A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps

• URL: http://arxiv.org/abs/2111.06841v1
• Date: Fri, 12 Nov 2021 17:59:52 GMT
• Title: A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
• Authors: Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac and Redouane Lguensat
• Abstract summary: We show that learning a model jointly with the dynamical solver and a meaningful $textita posteriori$-based loss function lead to stable and realistic simulations.
• Score: 4.212677330241214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible. While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, the backward energy transfer of two-dimensional flows still remains a stability issue for trained models. We show that learning a model jointly with the dynamical solver and a meaningful $\textit{a posteriori}$-based loss function lead to stable and realistic simulations when applied to quasi-geostrophic turbulence.

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