The mean sea surface (MSS) is an important field in physical oceanography, geophysics, and geodesy. In principle, it corresponds to the time-averaged height of the ocean surface. Auxiliary product : mean sea profile above a reference ellipsoid (T/P or WSG84). This surface is available on a regular grid (1/60°x1/60°, 1 minute). The mean sea surface MSS_CNES_CLS2025 (Charayon et al., in prep) has been computed using a 29-year [1993-2021] period of altimetric data. It was built exploring the use of SWOT KaRIn data to propose a new generation MSS able to better represent small wavelengths gapping state-of-the-art nadir-based MSS. Long wavelengths come from a MSS draft chosen to be the Hybrid 23 MSS (https://doi.org/10.24400/527896/a01-2024.002 ) low pass filtered at 20km since this nadir-based MSS model already captured well long wavelengths. The wavelengths shorter than 100km are improved thanks to a SWOT-KaRIn mean profile anomaly to the MSS draft. This mean profile anomaly is built with: - The SWOT-KaRIn science (L3 v3.0 Expert, cycles 1 to 31 included except cycle 17, https://doi.org/10.24400/527896/a01-2023.018) dataset - The experimental multimission gridded L4 sea level heights and velocities with SWOT using MIOST (https://doi.org/10.24400/527896/a01-2025.001 ). - In SWOT diamonds, since KaRIn data are not available, we used another innovation only based on DT2024 1Hz CryoSat2 and AltiKa SSHA data (https://doi.org/10.48670/moi-00146), period from 2010 to 2025, SSHA data relative to MSS Hybrid 23 low pass filtered at 5km. To build this nadir-based innovation, we used the static part of the mapping from MIOST method.

This dataset provides an estimation of the climate feedback parameter and the climate sensitivity developed in collaboration by Magellium and LEGOS. It is based on the publication of Meyssignac et al., (2023, https://doi.org/10.1038/s43247-023-00887-2) . The product is a crucial study on Effective Climate Sensitivity (ECS), focusing on the time variation of the climate feedback parameter (λ). Understanding λ is fundamental as it quantifies the Earth’s radiative response to changes in global average temperature. A less negative λ directly implies a greater climate sensitivity to greenhouse gas concentrations. The product addresses the high uncertainty in ECS by providing an observational estimate of λ temporal variations since 1970. Estimates are derived from the global energy balance equation, regressing the radiative response against temperature over periods longer than 25 years. Users are primarily interested in the time series of λ and its associated uncertainties. The input data for computing λ is provided in a secondary "extended product" for transparency, allowing users to rebuild the indicators. The final product, available as a NetCDF file, can be downloaded in open access and is licensed for any project or study.

Operational altimetry processing traditionally relies on a “frozen sea” assumption, neglecting the influence of ocean dynamics on Doppler frequencies and introducing biases in estimated sea-state parameters. This dataset is based on an advanced SAR (Delay-Doppler) processing approach that overcomes this limitation by explicitly accounting for ocean surface dynamics. The methodology incorporates enhanced SAR models that jointly retrieve the standard deviation of vertical wave orbital velocities and the along-track component of the geophysical Doppler vector. These quantities are estimated through full two-dimensional retracking of unfocused SAR Delay-Doppler maps (stacks), rather than through the conventional summed Doppler beam approach used in operational processing. The processing has been implemented within the Sentinel Processing Prototype (SPP), leading to the generation of two six-month Sentinel-6A (S6A) data series covering January–June 2022 and January–June 2024. Validation results show that the dataset is fully consistent with conventional altimetry products in terms of sea surface height (SSH) and significant wave height (SWH), while providing reduced noise levels and improved along-track resolution. Key added-value parameters and improvements include: • Vertical wave velocity statistics: The dataset characterizes azimuth smearing induced by vertical wave motion, enabling the retrieval of the standard deviation of vertical wave velocity. This parameter is directly related to the mean zero up-crossing wave period and refines the estimation of sea-state conditions. Its inclusion reduces sea-state-dependent biases in SWH and shows strong agreement with ERA5 wave model outputs without requiring external corrections. • Geophysical Doppler contribution: The along-track component of the geophysical Doppler vector is estimated, providing information linked to wind direction and intensity, as well as surface currents (including contributions such as Stokes drift). Accounting for this term significantly reduces wind-dependent SSH biases, particularly between ascending and descending passes, while preserving large-scale spectral content. This dataset constitutes a demonstration product designed for the scientific community, showcasing the potential of advanced SAR retracking techniques. It delivers enhanced-resolution altimetry measurements together with improved SSH and SWH estimates, wave-period-related diagnostics derived from vertical velocity, and geophysical Doppler information. These features open new perspectives for coastal ocean applications, surface current studies, and swell characterization.

