The Sentinel-6 Level-2P skewness products was developed to estimate the skewness from Sentinel-6 LR (Low Resolution Mode) and HR (High Resolution Mode) acquisitions. That demonstration product is generated by different retracking processes, provides an initial estimation of such a phenomenon and allows a finer description of the sea state.

These gridded products are produced from the following upstream data: - for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A/B, Sentinel-6 MF, SWOT Nadir => NRT (Near-Real-Time) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (http://marine.copernicus.eu/ ). The gridded product is based on near-real-time (NRT) Level-3 Nadir datasets for the period from July 7, 2025, to December 31, 2025. => MY (Multi-Year) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00146 ) delivered by the Copernicus Marine Service (CMEMS, http://marine.copernicus.eu/ ). The gridded product is based on MY Level-3 Nadir datasets for the period from March 28, 2023, to July 6, 2025. - for SWOT KaRIn : the L3_LR_SSH Expert v3.0 product distributed by AVISO (DOI: https://doi.org/10.24400/527896/A01-2023.018) from March 28, 2023 to December 31, 2025. One mapping algorithm is proposed: the MIOST approach which provides which provides global Sea Surface Height (SSH) solutions. The MIOST method is capable of accounting for various modes of ocean surface topography variability (e.g., geostrophic, barotropic, equatorial wave dynamics) by constructing multiple independent components within a predefined covariance model.

These gridded products are produced from the along-track (or Level-3) SEA LEVEL products (DOI: doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (CMEMS, marine.copernicus.eu) for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A/B, Sentinel-6 MF, SWOT nadir, and SWOT Level-3 KaRIn sea level products (DOI: https://doi.org/10.24400/527896/A01-2023.018). Three mapping algorithms are proposed: MIOST, 4DvarNET, 4DvarQG: - the MIOST approach which give the global SSH solutions: the MIOST method is able of accounting for various modes of variability of the ocean surface topography (e.g., geostrophic, barotrope, equatorial waves dynamic …) by constructing several independent components within an assumed covariance model. - the 4DvarNET approach for the regional SSH solutions: the 4DvarNET mapping algorithm is a data-driven approach combining a data assimilation scheme associated with a deep learning framework. - the 4DvarQG approach for the regional SSH solutions: the 4DvarQG mapping technique integrates a 4-Dimensional variational (4DVAR) scheme with a Quasi-Geostrophic (QG) model.

'''Short 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 '''DOI (product) :''' https://doi.org/10.48670/mds-00337

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global ocean, the ESA Ocean Colour CCI surface Chlorophyll (mg m-3, 4 km resolution) using the OC-CCI recommended chlorophyll algorithm is made available in CMEMS format. L3 products are daily files, while the L4 are monthly composites. ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). Ocean 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. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. A detailed description of calibration & validation is given in the relevant QUID, associated validation reports and quality documentation. '''Processing information:''' ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). '''Description of observation methods/instruments:''' Ocean 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. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''Suitability, Expected type of users / uses:''' This 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. '''DOI (product) :''' https://doi.org/10.48670/moi-00101

'''Short 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. A 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. '''DOI (product) :''' https://doi.org/10.48670/mds-00378

'''Short 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. '''DOI (product) :''' https://doi.org/10.48670/mds-00344

'''Short 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 (Høyer and She, 2007; Høyer 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’s (NIC) ice charts, Swedish Meteorological and Hydrological Institute (SHMI) and Finnish Meteorological Institute’s (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. The 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). '''DOI (product) :''' https://doi.org/10.48670/moi-00169

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The KD490 product identifies the turbidity of the water column, i.e., how visible light in the blue-green region of the spectrum penetrates within the water column. It is directly related to the presence of scattering particles in the water column. This product is derived from OLCI and remapped at nominal 300m spatial resolution using cylindrical equirectangular projection. '''Description of observation methods/instruments:''' Ocean 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. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of in water absorption parameters can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality documentation. '''DOI (product) :''' https://doi.org/10.48670/moi-00078

'''This product has been archived''' For operational and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global Ocean - the OSTIA diurnal skin Sea Surface Temperature product provides daily gap-free maps of: *Hourly mean skin Sea Surface Temperature at 0.25° x 0.25° horizontal resolution, using in-situ and satellite data from infra-red radiometers. The Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the Met Office. A 1/4° (approx. 28 km) hourly analysis of skin Sea Surface temperature (SST) is produced daily for the global ocean. The skin temperature of the ocean is the temperature measured by satellite infra-red radiometers and can experience a large diurnal cycle. The skin SST L4 product is created by combining: 1. the OSTIA foundation SST analysis which uses in-situ and satellite observations; 2. the OSTIA diurnal warm layer analysis which uses satellite observations; and 3. a cool skin model. OSTIA uses satellite data provided by the GHRSST project. '''DOI (product) :''' https://doi.org/10.48670/moi-00167