Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks

• Main Authors: Lguensat, Redouane, Fablet, Ronan, Sommer, Julien Le, Metref, Sammy, Cosme, Emmanuel, Ouenniche, Kaouther, Drumetz, Lucas, Gula, Jonathan
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)
• Format: Report
• Language: English
• Published: HAL CCSD 2020
• Subjects: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; North Atlantic
• Online Access: https://hal.science/hal-03084229

Accepted for publication in IEEE IGARSS 2020 conference The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field. However, to fulfill the promises of this mission, filtering the tidal component of the SSH measurements is necessary. This challenging problem is crucial since the posterior studies done by physical oceanographers using SWOT data will depend heavily on the selected filtering schemes. In this paper, we cast this problem into a supervised learning framework and propose the use of convolutional neural networks (ConvNets) to estimate fields free of internal tide signals. Numerical experiments based on an advanced North Atlantic simulation of the ocean circulation (eNATL60) show that our ConvNet considerably reduces the imprint of the internal waves in SSH data even in regions unseen by the neural network. We also investigate the relevance of considering additional data from other sea surface variables such as sea surface temperature (SST).