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arXiv · 2111.06841

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

Hugo Frezat·Julien Le Sommer·Ronan Fablet·Guillaume Balarac·Redouane Lguensat
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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 flows with success, two dimensional flows are more challenging because of the backscatter of energy from small to large scales. 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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Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat. 2021-11-12. A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps. https://arxiv.org/abs/2111.06841

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