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.

Archive de toutes les données de température de surface (SST) satellite produites dans le cadre du projet international GHRSST. Ifremer est un GDAC pour ces données, miroir du GDAC NASA/JPL. Ces données sont utilisées pour la génération de produits multi-capteurs (CMEMS, Medspiration) mais également dans le cadre d'un grand nombre d'études ou projets nécessitant l'utilisation de mesures de SST. L'archive regroupe plusieurs jeux de données provenant de différents satellite ainsi que des données in situ de référence pour leur validation. Elle est mise à jour en temps quasi-réel depuis 10 ans, avec service de diffusion opérationnelle associé (FTP et HTTP). Une fiche sextant (issue du catalogue CERSAT) sera fournie pour chaque dataset dans cette archive.

387 points were surveyed with a SP80 DGPS by Maxime Paschal as part of the La Rochelle Zero Carbon Territory (LRTZC) project on 26/05/23. At each point, the type of vegetation was specified.

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (eg become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (eg a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’, spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is done for each of the other datasets, producing a dataset of matchups. About 35 million in situ datapoints were then matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of 287,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. This dataset was funded by ESA Satellite Oceanographic Datasets for Acidification (OceanSODA) project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry. **This version is now superseded by the version 4 with higher spatial and temporal resolution**

Satellite altimeters routinely supply sea surface height (SSH) measurements which are key observations to monitor ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in the signal-to-noise ratio, making it very challenging to fully exploit available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinctive methodology emerged to be systematically applied in operational products. To best cope with this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination in the along-track SSH signals and more innovative and adapted noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. Here demonstrated, a fully data-driven approach is developed and applied to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is now found to best resolve the distribution of the sea surface height variability in the mesoscale 30-120 km wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal to noise ratio, but also for uncertainties in the denoising process, which assumes that SLA variability results in part from a stochastic process. Here, measurements from the Jason-3, Sentinel-3 A and SARAL/AltiKa altimeters are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. Anticipating data from the upcoming Surface Water and Ocean Topography (SWOT) mission, these denoised SLA measurements for three reference altimeter missions already yield valuable opportunities to assess global small mesoscale kinetic energy distributions. This dataset was developed within the Ocean Surface Topography Science Team (OSTST) activities. A grant was awarded to the SASSA (Satellite Altimeter Short-scale Signals Analysis) project by the TOSCA board in the framework of the CNES/EUMETSAT call CNES-DSP/OT 12-2118. Altimeter data were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and by the Sea State Climate Change Initiative (CCI) project.

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.

Level 2 skin Sea Surface Temperature derived from IASI on Metop-B, global and provided in full-resolution swath (12 km at nadir to 40 km), in GHRSST compliant netCDF format. SST is retrieved using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. 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.

This dataset provides Level 4 total current including geostrophy and a data-driven approach for Ekman and near-inertial current, based on a convolution between drifter observation and wind history, to fit empirically a complex and time-lag dependant transfert function between ERA5 wind stress and current The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by Datlas and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

This dataset provides detections of fronts derived from low resolution optimally interpolated remote sensing microwave SST L4 from REMSS over North Atlantic region. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by OceanDataLab and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents Metop-A North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. 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.