降水资料同化在梅雨锋特大暴雨个例模拟中的应用研究

A study of application of the precipitation data assimilation technique to numerical simulation of an excessive meiyu front rainfall event

  • 摘要: 利用暴雨数值预报模式AREM,以2009年6月29日发生在长江中下游地区的一次梅雨锋特大暴雨过程为例,以NCEP预报场为背景场,首先开展了不同化任何资料(无同化试验)以及分别利用GRAPES-3DVAR同化系统和局地分析预报系统(LAPS)同化地面、探空资料的3组数值试验,然后进一步开展了降水资料一维变分同化方法在GRAPES-3DVAR同化系统及LAPS系统中二次同化的效果研究,结果表明:(1)采用GRAPES-3DVAR系统同化地面、探空资料的数值试验,其降水预报效果不如无同化试验结果;而采用LAPS资料同化系统同化地面、探空资料的数值试验,其降水预报效果优于无同化试验结果,即就本个例而言,GRAPES 3DVAR同化系统对背景场的修正为负效果,LAPS同化系统对背景场的修正为正效果。(2)降水资料1DVAR方法在GRAPES 3DVAR同化系统中的应用,对物理量场有重要影响,使雨带上空变得更暖更湿,天气系统的配置更利于降水发生,中尺度系统的演变更有利于模拟出与实况更加接近的雨带位置、强度、中尺度结构特征,因而极大地改善了降水模拟效果,其模拟的1、6、24 h累积降水量位置、强度、中尺度结构特征都有较明显改善。(3)降水资料1DVAR方法在LAPS系统中的应用同样改善了降水预报效果,使雨带落区位置更加接近实况。

     

    Abstract: With the heavy rainfall numerical forecast model AREM,three numerical experiments with and without the surface and sounding data assimilation were carried out respectively to simulate an excessive meiyu front rainfall event occurred in the middle and lower reaches of the Yangtze River on 29 June 2009, using the data assimilation system GRAPES 3DVAR and LAPS (Local Analysis Prediction System) with NCEP forecasts as the background field. Then the assimilation effects of the vertical profile data retrieved from the precipitation data 1DVAR method (by assimilating them in the GRAPES 3DVAR and LAPS respectively) were investigated. The results indicate that: (1) the rainfall forecasts from the sounding assimilation experiment using the GRAPES-3DVAR/LAPS are worse/better than that from the no data assimilation experiments. As to this excessive rain case, the background field modification by the GRAPES 3DVAR/LAPS data assimilation system gives a negative/positive effect on the rainfall forecast; (2) the assimilation of the vertical profiles retrieved from the precipitation data 1DVAR method in the GRAPES-3DVAR data assimilation system has an important effect on the physical field, with the air above the rain belt becoming more moist and warmer and the weather system configuration being more favorable to the precipitation and the development of the mesoscale system, which are more beneficial to simulating a location and intensity of the rain belt and mesoscale system structure closer to the observation. Therefore it to a large degree improved the precipitation simulation, with the location and intensity of 1 h, 6 h, 24 h accumulated precipitation and the mesoscale structure feature obviously improved; (3) the application of the precipitation data 1DVAR method in the LAPS system also improved the rainfall forecasting and made the rain belt location be closer to the observation. 

     

/

返回文章
返回