Radar data assimilation improves short-term prediction of convective weather by providing more accurate initial conditions. However, the assimilation of dual-pol radar data is challenging, and relatively little previous research has been done assimilating these data. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm by enhancing the initialization of convection-resolving NWP models.
Our presentation will highlight our recent work on assimilating dual-pol radar data for real case storms. In our study, high-resolution Weather Research and Forecasting (WRF) model and its 3-Dimensional Variational (3DVAR) data assimilation system are used for several convective storm events. More recently, our research explores the use of the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, as well as snowfall events, with a significant focus being on an improved representation of ice hydrometeors. In addition, discussion will be provided on our ongoing work on the development of the ice microphysics processes in the 3DVAR assimilation procedure with respect to dual-pol observations. Developing uses of dual-pol variables for properly representing ice hydrometeor types within a data assimilation package helps to describe more precisely the ice particle distribution for snowstorms, as well as in convective systems.
Beyond the forward model developing within a 3DVAR framework, high-resolution (<=1 km) WRF model simulations and storm scale data assimilation experiments will be presented, emphasizing both warm rain and ice microphysical processes. Further details of the methodology of data assimilation, the influences of different dual-pol variables, the impact of the dual-pol data on microphysical properties, and the information content of the dual-pol variables and observational operators will also be presented at the conference.