Learning stochastic representations of geophysical dynamics

Abstract : In the last years, Neural Networks have enriched the state-of-the-art in probabilistic modeling. This is principally due to the advances in deep learning which allow a better understanding of complex systems. However, the stochastic representation of spatio-temporal fields is still an open challenge that may benefit from the recent advances in probabilistic mode-lization. In this work, we explore neural network to derive a stochastic representation of spatio-temporal dynamical systems based on ensemble forecasting. Trough the implementation of our stochastic model in a classical Kalman filtering scheme, we demonstrate the relevance of the proposed architecture in the reconstruction of geophysical fields with respect to the state-of-the-art approaches.
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Pré-publication, Document de travail
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Contributeur : Said Ouala <>
Soumis le : lundi 4 février 2019 - 09:55:34
Dernière modification le : mardi 30 juillet 2019 - 09:05:12
Document(s) archivé(s) le : dimanche 5 mai 2019 - 13:21:50


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  • HAL Id : hal-02005403, version 1


Said Ouala, Ronan Fablet, Cedric Herzet, Bertrand Chapron, Ananda Pascual, et al.. Learning stochastic representations of geophysical dynamics. 2019. ⟨hal-02005403⟩



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