逐时云迹风资料同化对暴雨预报的模拟试验

文中基于不同的云迹风同化方案,用GRAPES模式对2005年7月11-12日长江中下游一次暴雨强降水过程进行了云迹风资料同化试验及数值模拟,通过对比分析不同方案所得的分析场及预报场的差异,研究逐时云迹风资料三维变分同化对分析场及暴雨预报的影响.首先,根据连续性原理及双通道各层次云迹风资料的误差分析,分3个步骤对7月11日00:00-12:00 UTC共12个时次的双通道云迹风资料进行了初步的质量控制;然后,把经过质量控制的云迹风资料放入基于GRAPES 3D-VAR三维变分同化方案开发的逐小时循环同化系统中进行同化,将得出的分析场与单一时次未经质量控制的云迹风资料同化得出的分析场进行对比,探讨了逐时云迹风资料同化对数值预报分析场的影响;最后,把同化后的分析场作为初始场,用 GRAPES模式对 2005年7月11-12日长江流域暴雨过程做24 h降水预报试验,分析两个同化方案所模拟得到的预报场的差异.结果表明,经资料的筛选、同经纬度单点通道的选择及资料的稀疏化3个步骤控制后,各层次云迹风资料的误差有明显减小;加入经质量控制的逐时云迹风资料其三维变分同化可以提高分析场中风压场及水汽场的质量;而且在暴雨预报试验中可以相对更准确地预报暴雨落区及雨强.

Hourly assimilation of cloud motion winds and its impact on torrential rain forecast

Based on the different assimilation schemes of cloud motion wind data, the effects of hourly data assimilation in GRAPES model on the prediction of a torrential rain process over low-mid Yangtze reaches on 11-12 July 2005 is investigated through the comparative analysis of the simulated results. Firstly, cloud motion winds at different levels were selected with three steps of quality control based on principles of temporal and spatial continuity, then, incorporated into hourly 3-D variational data assimilation. The initial fields assimilatedwith scheme 1 (using the quality-controlled cloud motion winds) and 2 (using one time level raw cloud motion winds) were compared with the NCEP reanalysis data, and impacts of cloud motion winds on the assimilated initial fields are discussed. At last, the 24 -hour precipitation of the torrential rain process on 11-12 July 2005 was predicted using the assimilated fields as the initial fields. Itwas found from analyses of the assimilated initial fields and predicted 24-hour precipitation fields, that three steps of quality control obviously reduced errors of the cloud motion vector data at various levels, assimilation of the quality controlled cloud motion winds into the initial fields improved the qualities of initial wind, geopotential height, and moisture fields, thus yielding the forecasts of more accurate location and intensity of the torrential rain.