基于地面资料集合均方根滤波同化方案的京津冀暴雨模拟研究

A numerical study of the rainstorm in Beijing-Tianjin-Hebei region based on assimilation of surface AWS data using the Ensemble Square Root Filter

中文关键词:  自动气象站资料  资料同化  集合均方根滤波  地形代表性误差

英文关键词:AWS data assimilation  Data assimilation  EnSRF  Terrain error of representativeness

中文摘要:

      为了更加有效地同化地面自动气象站观测资料，针对模式地形与观测站地形存在的高度差异对同化效果的影响，提出了相应的解决方案。在同化系统的位温和露点观测误差中分别引入位温和露点地形代表性误差，在WRF模式中应用集合均方根滤波方法（EnSRF）同化地面自动气象站观测资料，并对2016年一次京津冀暴雨个例进行数值试验。研究结果表明，同化地面资料后，同化阶段的均方根误差、预报阶段的降水TS评分和前13个时次各要素预报均有整体改进。在观测误差中引入地形代表性误差与引入前相比，风场均方根误差得到整体改进；位温和露点的均方根误差在前期表现并不稳定，在后期有所改进；预报阶段前24 h累计降水与后24 h累计降水TS评分在整体上均有所提高。新方案能够减少高度差异对同化效果的影响。

英文摘要:

      In order to more efficiently assimilate surface Automatic Weather Station (AWS) data, a new scheme based on the Ensemble Square Root Filter (EnSRF) is proposed for further improvement via solving the negative impact of assimilation results caused by elevation differences between observation sites and the model surface. Terrain Error of Representativeness (TER) for potential temperature and dewpoint temperature are added to temperature and dewpoint temperature errors of surface observation data assimilation in the WRF-EnSRF system, and a numerical simulation of a heavy rain event in Beijing-Tianjin-Hebei region in 2016 has been conducted. Results show that the root mean square error (RMSE), the threat score (TS) and various elements simulated in the first 13 h generally have been improved. With the TER being added, the RMSE of the wind field is improved in general, whereas the RMSE of potential temperature and dewpoint temperature are unstable in the earlier stage but they are improved in the later stage; TS of the first 24 h and the 24-48 h accumulated rainfall are overall improved compared with the results without TER. Thereby, the new scheme is able to reduce the negative impact of assimilation results caused by elevation differences.