4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry

TL;DR

This paper introduces 4DVarNet-SSH, a neural network-based variational data assimilation framework that significantly improves the reconstruction of sea surface currents from satellite altimeter data, especially for high-resolution scales.

Contribution

The paper presents a novel end-to-end neural scheme for space-time interpolation of satellite altimeter data, enhancing mesoscale ocean current reconstruction beyond operational methods.

Findings

30-60% reduction in reconstruction error compared to optimal interpolation

Achieves resolution below 70km and 7 days for nadir+swot configurations

Demonstrates effectiveness on simulated ocean data in two case-study regions

Abstract

The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and Water Topography) altimeter mission. Operational systems however generally fail to retrieve mesoscale dynamics for horizontal scales below 100km and time-scale below 10 days. Here, we address this challenge through the 4DVarnet framework, an end-to-end neural scheme backed on a variational data assimilation formulation. We introduce a parametrization of the 4DVarNet scheme dedicated to the space-time interpolation of satellite altimeter data. Within an observing system simulation experiment (NATL60), we demonstrate the relevance of the proposed approach both for nadir and nadir+swot altimeter configurations for two contrasted case-study regions in terms of upper ocean dynamics. We report relative improvement with respect to the operational optimal interpolation between 30% and 60% in terms of reconstruction error. Interestingly, for the nadir+swot altimeter configuration, we reach resolved space-time scales below 70km and 7days. The code is open-source to enable reproductibility and future collaborative developments. Beyond its applicability to large-scale domains, we also address uncertainty quantification issues and generalization properties of the proposed learning setting. We discuss further future research avenues and extensions to other ocean data assimilation and space oceanography challenges.