FN Archimer Export Format PT J TI New Trends in Ensemble Forecast Strategy: Uncertainty Quantification for Coarse-Grid Computational Fluid Dynamics BT AF Resseguier, V. Li, L. Jouan, G. Dérian, P. Mémin, E. Chapron, Bertrand AS 1:1;2:2;3:1;4:2;5:2;6:3; FF 1:;2:;3:;4:;5:;6:PDG-ODE-LOPS-SIAM; C1 Lab, SCALIAN, Espace Nobel, 2 Allée de Becquerel, Rennes, 35700, France Fluminance Group, Inria, Campus universitaire de Beaulieu, Rennes, 35042, France LOPS, Ifremer, Pointe du Diable, Plouzané, 29280, France C2 SCALIAN, FRANCE INRIA, FRANCE IFREMER, FRANCE SI BREST SE PDG-ODE-LOPS-SIAM UM LOPS IN WOS Ifremer UMR copubli-france IF 8.171 TC 19 UR https://archimer.ifremer.fr/doc/00632/74457/74278.pdf LA English DT Article AB 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. PY 2021 PD JAN SO Archives Of Computational Methods In Engineering SN 1134-3060 PU Springer Science and Business Media LLC VL 28 IS 1 UT 000556439700001 BP 215 EP 261 DI 10.1007/s11831-020-09437-x ID 74457 ER EF