A localization technique for ensemble Kalman filters

Abstract

Abstract Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The phase‐space dimension is typically much larger than the number of ensemble members, which leads to inaccurate results in the computed covariance matrices. These inaccuracies can lead, among other things, to spurious long‐range correlations, which can be eliminated by Schur‐product‐based localization techniques. In this article, we propose a new technique for implementing such localization techniques within the class of ensemble transform/square‐root Kalman filters. Our approach relies on a continuous embedding of the Kalman filter update for the ensemble members, i.e. we state an ordinary differential equation (ODE) with solutions that, over a unit time interval, are equivalent to the Kalman filter update. The ODE formulation forms a gradient system with the observations as a cost functional. Besides localization, the new ODE ensemble formulation should also find useful application in the context of nonlinear observation operators and observations that arrive continuously in time. Copyright © 2010 Royal Meteorological Society