基于多中心TIGGE资料的区域GRAPES集合预报初步试验

The Preliminary Experiment of GRAPES-MESO Ensemble Prediction Based on TIGGE Data

基于全球交互式大集合(TIGGE)预报资料,研究利用TIGGE全球集合预报大尺度不确定信息,构造区域GRAPES集合预报的初值扰动方法和试验方案,并对2008年7月22日发生在黄淮地区的一次暴雨过程进行了集合预报试验。试验结果表明:构造的初值扰动场能够表征TIGGE全球集合预报初值中的大尺度不确定信息,区域GRAPES集合预报系统可以捕获极端降水天气,对降水预报具有显著的改进作用,集合预报平均及降水概率预报能有效地反映暴雨降水特点,暴雨发生概率较高的区域与实况对应关系较好。积分初期,暴雨的预报一致性(集合离散度)和预报技巧(集合平均预报均方根误差)之间的关系显示了区域GRAPES集合预报系统是合理的,但积分后期,由于模式在积分过程中的动力调整作用,减小了初值扰动对预报结果的影响,限制了集合离散度的增长速度。

A heavy rainfall is simulated by GRAPES MESO model, occurring in Huanghuai area on 22 July 2008 to study the forecast uncertainty and the initial perturbation method when establishing GRAPES MESO ensemble prediction system （called GRAPES-MEPS） based on TIGGE data. The results suggest that the initial perturbation structure can reflect some information of initial uncertainty. The GRAPES-MEPS has the ability to capture the extreme rainfall event and can significantly improve the forecast skill of heavy rainfall. The ensemble mean can capture some characteristics of the mesoscale heavy rainfall validly. The probability forecast gives the high probability area, where the heavy rainfall occurred actually. The ensemble verification results indicate that the relation between the spread and RMSE demonstrates the rationality of the GRAPES-MEPS during the early integral period, but the impact of the initial perturbation decreases in the subsequent integral period owing to the model dynamic adjustment.