地面观测资料在快速更新同化系统中的敏感性试验

Sensitivity Analysis of Surface Observation Data in WRF-ADAS Rapid Refresh System

通过对2011年7月31日发生在上海的局地强对流过程,利用快速更新同化系统(SMS-WARR),设计了四个试验,对地面观测资料(常规地面观测资料、加密自动气象站资料)进行敏感性数值试验。结果表明:不同地面观测资料同化对模式初始场的调整作用不同,地面观测资料的疏密影响模式初始场预报,加密自动站气象资料同化对初始温度场、风场的影响最明显;所有试验中,同化所有观测资料的模式能较好地模拟出此次强降水过程,且模拟的地面温度场、风场以及辐合线的演变与实况基本一致;通过分析、比较各试验初始场和预报结果的影响发现,同化所有地面观测资料能改善模式的初值,且观测资料通过循环同化的方式融合进模式,提高了模式对强对流中尺度结构特征的刻画,改善了对局地强对流天气系统的预报效果。

Using SMS-WARR and rapid refresh technique, the sensitivity of surface observation data （sur- face conventional observation data, automatic weather station data） to the numerical simulation of the se- vere convection event which occurred in Shanghai on July 31 2011 was analyzed. Four comparison experi- ments were designed to study the assimilation of surface observation data. The results showed that the ad- justment o{ model initial fields varies with assimilating different surface observation data. The density of surface observation data has impacts on initial fields. The initial temperature and wind fields are obviously affected by assimilating the automatic weather station data. After assimilating conventional surface obser- vation data and automatic weather station data, we found that the model can not only well simulate the process of this severe convection, but also simulate the surface temperature, wind field and evolution of convergence line which is more consistent with the observation. Through the analysis and comparison of the initial fileds and simulation results, we also found that the assimilation of all the observation data can improve the initial values of the model, and the observation data are integrated into the model by the mode of rapid refresh, which enhances the characterization of mesoscale structure of server convection, and im proved simulation capacity of the local severe convective systems.