", "modis", "modis-15a3h-061", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Leaf Area Index/FPAR 4-Day"}, "modis-16A3GF-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2001-01-01T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod16a3gf", "modis", "modis-16a3gf-061", "myd16a3gf", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Net Evapotranspiration Yearly Gap-Filled"}, "modis-17A2H-061": {"description": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod17a2h", "modis", "modis-17a2h-061", "myd17a2h", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Gross Primary Productivity 8-Day"}, "modis-17A2HGF-061": {"description": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod17a2hgf", "modis", "modis-17a2hgf-061", "myd17a2hgf", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled"}, "modis-17A3HGF-061": {"description": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-18T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod17a3hgf", "modis", "modis-17a3hgf-061", "myd17a3hgf", "nasa", "satellite", "terra", "vegetation"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Net Primary Production Yearly Gap-Filled"}, "modis-21A2-061": {"description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-16T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "mod21a2", "modis", "modis-21a2-061", "myd21a2", "nasa", "satellite", "temperature", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day"}, "modis-43A4-061": {"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-02-16T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "global", "imagery", "mcd43a4", "modis", "modis-43a4-061", "nasa", "reflectance", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily"}, "modis-64A1-061": {"description": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2000-11-01T00:00:00Z", null]]}}, "instruments": ["modis"], "keywords": ["aqua", "fire", "global", "imagery", "mcd64a1", "modis", "modis-64a1-061", "nasa", "satellite", "terra"], "license": "proprietary", "platform": "aqua,terra", "title": "MODIS Burned Area Monthly"}, "ms-buildings": {"description": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\n**Stage 1: Semantic Segmentation**\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\n**Stage 2: Polygonization**\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "extent": {"spatial": {"bbox": [[-180.0, 90.0, 180.0, -90.0]]}, "temporal": {"interval": [["2014-01-01T00:00:00Z", null]]}}, "keywords": ["bing-maps", "buildings", "delta", "footprint", "geoparquet", "microsoft", "ms-buildings"], "license": "ODbL-1.0", "title": "Microsoft Building Footprints"}, "mtbs": {"description": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States.  The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "extent": {"spatial": {"bbox": [[-166.705027, 56.331164, -137.664767, 69.980073], [-127.579429, 23.680916, -65.871833, 50.870718]]}, "temporal": {"interval": [["1984-12-31T00:00:00Z", "2018-12-31T00:00:00Z"]]}}, "keywords": ["fire", "forest", "mtbs", "usda", "usfs", "usgs"], "license": "proprietary", "title": "MTBS: Monitoring Trends in Burn Severity"}, "naip": {"description": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "extent": {"spatial": {"bbox": [[-124.784, 24.744, -66.951, 49.346], [-156.003, 19.059, -154.809, 20.127], [-67.316, 17.871, -65.596, 18.565], [-64.94, 17.622, -64.56, 17.814]]}, "temporal": {"interval": [["2010-01-01T00:00:00Z", "2023-12-31T00:00:00Z"]]}}, "keywords": ["aerial", "afpo", "agriculture", "imagery", "naip", "united-states", "usda"], "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program"}, "nasa-nex-gddp-cmip6": {"description": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- **model** is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- **scenario** is one of \"historical\", \"ssp245\" or \"ssp585\".\n- **variable** is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- **year** depends on the value of *scenario*. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some *experimental* **reference files** as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", "2100-12-31T00:00:00Z"]]}}, "keywords": ["climate", "cmip6", "humidity", "nasa", "nasa-nex-gddp-cmip6", "precipitation", "temperature"], "license": "proprietary", "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)"}, "nasadem": {"description": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "extent": {"spatial": {"bbox": [[-179.000139, -56.000139, 179.000139, 61.000139]]}, "temporal": {"interval": [["2000-02-20T00:00:00Z", "2000-02-20T00:00:00Z"]]}}, "keywords": ["dem", "elevation", "jpl", "nasa", "nasadem", "nga", "srtm", "usgs"], "license": "proprietary", "title": "NASADEM HGT v001"}, "noaa-c-cap": {"description": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S.  Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery.  Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another.  The use of standardized data and procedures assures consistency through time and across geographies.  C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure.  The data are updated every 5 years.", "extent": {"spatial": {"bbox": [[-160.315986, 17.892786, -64.966857, 49.471148], [-127.984554, 22.718719, -64.966857, 49.471148], [-160.315986, 18.879215, -154.706564, 22.271167], [-67.290869, 17.892786, -65.230246, 18.538629]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2016-12-31T00:00:00Z"], ["1975-01-01T00:00:00Z", "2016-12-31T00:00:00Z"], ["1992-01-01T00:00:00Z", "2005-12-31T00:00:00Z"], ["2010-01-01T00:00:00Z", "2010-12-31T00:00:00Z"]]}}, "keywords": ["coastal", "land-cover", "land-use", "noaa", "noaa-c-cap"], "license": "proprietary", "title": "C-CAP Regional Land Cover and Change"}, "noaa-cdr-ocean-heat-content": {"description": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1972-03-01T00:00:00Z", "2022-03-31T23:59:59Z"]]}}, "keywords": ["climate", "global", "noaa", "noaa-cdr-ocean-heat-content", "ocean", "temperature"], "license": "proprietary", "title": "Global Ocean Heat Content CDR"}, "noaa-cdr-ocean-heat-content-netcdf": {"description": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1972-03-01T00:00:00Z", "2022-03-31T23:59:59Z"]]}}, "keywords": ["climate", "global", "noaa", "noaa-cdr-ocean-heat-content-netcdf", "ocean", "temperature"], "license": "proprietary", "title": "Global Ocean Heat Content CDR NetCDFs"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"description": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "extent": {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-09-01T00:00:00Z", null]]}}, "keywords": ["climate", "global", "noaa", "noaa-cdr-sea-surface-temperature-optimum-interpolation", "ocean", "temperature"], "license": "proprietary", "title": "Sea Surface Temperature - Optimum Interpolation CDR"}, "noaa-cdr-sea-surface-temperature-whoi": {"description": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "extent": {"spatial": {"bbox": [[-180.0, -90, 180, 90]]}, "temporal": {"interval": [["1988-01-01T00:00:00Z", null]]}}, "keywords": ["climate", "global", "noaa", "noaa-cdr-sea-surface-temperature-whoi", "ocean", "temperature"], "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"description": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-180.0, -90, 180, 90]]}, "temporal": {"interval": [["1988-01-01T00:00:00Z", null]]}}, "keywords": ["climate", "global", "noaa", "noaa-cdr-sea-surface-temperature-whoi-netcdf", "ocean", "temperature"], "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR NetCDFs"}, "noaa-climate-normals-gridded": {"description": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n    - 100-year (1901\u20132000)\n    - 30-year (1991\u20132020)\n    - 15-year (2006\u20132020)\n- Daily normals\n    - 30-year (1991\u20132020)\n    - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n| Abbreviation |               Description                |\n| ------------ | ---------------------------------------- |\n| prcp         | Precipitation over the time period       |\n| tavg         | Mean temperature over the time period    |\n| tmax         | Maximum temperature over the time period |\n| tmin         | Minimum temperature over the time period |\n\nAnd an \"aggregation\":\n\n| Abbreviation |                                  Description                                   |\n| ------------ | ------------------------------------------------------------------------------ |\n| max          | Maximum of the variable over the time period                                   |\n| min          | Minimum of the variable over the time period                                   |\n| std          | Standard deviation of the value over the time period                           |\n| flag         | An count of the number of inputs (months, years, etc.) to calculate the normal |\n| norm         | The normal for the variable over the time period                               |\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "extent": {"spatial": {"bbox": [[-124.708333, 24.541666, -66.999995, 49.375001]]}, "temporal": {"interval": [["1901-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["climate-normals", "climatology", "conus", "noaa", "noaa-climate-normals-gridded", "surface-observations", "weather"], "license": "proprietary", "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)"}, "noaa-climate-normals-netcdf": {"description": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n    - 100-year (1901\u20132000)\n    - 30-year (1991\u20132020)\n    - 15-year (2006\u20132020)\n- Daily normals\n    - 30-year (1991\u20132020)\n    - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-124.708333, 24.541666, -66.999995, 49.375001]]}, "temporal": {"interval": [["1901-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["climate-normals", "climatology", "conus", "noaa", "noaa-climate-normals-netcdf", "surface-observations", "weather"], "license": "proprietary", "title": "NOAA US Gridded Climate Normals (NetCDF)"}, "noaa-climate-normals-tabular": {"description": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "extent": {"spatial": {"bbox": [[-177.38333, -14.3306, 174.1, 71.3214]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}, "keywords": ["climate-normals", "climatology", "conus", "noaa", "noaa-climate-normals-tabular", "surface-observations", "weather"], "license": "proprietary", "title": "NOAA US Tabular Climate Normals"}, "noaa-hrrr": {"description": "The [High-Resolution Rapid Refresh (HRRR)](https://rapidrefresh.noaa.gov/hrrr/) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model. HRRR runs are initialized by 3-km grids with 3-km radar assimilation. Radar data is assimilated in the HRRR every 15 minutes over a 1-hour period adding further detail to that provided by the hourly overarching 13-km Rapid Refresh (RAP) model.\n\nThe HRRR model is especially useful for short-term weather forecasting, including severe weather events, aviation weather, and renewable energy applications. The data are available in GRIB2 format and are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-174.8849, 21.122192719272277, -60.891244531606546, 76.3464], [-134.12142793280145, 21.122192719272277, -60.891244531606546, 52.62870335266725], [-174.8849, 41.596, -115.6988, 76.3464]]}, "temporal": {"interval": [["2021-03-21T00:00:00Z", null]]}}, "keywords": ["atmospheric", "forecast", "hrrr", "noaa", "noaa-hrrr", "weather"], "license": "CC-BY-4.0", "title": "NOAA High Resolution Rapid Refresh (HRRR)"}, "noaa-mrms-qpe-1h-pass1": {"description": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-176.0, 9.0, 150.0, 72.0], [-130.0, 20.0, -60.0, 55.0], [-164.0, 15.0, -151.0, 26.0], [140.0, 9.0, 150.0, 18.0], [-176.0, 50.0, -126.0, 72.0], [-90.0, 10.0, -60.0, 25.0]]}, "temporal": {"interval": [["2022-07-21T20:00:00Z", null]]}}, "keywords": ["caribbean", "guam", "mrms", "noaa", "noaa-mrms-qpe-1h-pass1", "precipitation", "qpe", "united-states", "weather"], "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 1"}, "noaa-mrms-qpe-1h-pass2": {"description": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "extent": {"spatial": {"bbox": [[-176.0, 9.0, 150.0, 72.0], [-130.0, 20.0, -60.0, 55.0], [-164.0, 15.0, -151.0, 26.0], [140.0, 9.0, 150.0, 18.0], [-176.0, 50.0, -126.0, 72.0], [-90.0, 10.0, -60.0, 25.0]]}, "temporal": {"interval": [["2022-07-21T20:00:00Z", null]]}}, "keywords": ["caribbean", "guam", "mrms", "noaa", "noaa-mrms-qpe-1h-pass2", "precipitation", "qpe", "united-states", "weather"], "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 2"}, "noaa-mrms-qpe-24h-pass2": {"description": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "extent": {"spatial": {"bbox": [[-176.0, 9.0, 150.0, 72.0], [-130.0, 20.0, -60.0, 55.0], [-164.0, 15.0, -151.0, 26.0], [140.0, 9.0, 150.0, 18.0], [-176.0, 50.0, -126.0, 72.0], [-90.0, 10.0, -60.0, 25.0]]}, "temporal": {"interval": [["2022-07-21T20:00:00Z", null]]}}, "keywords": ["caribbean", "guam", "mrms", "noaa", "noaa-mrms-qpe-24h-pass2", "precipitation", "qpe", "united-states", "weather"], "license": "proprietary", "title": "NOAA MRMS QPE 24-Hour Pass 2"}, "noaa-nclimgrid-monthly": {"description": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains **Monthly** data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*\n", "extent": {"spatial": {"bbox": [[-124.708333, 24.541666, -66.999995, 49.375001]]}, "temporal": {"interval": [["1895-01-01T00:00:00Z", null]]}}, "keywords": ["climate", "nclimgrid", "noaa", "noaa-nclimgrid-monthly", "precipitation", "temperature", "united-states"], "license": "proprietary", "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)"}, "nrcan-landcover": {"description": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "extent": {"spatial": {"bbox": [[-141.003, 41.6755, -52.6174, 83.1139]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2020-01-01T00:00:00Z"]]}}, "keywords": ["canada", "land-cover", "landsat", "north-america", "nrcan-landcover", "remote-sensing"], "license": "OGL-Canada-2.0", "title": "Land Cover of Canada"}, "planet-nicfi-analytic": {"description": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "extent": {"spatial": {"bbox": [[-180.0, -34.161818157002, 180.0, 30.145127179625]]}, "temporal": {"interval": [["2015-12-01T00:00:00Z", null]]}}, "keywords": ["imagery", "nicfi", "planet", "planet-nicfi-analytic", "satellite", "tropics"], "license": "proprietary", "title": "Planet-NICFI