Sea surface temperature prediction and reconstruction using patch-level neural network representations - IMT Atlantique Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

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

Résumé

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.

Domaines

Autres [stat.ML]

Dates et versions

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

Identifiants

Citer

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⟩
192 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More