From 1 - 2 / 2
  • This dataset provides global surface winds and pressure fields on a 0.25° resolution horizontal grid with hourly frequency, based on the global ERA5 reanalysis but with enhanced realistic TCs, built from a parametric wind formulation fitted to SAR high-resolution observations. TC wind structures in ERA5 are indeed known to be strongly biased. The methodology, developed in Herry et al. (2025) and applied here, is to replace ERA5 underestimated winds in TCs by the parametric wind formulation of Wood et al. (2013) with optimized parameters (fitted to SAR data), while keeping ERA5 wind field elsewhere. The wind profile and the transition area between the parametric and ERA5 winds are adjusted for each TC, according to its intensity and size. This blended product manages to represent a variety of realistic TC structures, and ensure an asymmetry associated to the synoptic flow for each case.

  • 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.