中文摘要：

本文利用包含复杂冰相微物理过程的WRF（Weather Research and Forecasting）模式,针对2007年4月23日发生在我国华南地区的一次典型飑线天气过程,分别进行了确定性预报和集合预报试验,发现确定性预报能大致捕捉到飑线系统的发生发展过程,但对飑线后部的层云区模拟效果较差。集合预报能够有效地减少模式的不确定性,大部分集合成员对飑线的模拟效果优于确定性预报。进一步将集合预报得到的40个成员作为背景场,采用En SRF（Ensemble Square Root Filter）同化多普勒天气雷达资料,并将分析得到的集合作为初始场进行集合预报,通过与未同化雷达资料的集合对比,考察了En SRF同化多部雷达资料对飑线系统的影响。结果表明：En SRF雷达资料同化增加了模式初始场的中小尺度信息,大部分集合成员的分析场能够较准确地再现飑线的热力场、动力场和微物理场的细致特征,并且模拟出飑线后部的层云结构。通过对En SRF分析的集合进行模拟发现,大部分集合成员较未同化雷达资料时模拟效果有明显改善。同化后的集合预报ETS（Equitable Threat Score）评分最高,其次是未同化的集合预报,确定性预报的最低。

英文摘要：

An ensemble forecast and a deterministic forecast of a squall line that occurred in southern China on 23 April 2007 have been conducted using the Weather Research and Forecasting（WRF） model with microphysical schemes that include complex ice and snow processes. It is found that the deterministic forecast can capture the main characteristics of the squall line, but the simulated squall line is inaccurate, especially in the back stratus cloud region. The ensembleforecast technique can reduce the uncertainty in the model simulation and the majority of the members in the ensemble show a better performance than the deterministic forecast. The analysis members, which are obtained from radar data assimilation using the En SRF（Ensemble Square Root Filter） method with outputs of the 40 members in the ensemble experiment as backgrounds, are used to provide initial conditions for the ensemble forecast. Differences in results among the ensemble members with and without radar data assimilation reflect the impact of EnS RF radar data assimilation on the simulation of the squall line. The analysis members with radar data assimilation provide more mesoscale and microscale information of the convective cells in the squall line system. Most members can capture the thermal-dynamical structure of the squall line system and successfully simulate the suqall line in the back stratus cloud region. Analysis of the simulations in the ensemble forecast with radar data assimilation indicates that most members perform better than that without radar data assimilation. The ETS（Equitable Threat Score） of the ensemble forecast with radar data assimilation is higher than that without radar data assimilation, and the ETS of the deterministic forecast is lower than that of the ensemble forecast.