Evaluation of Radar and Automatic Weather Station Data Assimilation for a Heavy Rainfall Event in Southern China  

作　　者：HOU Tuanjie Fanyou KONG CHEN Xunlai LEI Hengchi HU Zhaoxia 

机构地区：[1]Laboratory of Cloud-Precipitation Physics and Severe Storms,Institute of Atmospheric Physics,Chinese Academy of Sciences [2]Center for Analysis and Prediction of Storms,University of Oklahoma [3]Shenzhen Key Laboratory of Severe Weather in South China [4]Shenzhen Meteorological Bureau [5]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science & Technology

出　　处：《Advances in Atmospheric Sciences》2015年第7期967-978,共12页大气科学进展（英文版）

基　　金：supported by a grant to CAPS from Shenzhen Meteorological Bureau (SZMB) and Shenzhen Key Laboratory of Severe Weather in South China;Support was jointly provided by the National Basic Research Program of China (973 Program, Grant No. 2013CB430105);the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05100300);the National Natural Science Foundation of China (Grant No. 41105095)

摘　　要：To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

关 键 词：data assimilation radar data heavy rainfall quantitative precipitation forecasting 

分 类 号：P415.12[天文地球—大气科学及气象学] P457.6