The MIOST (Multiscale Interpolation Ocean Science Topography) experimental altimeter product provides grids at delayed-time, at global scale, 1/10° spatial resolution, the sea surface height (MSLA and MADT) as well as the geostrophic currents, resulting from specific processing. Use for regional studies, ocean variability (mesoscale circulation,...).

'''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:''' 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° (0.0625°) 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). '''DOI (product) :''' https://doi.org/10.48670/moi-00170

'''Short 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 '''DOI (product) :''' https://doi.org/10.48670/moi-00151

'''Short 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 wave length). 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. '''DOI (product) :''' https://doi.org/10.48670/moi-00144

'''DEFINITION''' Net sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79°N, 20°W - 10°E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80°N, 27°E - 60°E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60°E, 76°N - 80°N (positive westward). For further details, see Lien et al. (2021). '''CONTEXT''' The 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). '''CMEMS KEY FINDINGS''' The 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. Moreover, there is a significant, negative trend in the volume transport over the last two and a half decades, explained partly by reduced area transport. 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). '''DOI (product):''' https://doi.org/10.48670/moi-00192

''' Short 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° (0.0625°) 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). '''DOI (product) :''' https://doi.org/10.48670/moi-00157

'''Short 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 '''DOI (product) :''' https://doi.org/10.48670/moi-00138

'''Short 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 1982-2020 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 (2017-2020). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth. '''DOI (product) :''' https://doi.org/10.48670/moi-00153

'''Short 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 µg/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). To limit file size the products are provided in tiles of 600x800 km². 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-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. '''Processing information:''' The 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: * 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. * 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. * 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. * 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. * 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. * 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. '''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. '''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 and in CMEMS-BGP_HR-QUID-009-201_to_212. '''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: ''' *cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01 '''Files format:''' *netCDF-4, CF-1.7 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00108