Basemaps (Analytic)"}, "planet-nicfi-visual": {"description": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "extent": {"spatial": {"bbox": [[-180.0, -34.161818157002, 180.0, 30.145127179625]]}, "temporal": {"interval": [["2015-12-01T00:00:00Z", null]]}}, "keywords": ["imagery", "nicfi", "planet", "planet-nicfi-visual", "satellite", "tropics"], "license": "proprietary", "title": "Planet-NICFI Basemaps (Visual)"}, "sentinel-1-grd": {"constellation": "Sentinel-1", "description": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2014-10-10T00:28:21Z", null]]}}, "keywords": ["c-band", "copernicus", "esa", "grd", "sar", "sentinel", "sentinel-1", "sentinel-1-grd", "sentinel-1a", "sentinel-1b", "sentinel-1c"], "license": "proprietary", "platform": "SENTINEL-1A,SENTINEL-1B,SENTINEL-1C", "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)"}, "sentinel-1-rtc": {"constellation": "Sentinel-1", "description": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2014-10-10T00:28:21Z", null]]}}, "keywords": ["c-band", "copernicus", "esa", "rtc", "sar", "sentinel", "sentinel-1", "sentinel-1-rtc", "sentinel-1a", "sentinel-1b", "sentinel-1c"], "license": "CC-BY-4.0", "platform": "SENTINEL-1A,SENTINEL-1B,SENTINEL-1C", "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)"}, "sentinel-2-l2a": {"constellation": "sentinel-2", "description": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days.  This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2015-06-27T10:25:31Z", null]]}}, "instruments": ["msi"], "keywords": ["copernicus", "esa", "global", "imagery", "msi", "reflectance", "satellite", "sentinel", "sentinel-2", "sentinel-2-l2a", "sentinel-2a", "sentinel-2b"], "license": "proprietary", "platform": "Sentinel-2A,Sentinel-2B", "title": "Sentinel-2 Level-2A"}, "sentinel-3-olci-lfr-l2-netcdf": {"constellation": "Sentinel-3", "description": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2016-04-25T11:33:47.368562Z", null]]}}, "instruments": ["OLCI"], "keywords": ["biomass", "copernicus", "esa", "land", "olci", "sentinel", "sentinel-3", "sentinel-3-olci-lfr-l2-netcdf", "sentinel-3a", "sentinel-3b"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Land (Full Resolution)"}, "sentinel-3-olci-wfr-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-11-01T00:07:01.738487Z", null]]}}, "instruments": ["OLCI"], "keywords": ["copernicus", "esa", "ocean", "olci", "sentinel", "sentinel-3", "sentinel-3-olci-wfr-l2-netcdf", "sentinel-3a", "sentinel-3b", "water"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Water (Full Resolution)"}, "sentinel-3-slstr-frp-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2020-08-08T23:11:15.617203Z", null]]}}, "instruments": ["SLSTR"], "keywords": ["copernicus", "esa", "fire", "satellite", "sentinel", "sentinel-3", "sentinel-3-slstr-frp-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr", "temperature"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Fire Radiative Power"}, "sentinel-3-slstr-lst-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2016-04-19T01:35:17.188500Z", null]]}}, "instruments": ["SLSTR"], "keywords": ["copernicus", "esa", "land", "satellite", "sentinel", "sentinel-3", "sentinel-3-slstr-lst-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr", "temperature"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Land Surface Temperature"}, "sentinel-3-slstr-wst-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2017-10-31T23:59:57.451604Z", null]]}}, "instruments": ["SLSTR"], "keywords": ["copernicus", "esa", "ocean", "satellite", "sentinel", "sentinel-3", "sentinel-3-slstr-wst-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr", "temperature"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Sea Surface Temperature"}, "sentinel-3-sral-lan-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "extent": {"spatial": {"bbox": [[-180, -81.5, 180, 81.5]]}, "temporal": {"interval": [["2016-03-01T14:07:51.632846Z", null]]}}, "instruments": ["SRAL"], "keywords": ["altimetry", "copernicus", "esa", "radar", "satellite", "sentinel", "sentinel-3", "sentinel-3-sral-lan-l2-netcdf", "sentinel-3a", "sentinel-3b", "sral"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Land Radar Altimetry"}, "sentinel-3-sral-wat-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "extent": {"spatial": {"bbox": [[-180, -81.5, 180, 81.5]]}, "temporal": {"interval": [["2017-01-28T00:59:14.149496Z", null]]}}, "instruments": ["SRAL"], "keywords": ["altimetry", "copernicus", "esa", "ocean", "radar", "satellite", "sentinel", "sentinel-3", "sentinel-3-sral-wat-l2-netcdf", "sentinel-3a", "sentinel-3b", "sral"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Ocean Radar Altimetry"}, "sentinel-3-synergy-aod-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2020-04-16T19:36:28.012367Z", null]]}}, "instruments": ["OLCI", "SLSTR"], "keywords": ["aerosol", "copernicus", "esa", "global", "olci", "satellite", "sentinel", "sentinel-3", "sentinel-3-synergy-aod-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Global Aerosol"}, "sentinel-3-synergy-syn-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-09-22T16:51:00.001276Z", null]]}}, "instruments": ["OLCI", "SLSTR"], "keywords": ["aerosol", "copernicus", "esa", "land", "olci", "reflectance", "satellite", "sentinel", "sentinel-3", "sentinel-3-synergy-syn-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Land Surface Reflectance and Aerosol"}, "sentinel-3-synergy-v10-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "extent": {"spatial": {"bbox": [[-180.0, -56.0, 180.0, 75.0]]}, "temporal": {"interval": [["2018-09-27T11:17:21Z", null]]}}, "instruments": ["OLCI", "SLSTR"], "keywords": ["copernicus", "esa", "ndvi", "olci", "reflectance", "satellite", "sentinel", "sentinel-3", "sentinel-3-synergy-v10-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vg1-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "extent": {"spatial": {"bbox": [[-180.0, -56.0, 180.0, 75.0]]}, "temporal": {"interval": [["2018-10-04T23:17:21Z", null]]}}, "instruments": ["OLCI", "SLSTR"], "keywords": ["copernicus", "esa", "ndvi", "olci", "reflectance", "satellite", "sentinel", "sentinel-3", "sentinel-3-synergy-vg1-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vgp-l2-netcdf": {"constellation": "Sentinel-3", "description": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-10-08T08:09:40.491227Z", null]]}}, "instruments": ["OLCI", "SLSTR"], "keywords": ["copernicus", "esa", "olci", "reflectance", "satellite", "sentinel", "sentinel-3", "sentinel-3-synergy-vgp-l2-netcdf", "sentinel-3a", "sentinel-3b", "slstr"], "license": "proprietary", "platform": "Sentinel-3A,Sentinel-3B", "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)"}, "sentinel-5p-l2-netcdf": {"constellation": "Sentinel-5P", "description": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2018-04-30T00:18:50Z", null]]}}, "instruments": ["TROPOMI"], "keywords": ["air-quality", "climate-change", "copernicus", "esa", "forecasting", "sentinel", "sentinel-5-precursor", "sentinel-5p", "sentinel-5p-l2-netcdf", "tropomi"], "license": "proprietary", "platform": "Sentinel 5 Precursor", "title": "Sentinel-5P Level-2"}, "terraclimate": {"description": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1958-01-01T00:00:00Z", "2021-12-01T00:00:00Z"]]}}, "keywords": ["climate", "precipitation", "temperature", "terraclimate", "vapor-pressure", "water"], "license": "CC0-1.0", "title": "TerraClimate"}, "us-census": {"description": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "extent": {"spatial": {"bbox": [[-124.763068, 24.523096, -66.949895, 49.384358], [-179.148909, 51.214183, -129.974167, 71.365162], [172.461667, 51.357688, 179.77847, 53.01075], [-178.334698, 18.910361, -154.806773, 28.402123], [144.618068, 13.234189, 144.956712, 13.654383], [-67.945404, 17.88328, -65.220703, 18.515683], [144.886331, 14.110472, 146.064818, 20.553802], [-65.085452, 17.673976, -64.564907, 18.412655], [-171.089874, -14.548699, -168.1433, -11.046934], [-178.334698, 18.910361, -154.806773, 28.402123]]}, "temporal": {"interval": [["2021-08-01T00:00:00Z", "2021-08-01T00:00:00Z"]]}}, "keywords": ["administrative-boundaries", "demographics", "population", "us-census", "us-census-bureau"], "license": "proprietary", "title": "US Census"}, "usda-cdl": {"description": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "extent": {"spatial": {"bbox": [[-127.887212, 22.94027, -65.345507, 51.603492]]}, "temporal": {"interval": [["2008-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}, "keywords": ["agriculture", "land-cover", "land-use", "united-states", "usda", "usda-cdl"], "license": "proprietary", "title": "USDA Cropland Data Layers (CDLs)"}, "usgs-lcmap-conus-v13": {"description": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "extent": {"spatial": {"bbox": [[-129.27732, 21.805095, -63.11843, 52.92172]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2021-12-31T00:00:00Z"]]}}, "keywords": ["conus", "land-cover", "land-cover-change", "lcmap", "usgs", "usgs-lcmap-conus-v13"], "license": "proprietary", "title": "USGS LCMAP CONUS Collection 1.3"}, "usgs-lcmap-hawaii-v10": {"description": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "extent": {"spatial": {"bbox": [[-161.27577, 18.505136, -154.058649, 22.624478]]}, "temporal": {"interval": [["2000-01-01T00:00:00Z", "2020-12-31T00:00:00Z"]]}}, "keywords": ["hawaii", "land-cover", "land-cover-change", "lcmap", "usgs", "usgs-lcmap-hawaii-v10"], "license": "proprietary", "title": "USGS LCMAP Hawaii Collection 