Stochastic parameterization with dynamic mode decomposition - Mécanique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Stochastic parameterization with dynamic mode decomposition

Résumé

A physical stochastic parameterization is adopted in this work to account for the effects of the unresolved small-scale on the large-scale flow dynamics. This random model is based on a stochastic transport principle, which ensures a strong energy conservation. The dynamic mode decomposition (DMD) is performed on high-resolution data to learn a basis of the unresolved velocity field, on which the stochastic transport velocity is expressed. Time-harmonic property of DMD modes allows us to perform a clean separation between time-differentiable and time-decorrelated components. Such random scheme is assessed on a quasi-geostrophic (QG) model.
Fichier principal
Vignette du fichier
LU_DMD.pdf (1.37 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03597550 , version 1 (04-03-2022)

Identifiants

  • HAL Id : hal-03597550 , version 1

Citer

Long Li, Etienne Mémin, Gilles Tissot. Stochastic parameterization with dynamic mode decomposition. 2022. ⟨hal-03597550⟩
55 Consultations
64 Téléchargements

Partager

Gmail Facebook X LinkedIn More