A comparison of error subspace Kalman filters

Abstract

Three advanced filter algorithms based on the Kalman filter are reviewed and presented in a unified notation. They are the well-known ensemble Kalman filter (EnKF), the singular evolutive extended Kalman (SEEK) filter, and the less common singular evolutive interpolated Kalman (SEIK) filter. For comparison, the mathematical formulations of the filters are reviewed in relation to the extended Kalman filter as error subspace Kalman filters. The algorithms are presented in their original form and possible variations are discussed. A comparison of the algorithms shows their theoretical capabilities for efficient data assimilation with large-scale non-linear systems. In particular, problems of the analysis equations are apparent in the original EnKF algorithm due to the Monte Carlo sampling of ensembles. Theoretically, the SEIK filter appears to be a numerically very efficient algorithm with high potential for use with non-linear models. The superiority of the SEIK filter is demonstrated on the basis of identical twin experiments using a shallow-water model with non-linear evolution. Identical initial conditions for all three filters allow for a consistent comparison of the data assimilation results. These show how choices of particular state ensembles and assimilation schemes lead to significant variations of the filter performance. This is related to different qualities of the predicted error subspaces, as is demonstrated in an examination of the predicted state covariance matrices.