The Analysis and Prediction of Polarimetric Variables and Microphysical States of Convective Storms Using Double-Moment Microphysics, Multi-Network Radar Data, and the Ensemble Kalman Filter

Accurate storm-scale numerical weather prediction (NWP) requires a good estimate of the observed particle size distribution (PSD) of hydrometeors for the initial condition of the forecast. Two main approaches currently exist for estimating PSDs from radar data: i) direct observation-based retrieval, and ii) NWP model-based retrieval (i.e., data assimilation (DA)). Direct retrieval requires at least the same number of independent radar measurements as the number of PSD parameters sought which is higher than available measurements when using multi-moment microphysics schemes. In this case, model-based retrieval using the ensemble Kalman filter (EnKF) yields promising results.

In this study, radar data are assimilated using the EnKF for the case of a mesoscale convective system (MCS) that passed over western Oklahoma and northern Texas on 8-9 May 2007. Both single-moment (SM) and double-moment (DM) microphysics schemes are examined. The event was observed by multiple radar networks, including KOUN, a dual-polarimetric WSR-88D radar. A polarimetric radar simulator using the model state variables is used in conjunction with the polarimetric observations to assess the model microphysical state. It was found that simulated polarimetric variables from the estimate using the DM microphysics scheme matched observations better, specifically with regard to the values of the simulated variables and the difference between the PSDs of the stratiform and convective precipitation regions. Additionally, a DM forecast initiated from the DM analysis showed significant improvement over SM and DM forecasts initiated from the SM analysis in terms of the structure of the system in addition to the aforementioned state improvements.