A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multi-class ice microphysics. New aspects compared to previous studies include the demonstration of the ability of EnKF method in retrieving multiple microphysical species associated with a multi-class ice microphysics scheme, and in accurately retrieving the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside precipitation regions, are used. Significant positive impact of the reflectivity assimilation

*Ensemble Kalman filter (EnKF) is an emerging advanced method that can be applied to storm-scale atmospheric data assimilation. Since its rather successful use in observation system simulation experiments

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This paper explores the use of large ensembles of model runs with randomly perturbed initial conditions for the calculation of error covariance fields, initial condition sensitivity fields, and perturbation impact fields. The calculation of error covariances from ensembles is familiar from ensemble Kalman filter (EnKF) techniques, but the calculation of sensitivity and impact fields from ensembles is new. This work is unlike previous EnKF work in that the ensemble members are randomly perturbed in each degree of freedom (DOF) of the model, rather than having perturbation fields based on expected errors in the analysis. In this work, all the DOFs of the model (or a subset) are independently and simultaneously perturbed and the response of the model to each perturbed DOF is sought by statistical technique. Sensitivity results are conceptually comparable to adjoint calculations. Error covariance, impact, and sensitivity fields constitute the three distinct kinds of fields that can be found from an ensemble. These fields can be found equivalently to first order as either covariances or partial derivatives from regression analysis. This paper makes use of ensembles of 2000 members of a mesoscale model, run for 6 hours over a domain in the