Learning Stochastic Representations of Geophysical Dynamics

Said Ouala; Ronan Fablet; Cédric Herzet; Bertrand Chapron; Ananda Pascual; Fabrice Collard; Lucile Gaultier

ICASSP 2019accepted0 citations

Learning Stochastic Representations of Geophysical Dynamics

Said Ouala, Ronan Fablet, Cédric Herzet, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier

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 modelization. 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.

BibTeX

@inproceedings{icassp2019_learningstochast,
  title = {Learning Stochastic Representations of Geophysical Dynamics},
  author = {Said Ouala and Ronan Fablet and Cédric Herzet and Bertrand Chapron and Ananda Pascual and Fabrice Collard and Lucile Gaultier},
  booktitle = {ICASSP 2019},
  year = {2019}
}