World Ocean Atlas 2018 (WOA18) is a set of objectively analyzed (one degree grid and quarter degree grid) climatological fields of in situ temperature, salinity, dissolved oxygen, Apparent Oxygen Utilization (AOU), percent oxygen saturation, phosphate, silicate, and nitrate at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. Quarter degree fields are for temperature and salinity only. It also includes associated statistical fields of observed oceanographic profile data interpolated to standard depth levels on quarter degree, one degree, and five degree grids. Temperature and salinity fields are available for six decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, and 2005-2017) an average of all decades representing the period 1955-2017, as well as a thirty year "climate normal" period 1981-2010. Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled data 1960-2017, nutrient fields using all quality controlled data from the entire sampling period 1878-2017. This accession is a product generated by the National Centers for Environmental Information's (NCEI) Ocean Climate Laboratory Team. The analyses are derived from the NCEI World Ocean Database 2018.

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.

Data record (2004-2012) of level 3 hourly sub-skin Sea Surface Temperature derived from Meteosat at 0° longitude, covering 60S-60N and 60W-60E and re-projected on a 0.05° regular grid, in GHRSST compliant netCDF format. The satellite input data come from the imager SEVIRI on MSG satellites (Meteosat-8 and Meteosat-9). SST is retrieved from SEVIRI infrared channels (10.8 and 12.0 µm) using a multispectral algorithm and the cloud mask from CM SAF. NWP outputs (temperature and humidity profiles), OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The product format is compliant with the GHRSST Data Specification (GDS) version 2. Users are advised to use data only with quality levels 3,4 and 5.

NOAA High-resolution Blended Analysis of Daily SST and Ice. Data is from Sep 1981 and is on a 1/4 deg global grid.

Climatology of near-inertial currents. Global climatology of near-inertial current characteristics from satellite-tracked drifters. Data analysed : 8500 near-surface satellite-tracked drifter trajectories with drogues attached at 15 m depth.

NOAA STAR produces two lines of gridded 0.02deg super-collated L3S LEO datasets from Low Earth Orbiting (LEO) satellites, one from the NOAA afternoon JPSS (L3S_LEO_PM) and the other from the EUMETSAT mid-morning Metop-FG (L3S_LEO_AM). The L3S_LEO_PM is derived from JPSS satellites (in v2.80, NPP and N20) with VIIRS sensor onboard (0.75km/nadir). The L3S_LEO_PM dataset is produced by aggregating L3U datasets from two JPSS satellites ( https://doi.org/10.5067/GHVRS-3UO28 and https://doi.org/10.5067/GHV20-3UO28 ) and covers from Feb 2012-present. The L3S-LEO-PM data are reported in two files per 24hr interval, one daytime and one nighttime (nominal JPSS local equator crossing times around 01:30/13:30). Data is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near-Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency and then replaced by the Delayed Mode files about 2 months later, with identical file names. In addition to SST, the L3S-LEO files report the location and intensity of thermal fronts. The NRT/DM data are seamlessly stitched with the full-mission Reanalysis (RAN). The ACSPO L3S products are monitored and validated against in situ data in the NOAA iQuam system ( https://www.star.nesdis.noaa.gov/socd/sst/iquam ) in the NOAA SQUAM system ( https://www.star.nesdis.noaa.gov/socd/sst/squam ). Quality of SST imagery, clear-sky mask and thermal fronts is evaluated in the NOAA ARMS system ( https://www.star.nesdis.noaa.gov/socd/sst/arms ). NOAA plans to include data from other afternoon platforms and sensors, such as N21 and Aqua MODIS, into the future releases of the L3S_LEO_PM.

Barystatic and manometric sea level changes represent the mass component of sea level changes at global and regional scales, respectively. Barystatic sea level changes are estimated using the sea level budget (SLB) approach combining satellite altimetry with in-situ measurements of the seawater temperature and salinity. This SLB approach is adapted from Barnoud et al., (2023; https://doi.org/10.5194/os-19-321-2023). Manometric sea level variations and their uncertainties are not provided for the SLB approach, due to the high uncertainties associated with the drift of the halosteric component (e.g., Bouih et al., 2025; https://doi.org/10.5194/egusphere-egu24-16813).

