'''Short 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. '''DOI (product) :''' https://doi.org/10.48670/mds-00342

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean, the L4 multi-sensor daily satellite product is a 2km horizontal resolution subskin sea surface temperature analysis. This SST analysis is run by Meteo France CMS and is built using the European Ocean L3S products originating from bias-corrected European Ocean L3C mono-sensor products at 0.02 degrees resolution. This analysis uses the analysis of the previous day at the same time as first guess field. '''DOI (product) :''' https://doi.org/10.48670/moi-00161

'''Short 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. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''Short 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° 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) '''DOI (product) :''' https://doi.org/10.48670/moi-00310

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes Level-4 product including the daily interpolated chlorophyll field with no data voids starting from the multi-sensor (MODIS-Aqua, NOAA-20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and the monthly averaged chlorophyll concentration for the multi-sensor (at 1 km resolution) and Sentinel-OLCI Level-3 (at 300m resolution). Chlorophyll field are obtained by means of the Mediterranean regional algorithms: an updated version of the MedOC4 (Case 1 waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 waters, Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum with the average water type spectral signature from in situ measurements for both water types. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where pure Case II spectra are selected using a k-mean cluster analysis (Melin et al., 2015). Merging of Case I and Case II information is performed estimating the Mahalanobis distance between the observed and reference spectra and using it as weight for the final merged value. The interpolated gap-free Level-4 Chl concentration is estimated by means of a modified version of the DINEOF algorithm by GOS (Volpe et al., 2018). DINEOF is an iterative procedure in which EOF are used to reconstruct the entire field domain. As first guess, it uses the SeaWiFS-derived daily climatological values at missing pixels and satellite observations at valid pixels. The other Level-4 dataset is the time averages of the L3 fields and includes the standard deviation and the number of observations in the monthly period of integration. '''Processing information:''' Multi-sensor products are constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Hence, the single sensor OLCI data set is also distributed after BRDF correction. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution (300m for Sentinel3A-OLCI) using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll, kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. Level-4 includes both monthly time averages and the daily-interpolated fields. Time averages are computed on the delayed-time data. The interpolated product starts from the L3 products at 1 km resolution. At the first iteration, DINEOF procedure uses, as first guess for each of the missing pixels the relative daily climatological pixel. A procedure to smooth out spurious spatial gradients is applied to the daily merged image (observation and climatology). From the second iteration, the procedure uses, as input for the next one, the field obtained by the EOF calculation, using only a number of modes: that is, at the second round, only the first two modes, at the third only the first three, and so on. At each iteration, the same smoothing procedure is applied between EOF output and initial observations. The procedure stops when the variance explained by the current EOF mode exceeds that of noise. '''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 pre+D2sence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal. '''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. '''Dataset names:''' *dataset-oc-med-chl-multi-l4-chl_1km_monthly-rt-v02 *dataset-oc-med-chl-multi-l4-interp_1km_daily-rt-v02 *dataset-oc-med-chl-olci-l4-chl_300m_monthly-rt-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00113

'''This product has been archived''' '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. 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. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

'''Short 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. '''DOI (product) :''' https://doi.org/10.48670/moi-00134

'''Short description:''' Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. All the missions are homogenized with respect to a reference mission (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmosphic Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs This product is processed by the DUACS multimission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from all altimeter missions: Jason-3, Sentinel-3A, HY-2A, Saral/AltiKa, Cryosat-2, Jason-2, Jason-1, T/P, ENVISAT, GFO, ERS1/2. It provides a consistent and homogeneous catalogue of products for varied applications, both for near real time applications and offline studies. To produce maps of SLA (Sea Level Anomalies) in near-real time, the system exploits the most recent datasets available based on the enhanced OGDR+IGDR production. The system acquires and then synchronizes altimeter data and auxiliary data; each mission is homogenized using the same models and corrections. The Input Data Quality Control checks that the system uses the best altimeter data. The multi-mission cross-calibration process removes any residual orbit error, or long wavelength error (LWE), as well as large scale biases and discrepancies between various data flows; all altimeter fields are interpolated at crossover locations and dates. After a repeat-track analysis, a mean profile, which is peculiar to each mission, or a Mean Sea Surface (MSS) (when the orbit is non repetitive) is subtracted to compute sea level anomaly. The MSS is available via the Aviso+ dissemination (http://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mss.html [http://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mss.html]). Data are then cross validated, filtered from residual noise and small scale signals, and finally sub-sampled (sla_filtered variable). The ADT (Absolute Dynamic Topography, adt_filtered variable) can computed as follows: adt_filtered=sla_filtered+MDT where MDT. The Mean Dynamic Topography distributed by Aviso+ (http://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mdt.html [http://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mdt.html]). '''Associated products:''' A time invariant product [http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_NOISE_L4_NRT_OBSERVATIONS_008_032] 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.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the European Ocean- The L3 multi-sensor (supercollated) product is built from bias-corrected L3 mono-sensor (collated) products at the resolution 0.02 degrees. If the native collated resolution is N and N < 0.02 the change (degradation) of resolution is done by averaging the best quality data. If N > 0.02 the collated data are associated to the nearest neighbour without interpolation nor artificial increase of the resolution. A synthesis of the bias-corrected L3 mono-sensor (collated) files remapped at resolution R is done through a selection of data based on the following hierarchy: AVHRR_METOP_B, VIIRS_NPP, SLSTRA, SEVIRI, AVHRRL-19, MODIS_A, MODIS_T, AMSR2. This hierarchy can be changed in time depending on the health of each sensor. '''DOI (product) :''' https://doi.org/10.48670/moi-00163

'''Short 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. '''DOI (product) :''' https://doi.org/10.48670/moi-00154