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-area models (LAMs), which is the focus of the current review. 1 The first LAM application of the EnKF was found in Snyder and Zhang (2003) and Zhang et al. (2004) , where synthetic radar data was assimilated into a cloud model. Those two studies demonstrated that the EnKF analysis can faithfully approximate the truth in terms of both dynamic and thermodynamic variables of a supercell storm ( Fig. 1 ). Fig . 1. The performance of a convective-scale EnKF in assimilating radar radial velocity for
-area models (LAMs), which is the focus of the current review. 1 The first LAM application of the EnKF was found in Snyder and Zhang (2003) and Zhang et al. (2004) , where synthetic radar data was assimilated into a cloud model. Those two studies demonstrated that the EnKF analysis can faithfully approximate the truth in terms of both dynamic and thermodynamic variables of a supercell storm ( Fig. 1 ). Fig . 1. The performance of a convective-scale EnKF in assimilating radar radial velocity for
the background error covariances. Such an issue can be raised both for ensemble-based and innovation-based variance estimates. Evaluating and improving the accuracy of these two kinds of variance estimates is all the more important as innovation-based estimates can be used to represent model error contributions, by calibrating additive model perturbations (e.g., Houtekamer et al. 2009 ) or ensemble inflation factors (e.g., Li et al. 2009 ). Moreover, other approaches than Schur filter techniques
the background error covariances. Such an issue can be raised both for ensemble-based and innovation-based variance estimates. Evaluating and improving the accuracy of these two kinds of variance estimates is all the more important as innovation-based estimates can be used to represent model error contributions, by calibrating additive model perturbations (e.g., Houtekamer et al. 2009 ) or ensemble inflation factors (e.g., Li et al. 2009 ). Moreover, other approaches than Schur filter techniques
