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.