1.0"}}, "providers_config": {"3dep-lidar-classification": {"_collection": "3dep-lidar-classification"}, "3dep-lidar-copc": {"_collection": "3dep-lidar-copc"}, "3dep-lidar-dsm": {"_collection": "3dep-lidar-dsm"}, "3dep-lidar-dtm": {"_collection": "3dep-lidar-dtm"}, "3dep-lidar-dtm-native": {"_collection": "3dep-lidar-dtm-native"}, "3dep-lidar-hag": {"_collection": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"_collection": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"_collection": "3dep-lidar-pointsourceid"}, "3dep-lidar-returns": {"_collection": "3dep-lidar-returns"}, "3dep-seamless": {"_collection": "3dep-seamless"}, "alos-dem": {"_collection": "alos-dem"}, "alos-fnf-mosaic": {"_collection": "alos-fnf-mosaic"}, "alos-palsar-mosaic": {"_collection": "alos-palsar-mosaic"}, "aster-l1t": {"_collection": "aster-l1t"}, "chesapeake-lc-13": {"_collection": "chesapeake-lc-13"}, "chesapeake-lc-7": {"_collection": "chesapeake-lc-7"}, "chesapeake-lu": {"_collection": "chesapeake-lu"}, "chloris-biomass": {"_collection": "chloris-biomass"}, "cil-gdpcir-cc-by": {"_collection": "cil-gdpcir-cc-by"}, "cil-gdpcir-cc-by-sa": {"_collection": "cil-gdpcir-cc-by-sa"}, "cil-gdpcir-cc0": {"_collection": "cil-gdpcir-cc0"}, "conus404": {"_collection": "conus404"}, "cop-dem-glo-30": {"_collection": "cop-dem-glo-30"}, "cop-dem-glo-90": {"_collection": "cop-dem-glo-90"}, "daymet-annual-hi": {"_collection": "daymet-annual-hi"}, "daymet-annual-na": {"_collection": "daymet-annual-na"}, "daymet-annual-pr": {"_collection": "daymet-annual-pr"}, "daymet-daily-hi": {"_collection": "daymet-daily-hi"}, "daymet-daily-na": {"_collection": "daymet-daily-na"}, "daymet-daily-pr": {"_collection": "daymet-daily-pr"}, "daymet-monthly-hi": {"_collection": "daymet-monthly-hi"}, "daymet-monthly-na": {"_collection": "daymet-monthly-na"}, "daymet-monthly-pr": {"_collection": "daymet-monthly-pr"}, "deltares-floods": {"_collection": "deltares-floods"}, "deltares-water-availability": {"_collection": "deltares-water-availability"}, "drcog-lulc": {"_collection": "drcog-lulc"}, "eclipse": {"_collection": "eclipse"}, "ecmwf-forecast": {"_collection": "ecmwf-forecast"}, "era5-pds": {"_collection": "era5-pds"}, "esa-cci-lc": {"_collection": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"_collection": "esa-cci-lc-netcdf"}, "esa-worldcover": {"_collection": "esa-worldcover"}, "fia": {"_collection": "fia"}, "fws-nwi": {"_collection": "fws-nwi"}, "gap": {"_collection": "gap"}, "gbif": {"_collection": "gbif"}, "gnatsgo-rasters": {"_collection": "gnatsgo-rasters"}, "gnatsgo-tables": {"_collection": "gnatsgo-tables"}, "goes-cmi": {"_collection": "goes-cmi"}, "goes-glm": {"_collection": "goes-glm"}, "gpm-imerg-hhr": {"_collection": "gpm-imerg-hhr"}, "gridmet": {"_collection": "gridmet"}, "hgb": {"_collection": "hgb"}, "hls2-l30": {"_collection": "hls2-l30"}, "hls2-s30": {"_collection": "hls2-s30"}, "hrea": {"_collection": "hrea"}, "io-biodiversity": {"_collection": "io-biodiversity"}, "io-lulc": {"_collection": "io-lulc"}, "io-lulc-9-class": {"_collection": "io-lulc-9-class"}, "io-lulc-annual-v02": {"_collection": "io-lulc-annual-v02"}, "jrc-gsw": {"_collection": "jrc-gsw"}, "kaza-hydroforecast": {"_collection": "kaza-hydroforecast"}, "landsat-c2-l1": {"_collection": "landsat-c2-l1"}, "landsat-c2-l2": {"_collection": "landsat-c2-l2"}, "met-office-global-deterministic-height": {"_collection": "met-office-global-deterministic-height"}, "met-office-global-deterministic-near-surface": {"_collection": "met-office-global-deterministic-near-surface"}, "met-office-global-deterministic-pressure": {"_collection": "met-office-global-deterministic-pressure"}, "met-office-global-deterministic-whole-atmosphere": {"_collection": "met-office-global-deterministic-whole-atmosphere"}, "met-office-uk-deterministic-height": {"_collection": "met-office-uk-deterministic-height"}, "met-office-uk-deterministic-near-surface": {"_collection": "met-office-uk-deterministic-near-surface"}, "met-office-uk-deterministic-pressure": {"_collection": "met-office-uk-deterministic-pressure"}, "met-office-uk-deterministic-whole-atmosphere": {"_collection": "met-office-uk-deterministic-whole-atmosphere"}, "mobi": {"_collection": "mobi"}, "modis-09A1-061": {"_collection": "modis-09A1-061"}, "modis-09Q1-061": {"_collection": "modis-09Q1-061"}, "modis-10A1-061": {"_collection": "modis-10A1-061"}, "modis-10A2-061": {"_collection": "modis-10A2-061"}, "modis-11A1-061": {"_collection": "modis-11A1-061"}, "modis-11A2-061": {"_collection": "modis-11A2-061"}, "modis-13A1-061": {"_collection": "modis-13A1-061"}, "modis-13Q1-061": {"_collection": "modis-13Q1-061"}, "modis-14A1-061": {"_collection": "modis-14A1-061"}, "modis-14A2-061": {"_collection": "modis-14A2-061"}, "modis-15A2H-061": {"_collection": "modis-15A2H-061"}, "modis-15A3H-061": {"_collection": "modis-15