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End-to-End Learning of Variational Interpolation Schemes for Satellite-Derived SSH Data

Maxime Beauchamp 1, 2 Mohamed Mahmoud Amar 1 Quentin Febvre 1, 2 Ronan Fablet 1, 2 
2 Lab-STICC_OSE - Equipe Observations Signal & Environnement
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : The reconstruction of better-resolved sea surface currents is a key challenge in space oceanography. Besides the upcoming SWOT wide-swath altimeter mission, new algorithms are explore to produce improved gap-free gridded products. Based on the recent development of a generic end-to-end deep learning scheme for inverse problems backed on a variational formulation, we investigate how this framework applies to the space-time interpolation of satellite-derived SSH fields. We consider different parameterization of the proposed end-toend learning scheme, especially regarding the embedded variational solver. Using an Observing System Simulation Experiment based on high-resolution numerical simulations in the Gulf Stream region, we show that the later may significantly outperform the state-of-the-art, including DUACS optimal interpolation product, when jointly considering nadir along-track altimeter data and upcoming SWOT wide-swath data.
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Submitted on : Friday, August 12, 2022 - 11:13:32 AM
Last modification on : Saturday, August 20, 2022 - 3:50:11 AM


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Maxime Beauchamp, Mohamed Mahmoud Amar, Quentin Febvre, Ronan Fablet. End-to-End Learning of Variational Interpolation Schemes for Satellite-Derived SSH Data. IGARSS 2021: IEEE International Geoscience and Remote Sensing Symposium, Jul 2021, Brussels, France. pp.7418-7421, ⟨10.1109/IGARSS47720.2021.9554800⟩. ⟨hal-03750401⟩



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