多普勒雷达资料同化在“7.21”北京特大暴雨个例中的应用

基于WRF(Weather Research Forecast)模式和GSI(Gridpoint Statistical Interpolation)同化系统,研究了同化4部多普勒雷达探测资料对"7.21"北京特大暴雨过程中降水预报的改善作用。GSI系统直接同化径向风,而采用云分析的方式间接同化反射率。2012年7月20日21时—21日00时(世界时)雷达探测资料同化试验采用30 min循环同化径向风和反射率资料。结果表明,循环同化雷达探测资料改善了短时(0—6 h)和短期(0—24 h)降水预报,ETS评分提高了约0.2。同化反射率资料增加了初始场的水凝物,改善了温度场分布,直接影响了降水的形成,同时还使650—250 hPa位势高度的均方根误差平均降低了8 gpm。直接同化径向风资料对中尺度风场产生了一定影响。ETS评分结果表明:同化反射率资料的效果要优于同化径向风。

Application of assimilating Doppler weather radar data in the "7.21" Beijing excessive storm

Based on the WRF (Weather Research Forecast) model and the GSI (Gridpoint Statistical Interpolation) assimilation system, the impact of the assimilating four Doppler weather radars (DWR) reflectivity and radial velocity (V_r) for quantitative precipitation forecasts (QPFs) of the "7.21" Beijing excessive storm have been examined. The GSI directly assimilates V_r, while indirectly assimilates the reflectivity through the cloud analysis. The radar data are assimilated every 30 min from 21:00 UTC 20 Jul to 00:00 UTC 21 Jul 2012. The numerical experiments demonstrate that the DWR data assimilation can improve nowcast and short-term precipitation forecasts, whose ETS scores averagely increase by 0.2. The reflectivity data are used primarily in a cloud analysis that retrieves the amount of hydrometeors and adjusts the in-cloud temperature and moisture which have direct influence on generating precipitation. The assimilating reflectivity makes the root-mean-square error (RMSE) of the geopotential height averaged over between 650 and 250 hPa decreases by 8 gpm. The direct assimilation of DWR V_r in GSI exerts a sure influence in mesoscale wind fields. Through the quantitative verification of the simulation results, the forecast with reflectivity assimilation is better than that with the V_r assimilation.