A3H-061"}, "modis-16A3GF-061": {"_collection": "modis-16A3GF-061"}, "modis-17A2H-061": {"_collection": "modis-17A2H-061"}, "modis-17A2HGF-061": {"_collection": "modis-17A2HGF-061"}, "modis-17A3HGF-061": {"_collection": "modis-17A3HGF-061"}, "modis-21A2-061": {"_collection": "modis-21A2-061"}, "modis-43A4-061": {"_collection": "modis-43A4-061"}, "modis-64A1-061": {"_collection": "modis-64A1-061"}, "ms-buildings": {"_collection": "ms-buildings"}, "mtbs": {"_collection": "mtbs"}, "naip": {"_collection": "naip"}, "nasa-nex-gddp-cmip6": {"_collection": "nasa-nex-gddp-cmip6"}, "nasadem": {"_collection": "nasadem"}, "noaa-c-cap": {"_collection": "noaa-c-cap"}, "noaa-cdr-ocean-heat-content": {"_collection": "noaa-cdr-ocean-heat-content"}, "noaa-cdr-ocean-heat-content-netcdf": {"_collection": "noaa-cdr-ocean-heat-content-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"_collection": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "noaa-cdr-sea-surface-temperature-whoi": {"_collection": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"_collection": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-climate-normals-gridded": {"_collection": "noaa-climate-normals-gridded"}, "noaa-climate-normals-netcdf": {"_collection": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-tabular": {"_collection": "noaa-climate-normals-tabular"}, "noaa-hrrr": {"_collection": "noaa-hrrr"}, "noaa-mrms-qpe-1h-pass1": {"_collection": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"_collection": "noaa-mrms-qpe-1h-pass2"}, "noaa-mrms-qpe-24h-pass2": {"_collection": "noaa-mrms-qpe-24h-pass2"}, "noaa-nclimgrid-monthly": {"_collection": "noaa-nclimgrid-monthly"}, "nrcan-landcover": {"_collection": "nrcan-landcover"}, "planet-nicfi-analytic": {"_collection": "planet-nicfi-analytic"}, "planet-nicfi-visual": {"_collection": "planet-nicfi-visual"}, "sentinel-1-grd": {"_collection": "sentinel-1-grd"}, "sentinel-1-rtc": {"_collection": "sentinel-1-rtc"}, "sentinel-2-l2a": {"_collection": "sentinel-2-l2a"}, "sentinel-3-olci-lfr-l2-netcdf": {"_collection": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"_collection": "sentinel-3-olci-wfr-l2-netcdf"}, "sentinel-3-slstr-frp-l2-netcdf": {"_collection": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"_collection": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"_collection": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"_collection": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"_collection": "sentinel-3-sral-wat-l2-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"_collection": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"_collection": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"_collection": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"_collection": "sentinel-3-synergy-vg1-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"_collection": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-5p-l2-netcdf": {"_collection": "sentinel-5p-l2-netcdf"}, "terraclimate": {"_collection": "terraclimate"}, "us-census": {"_collection": "us-census"}, "usda-cdl": {"_collection": "usda-cdl"}, "usgs-lcmap-conus-v13": {"_collection": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"_collection": "usgs-lcmap-hawaii-v10"}}}, "usgs_satapi_aws": {"collections_config": {"eo-1-ali-l1gst": {"description": "Level 1GST is terrain corrected and provided in 16-bit radiance values.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2001-05-01T00:00:00.000Z", "2017-03-12T00:00:00.000Z"]]}}, "instruments": ["ALI"], "keywords": ["advanced-land-imager", "ali", "earth-observing-1", "eo-1", "eo-1-ali-l1gst", "l1gst"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "EARTH_OBSERVING_1", "title": "Earth Observing-1 Advanced Land Imager (ALI) Level 1GST (L1GST) Product"}, "eo-1-ali-l1t": {"description": "Level 1T is precision terrain correction by incorporating ground control points from the GLS2005 dataset. Scenes with sufficient ground control data and satisfactory RMS error are processed to Level 1T.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2001-05-01T00:00:00.000Z", "2017-03-12T00:00:00.000Z"]]}}, "instruments": ["ALI"], "keywords": ["advanced-land-imager", "ali", "earth-observing-1", "eo-1", "eo-1-ali-l1t", "l1t"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "EARTH_OBSERVING_1", "title": "Earth Observing-1 Advanced Land Imager (ALI) Level 1T (L1T) Product"}, "eo-1-hyperion-l1gst": {"description": "Level 1GST is terrain corrected and provided in 16-bit radiance values.