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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Sep 2020

17 Sep 2020

Review status
This preprint is currently under review for the journal NPG.

Boosting performance in machine learning of geophysical flows via scale separation

Davide Faranda1,2,3, Mathieu Vrac1, Pascal Yiou1, Flavio Maria Emanuele Pons1, Adnane Hamid1, Giulia Carella1, Cedric Ngoungue Langue1, Soulivanh Thao1, and Valerie Gautard4 Davide Faranda et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement, CE Saclay l’Orme des Merisiers, UMR 8212CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
  • 2London Mathematical Laboratory, 8 Margravine Gardens, London, W68RH, UK
  • 3LMD/IPSL, Ecole Normale Superieure, PSL research University, Paris, France
  • 4DRF/IRFU/DEDIP//LILAS Departement d’Electronique des Detecteurs et d’Informatique pour la Physique, CE Saclayl’Orme des Merisiers, 91191 Gif-sur-Yvette, France

Abstract. Recent advances in statistical and machine learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this article we investigate the applicability of such a framework to geophysical flows, known to involve multiple scales in length, time and energy and to feature intermittency. We show that both multiscale dynamics and intermittency introduce severe limitations on the applicability of recurrent neural networks, both for short-term forecasts, as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics from the intermittent/small-scale features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics. Better short and long term forecasts of sea-level pressure data can be obtained with an optimal choice of spatial coarse grain and time filtering.

Davide Faranda et al.

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Davide Faranda et al.

Davide Faranda et al.


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