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Center for Nonlinear Studies |
Lagrangian data assimilation involves using observations of the positions of passive drifters in a flow in order to obtain a probability distribution of the underlying Eulerian flow field. Several data assimilation schemes have been studied in the context of geophysical fluid flows, but many of them have disadvantages within this framework. In this talk I will give an overview of Lagrangian data assimilation from a Bayesian viewpoint, discuss advantages and disadvantages of some traditional (sequential) data assimilation algorithms, and present a hybrid filter scheme applied to the shallow water equations.
Host: Aric Hagberg