Sagy Ephrati, University of Twente
Data-driven stochastic forcing for uncertainty quantification and subgrid-scale modeling in geophysical fluid dynamics
Abstract: Stochasticity has been employed systematically in geophysical fluid dynamics (GFD) to model uncertainty. Geophysical flows are typically dominated by advection effects and contain a family of conserved quantities, of which energy and enstrophy are considered most important. Stochastic advection by Lie transport (SALT), which is a data-driven enstrophy-preserving transport noise, can be used to quantify uncertainty in these models. The first part of the presentation illustrates how SALT can be used efficiently to quantify uncertainty due to unresolved dynamics. However, this approach seems insufficient in the presence of discretization error. To counteract the effects of coarsening, we apply a simple data-driven stochastic subgrid-scale parametrization inspired by data assimilation algorithms. In the second part of the presentation, we show that the proposed parametrization recovers measured reference kinetic energy spectra in coarse numerical simulations.
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https://cwi-nl.zoom.us/j/89412162009?pwd=QnFheEFvQ0NsSnZoWldoVStMRWN0QT09
Meeting ID: 894 1216 2009
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