Filter divergence and EnKF

The Ensemble Kalman Filter (EnKF) is a widely used tool for assimilating data with high dimensional nonlinear models. Nevertheless, our theoretical understanding of the filter is largely supported by observational evidence rather than rigorous statements. In this talk we attempt to make rigorous statements regarding "filter divergence", where the filter loses track of the underlying signal. When observations are assimilated very frequently, the algorithm can be understood as an approximation of a continuous time stochastic differential equation. This interpretation allows us to use continuous time stochastic methods to analyze the algorithm.

Speaker Bio: Interested in stochastic systems that arise from physical modeling problems, such as multiscale systems and data assimilation algorithms. Courant instructor at NYU (2014 - current), short postdocs at Warwick and UNC Chapel Hill (2013-2014), PhD at Warwick w/ advisor Martin Hairer (2009-2013).

Presentation Slides: PDF

Note: Visitors from outside NIST must contact Cathy Graham; (301) 975-3800; at least 24 hours in advance.