New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics

Valentin Resseguier (1) , Long Li (2, 3) , Gabriel Jouan (1) , Pierre Dérian (4) , Etienne Mémin (4) , Bertrand Chapron (5)

1 L@b SCALIAN
2 IRMAR - Institut de Recherche Mathématique de Rennes
3 Inria Rennes – Bretagne Atlantique
4 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
5 LOPS - Laboratoire d'Océanographie Physique et Spatiale

Numerical simulations of industrial and geophysical fluid flows cannot usually solve the exact Navier-Stokes equations. Accordingly, they encompass strong local errors. For some applications-like coupling models and measurements-these errors need to be accurately quantified, and ensemble forecast is a way to achieve this goal. This paper reviews the different approaches that have been proposed in this direction. A particular attention is given to the models under location uncertainty and stochastic advection by Lie transport. Besides, this paper introduces a new energy-budget-based stochastic subgrid scheme and a new way of parameterizing models under location uncertainty. Finally, new ensemble forecast simulations are presented. The skills of that new stochastic parameterization are compared to that of the dynamics under location uncertainty and of randomized-initial-condition methods.

Domaines: Dynamique Chaotique [nlin.CD]; Mécanique des fluides [physics.class-ph]; Océan, Atmosphère; Traitement du signal et de l'image [eess.SP]

Valentin Resseguier, Long Li, Gabriel Jouan, Pierre Dérian, Etienne Mémin, et al.. New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics. Archives of Computational Methods in Engineering, 2021, 28 (1), pp.215-261. ⟨10.1007/s11831-020-09437-x⟩. ⟨hal-02558016v1⟩