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Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature
Provedor de dados: 5
Autores: Ouala, Said; Fablet, Ronan; Herzet, Cedric; Chapron, Bertrand; Pascual, Ananda; Collard, Fabrice; Gaultier, Lucile.
The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential...
Tipo: Text
Palavras-chave: Data assimilation; Dynamical model; Kalman filter; Neural networks; Data-driven models; Interpolation.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00481/59286/61979.pdf