This Level 2 product provides marine reflectances from the VENµS mission, processed with the Polymer algorithm, on a subset of sites with coastal or inland areas. VENµS (Vegetation and Environment monitoring on a New Micro-Satellite) is a Franco-Israeli satellite launched in 2017, dedicated to the fine and regular monitoring of terrestrial vegetation, in particular cultivated areas, forests, protected natural areas, etc. The images acquired in 12 spectral bands by a camera provided by CNES, on a selection of about one hundred scientific sites spread over the planet, are of high spatial (5 m) and temporal resolution. The lifetime of the VENµS satellite has been divided into two phases: a first phase VM1 at an altitude of 720 km with a 2-day revisit, a native spatial resolution of 5.3 m and a swath of 27.6 km from August 2017 to November 2020, and a second phase VM5 at an altitude of 560 km with a daily revisit, a native spatial resolution of 4.1 m and a swath of 21.3 km from March 2022 to July 2024. VENµS is the first sensor on board an orbiting satellite to combine such revisit frequency and spatial finesse for vegetation monitoring. A subset of sites with coastal areas or inland waters have been identified to generate Level 2 data dedicated to marine reflectance. The geographical areas covered are given through a kmz file, see below to download it. This Level 2 data product has been processed using the Polymer algorithm developed by Hygeos (https://hygeos.com/en/polymer/) and provides marine reflectances for the VENµS bands from 420 to 865 nm. These reflectances, without units, include a bidirectional normalization for the Sun at nadir and the observer at nadir. VENµS data products (Level-1, Level-2 and Level-3) are primarily generated with the MAJA algorithm, further information can be found on THEIA website: https://www.theia-land.fr/en/product/venus/

Barystatic and manometric sea level changes represent the mass component of sea level changes at global and regional scales, respectively. Barystatic and manometric sea level changes are estimated using the satellite gravimetry measurements from the GRACE and GRACE-FO missions. Barystatic sea level changes are due to freshwater exchanges with the continents, including ice mass changes from Greenland, Antarctica and continental glaciers, as well as freshwater fluxes from continental areas (i.e. large river basins such as the Amazon or Mississippi). Manometric sea level changes are due to several processes, including the atmosphere - ocean circulation, sea level fingerprints or global water cycle. Climate modes, such as the El Nino Southern Oscillation, Arctic Oscillation, North Atlantic Oscillation or Southern Annular Mode, have also been shown to significantly influence manometric sea level variations (e.g., Pfeffer et al., 2022; https://doi.org/10.1007/s00382-021-05953-z). Two products are distributed: - Barystatic sea level changes from satellite gravimetry with uncertainties at 1-sigma: monthly time series, - Manometric sea level changes from satellite gravimetry with uncertainties at 1-sigma: monthly grids with 1x1 degree. The GRACE and GRACE Follow-On missions monitor the time-variations in the gravity field almost continuously since 2002. Numerous centers distribute time-lapse solutions of the Earth’s gravitational potential, delivered as Stokes coefficients, known as Level-2 solutions. The Level-2 solutions need to be corrected for several geophysical effects and instrumental errors, converted into surface mass anomalies and projected onto the ellipsoid. The resulting gridded surface mass anomalies with appropriate corrections applied are referred to as Level-3 solutions. Several sources of errors affect the solutions of Level-2 and-3, imposed by the satellite configuration, instrumental errors and uncertainties in the geophysical corrections used to process the measurements. We use the ensemble approach of Blazquez et al., (2018; https://doi.org/10.1093/gji/ggy293), to robustly estimate the manometric and barystatic sea level changes and their uncertainties.

The Level-2P (L2P) products are pre-operational homogenized, mono-mission satellite altimetry datasets generated by the DUACS production system. They provide along‑track SLA data together with the full set of standardised geophysical and environmental corrections (e.g., tide‑free sea level, inverse barometer, sea‑state bias, atmospheric corrections). The L2P SLA datasets are the primary input for the higher‑order DUACS processing chain that yields the operational sea‑level (Level-3 and Level-4) products distributed by the Copernicus Marine Service (CMS) and the Copernicus Climate Change Service (C3S). **Processing Timeliness:** - **Near-Real-Time (NRT)**: Rapid delivery windows (under 3h – up to 24h for the newest passes) for operational marine forecasting. - **Short Time Critical (STC)**: Intermediate latency (24h – 3 days) streams optimized for quick-turnaround cal/val and monitoring. - **Non-Time Critical (NTC)**: Science‑quality dataset (30 days – up to 90 days) with the most stable baseline corrections; foundation for long‑term climate analyses. The L2P SLA NRT/STC 20Hz data products version 4.0 (DUACS DT 2024 baseline) are available for the following missions: Jason-3, Sentinel‑6 Michael Freilich (MF) and SWOT. The L2P SLA NTC 1Hz data products version 4.0 (DUACS DT 2024 baseline) are available for the following missions: ERS‑1, TOPEX/Poseidon, ERS‑2, Geosat‑Follow‑On, Jason‑1, Envisat, Jason‑2, CryoSat‑2, Haiyang‑2A, SARAL/AltiKa, Jason‑3, Sentinel‑3A, Sentinel‑3B, Sentinel‑6 Michael Freilich (MF) and SWOT.