Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations

• Published in: Remote Sensing
• Main Authors: Beauchamp, Maxime, Fablet, Ronan, Ubelmann, Clément, Ballarotta, Maxime, Chapron, Bertrand
• Other Authors: Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), OceanNext, Collecte Localisation Satellites (CLS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: Data-driven and learning-based approaches; Interpolation; Benchmarking; Nadir & SWOT altimetric satellite data; Sea surface height (SSH); Nadir & 23; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; North Atlantic
• Online Access: https://imt-atlantique.hal.science/hal-02931892; https://imt-atlantique.hal.science/hal-02931892v2/document; https://imt-atlantique.hal.science/hal-02931892v2/file/Remote_Sensing_2020.pdf; https://doi.org/10.3390/rs12223806

International audience Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on two small 10 • × 10 • GULFSTREAM and 8 • × 10 • OSMOSIS regions, part of the North-Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale dynamics while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on Observation System Simulation Experiments (OSSE), we will use the the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new neural networks-based end-to-end learning framework for the representation of spatio-temporal irregularly-sampled data. The main objective of this paper consists in providing a thorough intercomparison exercise with appropriate benchmarking metrics to assess if these approaches helps to improve the SSH altimetric interpolation problem and to identify which one performs best in this context. We demonstrate how the newly introduced NN method is a significant improvement with a plug-and-play implementation and its ability to catch up the small scales ranging up to 40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly ...