Multivariate localization methods for ensemble Kalman filtering

Soojin Roh, Mikyoung Jun, Istvan Szunyogh, Marc Genton

Research output: Contribution to journalArticleResearchpeer-review


In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The
standard approach to reducing the effects of this sampling
variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help
Original languageEnglish
Pages (from-to)723-735
Number of pages13
JournalNonlinear Processes in Geophysics
Issue number6
Publication statusPublished - 3 Dec 2015

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