The SWOT KaRIn Level-3 Wind Wave product (L3_LR_WIND_WAVE) is an innovative product derived from the Unsmoothed L3_LR_SSH product (https://doi.org/10.24400/527896/A01-2024.003), which is based on the algorithm presented by Ardhuin et al. (2024; https://doi.org/10.1029/2024GL109658). L3_LR_WIND_WAVE takes advantage of the KaRIn Low Rate (LR) chain's ability to resolve waves with wavelengths greater than 500 meters (approximately 18 seconds) and provides detailed information on the characteristics of these wave regimes. This includes significant wave height (SWH), dominant wavelength, and wave propagation direction. These regimes are associated with long-period swells and extreme events that play a critical role in ocean dynamics, coastal processes, and maritime operations. The SWOT L3_LR_WIND_WAVE product is organized into two subproducts, "Light" and "Extended". The L2_LR_SSH "Light" product is described in this metadata sheet. The "Light" L3_LR_WIND_WAVE (also known as the "lightweight" product) includes the SWOT L3_LR_SSH 250-m SSHA spectrum, corrected for instrumental effects and expressed in both Cartesian and polar coordinates. It also includes the swell partition of the spectrum and the wave parameters integrated over this partition, for both the WW3 model and the KaRIn model (significant wave height, wavelength, and direction). The "Extended" L3_LR_WIND_WAVE includes the aforementioned variables plus the WW3 spectrum in the same frequency grid as the KaRIn spectrum and the KaRIn transfer functions used for correction, as well as some parameters derived from KaRIn observations (e.g., coherence, mean backscatter).

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.

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/

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.

The CDR-derived Wet Tropospheric Correction (WTC) Product V2 is generated from the Level-2+ along-track altimetry products version 2024 (L2P 2024) distributed by AVISO+ (www.aviso.altimetry.fr). It provides a long-term, homogenized estimation of the wet tropospheric correction based on Climate Data Records (CDRs) of atmospheric water vapour combined with high frequencies MWR data. Two independent CDRs datasets are used: - REMSS V7R2 (coverage until 2022) https://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product/ - HOAPS V5 precursor CDR from EUMETSAT CM SAF (coverage until 2020) HOAPS V4/V5 data available via https://wui.cmsaf.eu Note: the HOAPS V5 precursor is not yet an official CM SAF product; full validation and public release are pending. The MWR/CDR WTC V2 estimates is derived using spatially varying but temporally constant polynomial coefficients (ai). 1. WTC V2 – Along-track L2P Product Data format: The WTC V2 product is delivered in Level-2+ (L2P) format, along the satellite ground track. Each mission is distributed as a compressed archive (.tar.gz) containing one NetCDF4 CF-1.8 file per mission cycle. Archive naming convention: <mission>_WTC_from_WV_CDR_<version>.tar.gz mission: TP (TOPEX/Poseidon), J1, J2, J3 version: product version (currently V2) File naming convention inside archives: <mission>_C<cycle>.nc cycle: 4-digit cycle index (e.g., C0001) Each NetCDF file contains: 1/ Along-track WTC estimate; 2/ Ancillary information; 3/ Space–time coordinates 2. WTC CDR Uncertainties – Gridded Product: A complementary product is provided, delivering regional trend estimates and associated uncertainties from the WTC Climate Data Record. The uncertainty product is distributed as a single NetCDF4 file: wtc_trend_uncertainties.nc . This file contains global gridded fields of WTC CDR trend and uncertainty parameters. Product content: This is the first dedicated version providing both: WTC CDR (HOAPS) linear trends, and Uncertainty estimates on these trends. Uncertainties are expressed as 1-sigma confidence intervals, and propagated using the methodology described in Section 2.3 of the Product User Manual. The product includes: - Total uncertainty on the WTC trend, propagated from all identified uncertainty sources in the WTC–TCWV regression. - Individual contributions of uncertainty sources (Uncertainties on regression coefficients: a0, a1 and their standard deviations; Uncertainties inherited from the HOAPS TCWV CDR) These fields enable users to assess the relative importance of each uncertainty component and recompute uncertainty propagation with alternative methods. Included regression input variables: To ensure transparency and reproducibility, the product provides: 1/ regression coefficients a0, a1; 2/ their associated uncertainties (std of a0, std of a1); 3/additional diagnostic fields required to recompute uncertainties if needed.

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.

Monomission altimeter satellite along-track sea surface heights computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently OSTM/Jason-2. The sla is computed with an optimal and centered computation time window (6 weeks before and after the date). Two kinds of datasets are proposed: filtered (nominal dataset) and unfiltered.