The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale

46 47 A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and 48 ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System 49 (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the 50 static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational 51 analysis is performed using an equal weighting of static and flow-dependent error covariance as 52 derived from ensemble forecasts. The method is first applied to the assimilation of simulated 53 radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), 54 Hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the 55 EnKF method provides the best results for the model dynamic variables, while the hybrid 56 method provides the best results for hydrometeor related variables in term of rms errors. 57 Although storm structures can be established reasonably well using 3DVAR, the rms errors are 58 generally worse than seen from the other two methods. With two radars, the results from 59 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the 60 storm spin-up time because it fits the observations, especially the reflectivity observations, better 61 than the EnKF and the 3DVAR at the beginning of the assimilation cycles.