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2001-05-01T00:00:00.000Z", "2017-03-12T00:00:00.000Z"]]}}, "instruments": ["HYPERION"], "keywords": ["earth-observing-1", "eo-1", "eo-1-hyperion-l1gst", "hyperion", "l1gst"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "EARTH_OBSERVING_1", "title": "Earth Observing-1 Hyperion Level 1GST (L1GST) Product"}, "eo-1-hyperion-l1t": {"description": "Level 1T is precision terrain correction by incorporating ground control points from the GLS2005 dataset. Scenes with sufficient ground control data and satisfactory RMS error are processed to Level 1T.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["2001-05-01T00:00:00.000Z", "2017-03-12T00:00:00.000Z"]]}}, "instruments": ["HYPERION"], "keywords": ["earth-observing-1", "eo-1", "eo-1-hyperion-l1t", "hyperion", "l1t"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "EARTH_OBSERVING_1", "title": "Earth Observing-1 Hyperion Level 1T (L1T) Product"}, "landsat-c2ard-bt": {"description": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2ard-bt", "top-of-atmosphere-brightness-temperature"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2ard-sr": {"description": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2ard-sr", "surface-reflectance"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2ard-st": {"description": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2ard-st", "surface-temperature"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2ard-ta": {"description": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2ard-ta", "top-of-atmosphere-reflectance"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l1": {"description": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1972-07-25T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-1", "landsat-2", "landsat-3", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l1"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-1 Product"}, "landsat-c2l2-sr": {"description": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2-sr", "surface-reflectance"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2l2-st": {"description": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2-st", "surface-temperature"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2l2alb-bt": {"description": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2alb-bt", "top-of-atmosphere-brightness-temperature"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2l2alb-sr": {"description": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2alb-sr", "surface-reflectance"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product"}, "landsat-c2l2alb-st": {"description": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2alb-st", "surface-temperature"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product"}, "landsat-c2l2alb-ta": {"description": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l2alb-ta", "top-of-atmosphere-reflectance"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l3-ba": {"description": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "burned-area", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l3-ba"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product"}, "landsat-c2l3-dswe": {"description": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "dynamic-surface-water-extent-", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l3-dswe"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product"}, "landsat-c2l3-fsca": {"description": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "extent": {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [["1982-08-22T00:00:00.000Z", null]]}}, "keywords": ["analysis-ready-data", "fractional-snow-covered-area", "landsat", "landsat-4", "landsat-5", "landsat-7", "landsat-8", "landsat-9", "landsat-c2l3-fsca"], "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "platform": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product"}}, "providers_config": {"eo-1-ali-l1gst": {"_collection": "eo-1-ali-l1gst"}, "eo-1-ali-l1t": {"_collection": "eo-1-ali-l1t"}, "eo-1-hyperion-l1gst": {"_collection": "eo-1-hyperion-l1gst"}, "eo-1-hyperion-l1t": {"_collection": "eo-1-hyperion-l1t"}, "landsat-c2ard-bt": {"_collection": "landsat-c2ard-bt"}, "landsat-c2ard-sr": {"_collection": "landsat-c2ard-sr"}, "landsat-c2ard-st": {"_collection": "landsat-c2ard-st"}, "landsat-c2ard-ta": {"_collection": "landsat-c2ard-ta"}, "landsat-c2l1": {"_collection": "landsat-c2l1"}, "landsat-c2l2-sr": {"_collection": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"_collection": "landsat-c2l2-st"}, "landsat-c2l2alb-bt": {"_collection": "landsat-c2l2alb-bt"}, "landsat-c2l2alb-sr": {"_collection": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-st": {"_collection": "landsat-c2l2alb-st"}, "landsat-c2l2alb-ta": {"_collection": "landsat-c2l2alb-ta"}, "landsat-c2l3-ba": {"_collection": "landsat-c2l3-ba"}, "landsat-c2l3-dswe": {"_collection": "landsat-c2l3-dswe"}, "landsat-c2l3-fsca": {"_collection": "landsat-c2l3-fsca"}}}, "wekeo_cmems": {"collections_config": {}, "providers_config": {}}}