Special CMX Seminar

The ensemble Kalman filter (EnKF) is a widely used data assimilation algorithm to combine dynamical systems with observations. While the EnKF has found widespread use in practical applications, the development of theoretical foundations for the algorithm remains an area of active research. This talk will describe recent progress in the analysis of the EnKF. First, we first characterize settings where the ensemble Kalman filter can provide accurate state estimates over long time horizons, even for chaotic and partially-observed dynamical systems with mis-specified forecast models. Next, we provide a non-asymptotic analysis that explains how the EnKF can exploit structured covariance operators to provide reliable estimates of uncertainty with a small ensemble size.