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- W4319040892 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 100 km 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 70 km and 7 days. 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." @default.
- W4319040892 created "2023-02-03" @default.
- W4319040892 creator A5052865881 @default.
- W4319040892 date "2023-02-02" @default.
- W4319040892 modified "2023-09-25" @default.
- W4319040892 title "Comment on gmd-2022-241" @default.
- W4319040892 doi "https://doi.org/10.5194/gmd-2022-241-ec1" @default.
- W4319040892 hasPublicationYear "2023" @default.
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