基于GRAPES-MESO模式的非静力三维变分同化方案性能分析  

Evaluation on Nonhydrostatic Three-Dimensional Variational Data Assimilation Scheme of GRAPES-MESO Model

作　　者：王叶红[1] 赖安伟[1] 林春泽[1] 王明欢[1] 

机构地区：[1]中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,湖北武汉430074

出　　处：《高原气象》2013年第3期689-706,共18页Plateau Meteorology

基　　金：公益性行业(气象)科研专项(GYHY200906010;GYHY201206003);国家自然科学基金项目(41075034;40975025;41075038)共同资助

摘　　要：利用国家气象中心中尺度业务模式GRAPES-MESO V3.0,以2009年6—8月为例,用T213模式12h预报场为大尺度模式背景场,进行了非静力GRAPES-3DVAR系统同化(3DVAR试验)与不同化(CNTL试验)探空资料的两组数值试验,并对降水、高度、温度、风场和相对湿度场进行了统计检验和典型个例分析,探讨了非静力三维变分同化系统的性能。结果表明:(1)除700hPa相对湿度场外,3DVAR试验850,700和500hPa初始温、压、湿和风场均方根误差均比CNTL试验有所增加,其中3DVAR试验对500hPa初始温、压和风场的负贡献最为显著。随着预报时间延长,3DVAR试验对500hPa风场预报的负贡献较为明显;(2)对整个模式积分区域平均状况而言,3DVAR试验对模式0～24h和12～36h降水预报中大多数降水量级的TS评分都小于CNTL试验;(3)CNTL试验对小雨、中雨的预报范围比较适中,对大雨、暴雨及大暴雨的预报范围均小于实况,且随着降水量级的增大,预报范围较实况偏小的程度加剧;3DVAR试验则对各量级降水范围预报偏小的程度均大于CNTL试验,特别是中雨以上强度的降水;(4)CNTL试验较3DVAR试验能更好地模拟雨带的分布、演变特征和降水强度的变化;(5)CNTL试验较好地再现了长江中下游、华南、华北、东北、西南地区东部和华东平均降水率的逐日演变及峰值、谷值模拟,但强度普遍偏弱;3DVAR试验模拟的各区域日平均降水率的演变趋势与CNTL试验类似,但强度弱于CNTL试验,因而其与实况的差异进一步加大;(6)两个试验与观测均有较好的一致性,CNTL试验的一致性略好于3DVAR试验;对<1.5mm·d-1的降水,两个试验都倾向于过多地预报了降水量;对>1.5mm·d-1的降水,两个试验预报的都偏弱;对1.5mm·d-1以下的降水,两个试验的预报水平相当;对1.5mm·d-1以上的降水,CNTL试验的预报优于3DVAR试验;(7)对2009年6—8月逐日降水预报结果进行了考察,整体而言,CNTL试验能较With the mesoscale operational numerical model GRAPES-MESO V3.0 from National Meteoro- logical Center of China Meteorological Administration and 12 h T213 forecasts as background field, two numerical experiments with （Exp. 3DVAR） and without （Exp. CNTL） assimilating radio-soundings using the nonhydrostatics GRAPES-3DVAR are conducted from June to August 2009 to investigate the perform- ance of GRAPES-3DVAR. The statistical verifications of precipitation, geopotential height, temperature, wind and relative humidity and typical weather events are conducted. The results indicate that： （1） The root mean square errors of initial temperature, geopotential height, relative humidity and wind field on 850, 700 and 500 hPa in Exp. 3DVAR are morer than that of in Exp. CNTL, except for 700 hPa relative humidity. Exp. 3DVAR has a significant negative contribution to initial temperature, geopotential height and relative humidity on 500 hPa. And Exp. 3DVAR has an obvious negative contribution to 500 hPa wind forecast with forecasting time going on. （2） As to the area mean of whole model domain, TS scores at dif- ferent orders in 0~24 h and 12~36 h rainfall forecasts in Exp. 3DVAR are mostly less than that of in Exp. CNTL. （3） The coverage of light rain and moderate rain forecasted by Exp. CNTL are close to the observation, while that of heavy rain, torrential rain, heavy torrential rain are less than that of the obser- vation. Moreover, with the increase of rainfall, the changing small degree of forecast range is more than the observation. The forecast area of rainfall at different orders in Exp. 3DVAR are worse than that of in Exp. CNTL, especially for those above moderate rain. （4） The distribution, evolution and intensity varia- tions of rain region in Exp. CNTL are better than that of Exp. 3DVAR. （5） The daily evolution, peak and valley values of the simulated average rainfall rate in the mid-lower reaches of Yangtze River, South China, North China, Northeast China, east of Southwest China and

关 键 词：GRAPES-3DVAR同化系统 GRAPES-MESO V3 0模式 预报效果检验 

分 类 号：P456.7[天文地球—大气科学及气象学]