Localization techniques for ensemble transform Kalman filters∗
نویسندگان
چکیده
Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The dimension of phase is typically much larger than the number of ensemble members which leads to inaccurate results in the computed covariance matrices. These inaccuracies lead, among others, to spurious long range correlations which can be eliminated by Schur-product-based localization techniques. In this paper, we propose computationally robust and efficient techniques 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 analysis update of the ensemble deviation matrix.
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