Large scale flows under location uncertainty: a consistent stochastic framework

Bertrand Chapron 1 Pierre Dérian 2 Etienne Mémin 2 Valentin Resseguier 2
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRMAR - Institut de Recherche Mathématique de Rennes, IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Inria Rennes – Bretagne Atlantique
Abstract : Using a classical example, the Lorenz-63 model, an original stochastic framework is applied to represent large-scale geophysical flow dynamics. Rigorously derived from a reformulated material derivative, the proposed framework encompasses several meaningful mechanisms to model geophysical flows. The slightly compressible setup , as treated in the Boussinesq approximation, brings up a stochastic transport equation for the density and other related thermo-dynamical variables. Coupled to the momentum equation through a forcing term, a resulting stochastic Lorenz-63 model is consistently derived. Based on such a reformulated model, the pertinence of this large-scale stochastic approach is demonstrated over classical eddy-viscosity based large-scale representations.
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Bertrand Chapron, Pierre Dérian, Etienne Mémin, Valentin Resseguier. Large scale flows under location uncertainty: a consistent stochastic framework. Quarterly Journal of the Royal Meteorological Society, Wiley, 2018, 144 (710), pp.251-260. ⟨10.1002/qj.3198⟩. ⟨hal-01629898⟩

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