Assimilation of Polarimetric Radar Data Using Ensemble Kalman Filter: Experiment with Simulated Data

Since the use of differential reflectivity for rainfall estimation was first proposed by Seliga and Bringi (1976), many studies have shown that polarimetric measurements can improve precipitation type classification and quantitative precipitation estimate (Straka et al. 2000). Moreover, the polarimetric radar (PR) upgrade plan of the National Weather Services (NWS) for the operational WSR-88D network radars will undoubtedly motivate more active research on the utilization of PR data. A more accurate estimate of the amounts of hydrometeors using PR data can contribute to the improvement and verification of microphysical parameterizations in cloud and mesoscale models. It can provide a useful means for the initialization of hydrometeor types and amount for storm-scale and mesoscale NWP models, and help the verification of quantitative precipitation forecasts (QPF). Such estimations can also help enhance our understanding of the interactions between microphysics and kinematics in severe storms and mesoscale system (Straka et al. 2000). Polarimetric radars also should be helpful for storm-scale model initialization through data assimilation. Initialization of convective storms using radar data within a numerical model has enjoyed reasonable success in recent years, using methods such as the complex cloud analysis, 4DVAR and more recently the ensemble Kalman filter (EnKF). The first paper which investigates the potential of EnKF to assimilate Doppler radar data into cloud model with a warm rain microphysics only is Snyder and Zhang (2003). In their study, state variables not directly observed are successfully retrieved using EnKF. The recent studies of Tong and Xue (2005, TX05 hereafter) and Xue et al. (2005, hereafter XTD05) also show that the cloud fields, including microphysical species associated with a 3-ice microphysics scheme, can be accurately retrieved using the EnKF method from simulated radial velocity and reflectivity data. It is expected that the analysis results can be further improved when additional polarimetric parameters are assimilated. The parameters include differential reflectivity (Zdr), specific differential phase (Kdp) and possibly some other parameters for hydrometeor classification. Wu et al. (2000) used Zdr indirectly in a cloudscale 4DVAR data assimilation system; the reflectivity (Z) and differential reflectivity were first converted to rain and ice mixing ratios which are subsequently assimilated together with the radial velocity (Vr) data. In this study, the direct assimilation of dual polarization radar data using an ensemble Kalman filter is explored for the first time. Forward observation operators for the polarimetric radar measurements that are consistent with microphysics schemes with varying degrees of assumptions are first developed and their sensitivities to the assumptions are examined. These observational operators are then used to create simulated data sets from a model storm, and the impacts of these data are examined through Observing System Simulation Experiments (OSSEs).