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Over the last years, a very active field of research aims at exploring new data-driven and 1 learning-based methodologies to propose computationally efficient strategies able to benefit from 2 the large amount of observational remote sensing and numerical simulations for the reconstruction, 3 interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, 4 we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal 5 interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal 6 fields. We focus on two small 10◦ × 10◦ GULFSTREAM and 8◦ × 10◦ OSMOSIS regions, part 7 of the North-Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale 8 dynamics while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on 9 Observation System Simulation Experiments (OSSE), we will use the the NATL60 high resolution 10 deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric 11 observational dataset: along-track nadir data for the current capabilities of the observation system 12 and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce 13 the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new 14 neural networks-based end-to-end learning framework for the representation of spatio-temporal 15 irregularly-sampled data. The main objective of this paper consists in providing a thorough 16 intercomparison exercise with appropriate benchmarking metrics to assess if these approaches 17 helps to improve the SSH altimetric interpolation problem and to identify which one performs best 18 in this context. We demonstrate how the newly introduced NN method is a significant improvement 19 with a plug-and-play implementation and its ability to catch up the small scales ranging up to 40km, 20 inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating 21 jointly wide-swath SWOT and (agreggated) along-track nadir observations. 22

Intercomparison of Data-Driven and Learning-Based Interpolations of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations