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Sea surface temperature prediction and reconstruction using patch-level neural network representations

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Abstract

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation datasets. In this work we investigate the relevance of recently introduced bilinear residual neural network representations, which mimic numerical integration schemes such as Runge-Kutta, for the forecasting and assimilation of geophysical fields from satellite-derived remote sensing data. As a case-study, we consider satellite-derived Sea Surface Temperature time series off South Africa, which involves intense and complex upper ocean dynamics. Our numerical experiments demonstrate that the proposed patch-level neural-network-based representations outperform other data-driven models, including analog schemes, both in terms of forecasting and missing data interpolation performance with a relative gain up to 50\% for highly dynamic areas.

Dates and versions

hal-01883209 , version 1 (27-09-2018)

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Said Ouala, Cédric Herzet, Ronan Fablet. Sea surface temperature prediction and reconstruction using patch-level neural network representations. IGARSS 2018 : IEEE International Geoscience and Remote Sensing Symposium, Jul 2018, Valence, Spain. pp.1-4, ⟨10.1109/IGARSS.2018.8519345⟩. ⟨hal-01883209⟩
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