P76  Evaluation of 3DVar Data Assimilat and Planetary Boundary Layer Scheme Sensitivities for Two Heavy Rainfall Cases in Southern China

Hou, Tuanjie, Fanyou Kong and Xunlai Chen, University of Oklahoma

To improve the accuracy of short-term (0-12h) forecasts of severe weather in Southern China, a realtime storm-scale forecasting system based on the WRF-ARW model and the ARPS 3DVAR/Cloud Analysis system has been developed collaboratively by the Center for Analysis and Prediction of Storms (CAPS) in University of Oklahoma, Shenzhen Meteorological Bureau (SZMB), and the Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences, and in operation since March 2010. The forecast system consists of an outer domain with 12-km horizontal grid spacing and a one-way nested high-resolution domain at 4-km grid spacing. Initialized from the fine-resolution ECMWF data, the forecast system was initially characterized by assimilating radar reflectivity and radial wind from local WSR-98 radars every hour in realtime. To provide more realistic synoptic settings for the storm-scale forecasts, surface (AWS) and rawinsonde observations including temperature, dew point, pressure and wind profiles were assimilated in two heavy rainfall cases in Southern China., A series of experiments including assimilation of radar data only, both radar and AWS data, and both radar and rawinsonde data were conducted. The impact of various observation data assimilation on quantitative precipitation forecasting was examined. The equitable threat scores (ETS) of 1-, 3- 6- and 12-h accumulated precipitation from the 4-km domain were computed against rain gauge measurement from over 1000 stations to evaluate forecast skills.