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The reconstruction of sea surface currents from satellite altimeter data is akey challenge in spatial oceanography, especially with the upcoming wide-swathSWOT (Surface Ocean and Water Topography) altimeter mission. Operationalsystems however generally fail to retrieve mesoscale dynamics for horizontalscales below 100km and time-scale below 10 days. Here, we address thischallenge through the 4DVarnet framework, an end-to-end neural scheme backed ona variational data assimilation formulation. We introduce a parametrization ofthe 4DVarNet scheme dedicated to the space-time interpolation of satellitealtimeter data. Within an observing system simulation experiment (NATL60), wedemonstrate the relevance of the proposed approach both for nadir andnadir+swot altimeter configurations for two contrasted case-study regions interms of upper ocean dynamics. We report relative improvement with respect tothe operational optimal interpolation between 30% and 60% in terms ofreconstruction error. Interestingly, for the nadir+swot altimeterconfiguration, we reach resolved space-time scales below 70km and 7days. Thecode is open-source to enable reproductibility and future collaborativedevelopments. Beyond its applicability to large-scale domains, we also addressuncertainty quantification issues and generalization properties of the proposedlearning setting. We discuss further future research avenues and extensions toother ocean data assimilation and space oceanography challenges.

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