Impact of dual-polorimetric radar data assimilation on short-term rainfall forecast of two convective systems

Quantitative rainfall forecast is one of the major interest in numerical weather prediction. Radar data assimilation is a promising way to improve the rainfall forecast by providing detailed information for the mesoscale structure of the weather systems. Radar data assimilation still remains a challenging problem due to the difficulty in the technique of assimilating the uneven distributed radar data, as well as the uncertainty in radar observations. With the forthcoming dual-polarimetric capabilities of the U.S. NEXRAD radar system (set to become installed beginning now ~ 2010), the time is upon us to begin understanding how these data can improve forecast initialization. Very little previous research has been done on the assimilation of fields, describing microphysical precipitation information, as derived from dual-polarimetric observations.

The goal of this study is to improve the methodology of radar data assimilation, to understand the precipitation processes of convective systems, and to investigate to what extent the dual-pol radar observations can help with the short-term rainfall forecasts from organized convection. The WRF model and its 3DVAR data assimilation system are used in our work. We will present our recent work on assimilating the ARMOR dual-polarimetric radar data, done over North Alabama. Details of the methodology of assimilation the dual-pol radar variables into the initial conditions for the numerical simulations of two convective storms will be provided. We will also discuss the different impact of each dual-pol radar variable on model initial condition and the short-term rainfall prediction of the convective systems.