Abstract
Abstract. This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.
Highlights
AND PROBLEM STATEMENTSatellite sensors provide invaluable information for the observation, reconstruction, forecasting and simulation of upper ocean dynamics, which is of key importance for variety of scientific and societal challenges, including for instance marine pollution monitoring, offshore activities, maritime traffic, climate studies
We report numerical experiments for the following case-studies: (i) the space-time interpolation of sea surface height (SSH) from along-track nadir and wide-swath altimetry data, (ii) the short-term forecasting of SSH dynamics, (iii) the design of adaptive sampling strategies to improve the reconstruction of SLA fields
We have investigated the application of end-to-end learning schemes based on variational formulations for the exploitation of satellite ocean remote data, and satellite altimetry data
Summary
Satellite sensors provide invaluable information for the observation, reconstruction, forecasting and simulation of upper ocean dynamics, which is of key importance for variety of scientific and societal challenges, including for instance marine pollution monitoring, offshore activities, maritime traffic, climate studies,. PCA-based and analog (i.e., nearestneighbor) methods (Alvera-Azcarate et al, 2005, Lguensat et al, 2017) were first considered As it has rapidly become the state-of-the-art approaches for numerous signal and image processing issues, including for instance for super-resolution or denoising problems (Dong et al, 2016, Chen et al, 2015), deep learning naturally arises as a appealing class of methods for ocean remote sensing data. The third application explores whether we may learn to predict where to sample new observations to complement satellite altimetry and best inform sea surface dynamics (Section 3.4). These different applications point out new means to learn task-specific or task-adapted representation and solvers for space-based ocean observing systems.
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