The ice water content(IWC) distribution in a mixed-phase cloud system was investigated using Cloud-Sat data,aircraft measurements,and the Weather Research and Forecasting(WRF) model.Simulated precipitation and IWC were in general agreement with rain gauge,sat-ellite,and aircraft observations.The cloud case was char-acterized by a predominant cold layer and high IWC throughout the cloud-development and precipitation stages.The CloudSat-retrieved products suggested that the IWC was distributed from 4.0 to 8.0 km,with the maximum values(up to 0.5 g m-3) at 5.0-6.0 km at the earlymature stage of cloud development.High IWC(up to 0.8 g m-3) was also detected by airborne probes at 4.2 and 3.6 km at the late-mature stage.The WRF model simulation re-vealed that the predominant riming facilitated rapid ac-cumulation of high IWC at 3.0-6.0 km.
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.
