7B.6 An examination of WRF-based RTFDDA radar data assimilation on its capability in forecasting precipitation through observing system simulation experiments

Huang, Yongjie, Mei Xu, and Yubao Liu, National Center for Atmospheric Research, Boulder, Colorado

There are several methods to assimilate radar observations into a mesoscale model, such as 3DVAR, 4DVAR, EnKF. Meanwhile, a less expensive method based on the analysis nudging (Newtonian relaxation) technique was developed in order to be more applicable to real-time operations. A WRF-based real-time four-dimensional data assimilation and short-term forecasting system (RTFDDA) which has been developed at NCAR shows that the nudging method is effective in assimilating synoptic scale observations. The purpose of this study is to investigate the performance of WRF-based RTFDDA radar data assimilation (RTFDDA-RDA) in terms of the short-term quantitative precipitation forecasts (QPFs). The RTFDDA-RDA based on the analysis nudging technique assimilates cloud hydrometeors and latent heat. Simulated data from observing system simulation experiments (OSSEs) of a convective storm are assimilated, and the performances of the assimilation are evaluated. Results indicate that the RTFDDA-RDA system is able to analyze certain mesoscale and convective-scale features and helps improve the short-term precipitation forecasting of convective storm through nudging cloud hydrometeors retrieved from radar reflectivity and latent heat. Besides, difference of assimilation frequency and nudging coefficient (assimilation time window) in the radar assimilation experiments has a different influence on short term precipitation forecasting. It is demonstrated that the RTFDDA-RDA can improve the initial conditions and then improve skills on QPF. A systematic evaluation of the performance of RTFDDA-RDA will be conducted in a flood season real-time test.