Computational and Applied Mathematics seminar

Abstract: Stochasticity has been employed systematically in geophysical fluid dynamics (GFD) to model uncertainty. Additionally, fully resolving geophysical flows is computationally expensive due to the large range of scales of motion present in these flows. These computational costs are efficiently mitigated by performing GFD simulations on coarse computational grids and modeling the effects of unresolved scales on resolved scales. On such grids, the uncertainty due to unresolved small-scale motions has to be taken into account as well as the loss of accuracy due to poorly resolved spatial derivatives. In this presentation, we discuss how data assimilation methods can be used to derive data-driven stochastic forcing for coarse computational GFD. We will show that a straightforward algorithm, based on several simplifying assumptions, already leads to qualitatively accurate outcomes at strongly reduced computational costs.