文章摘要
WRF模式物理参数化方案对一次局地大暴雨预报的影响研究
Impact study of parameterized physical processes of WRF model to a local torrential rainfall
投稿时间:2019-09-13  修订日期:2019-11-07
DOI:
中文关键词: WRF模式  资料同化  物理参数化方案  大暴雨  预报影响
英文关键词: WRF model  data assimilation  microphysics process  torrential rainfall  forecast impact
基金项目:
作者单位E-mail
盛春岩 山东省气象科学研究所 sdqxscy@126.com 
荣艳敏 山东省气象科学研究所  
曲巧娜 山东省气象科学研究所  
范苏丹 山东省气象科学研究所  
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中文摘要:
      基于WRF(Weather Research and Forecasting)模式及其3dvar(3-Dimentional Variational)资料同化系统,采用36、12、4km嵌套网格进行快速更新循环同化和不同物理参数化方案对比试验,对2011年5月8日鲁中一次局地大暴雨过程不同物理参数化方案和不同初始场对降水预报的影响进行了研究。结果表明,快速更新循环同化地面观测资料对于降水落区起到了关键作用,不同物理参数化方案对降水强度预报起到了关键作用。Lin方案和WSM-6微物理参数化方案对降水预报均较好,Lin方案降水预报较WSM-6方案略强。4km网格使用K-F积云对流参数化方案或不使用积云对流参数化方案,预报的降水均较好。4km网格使用旧的K-F积云对流参数化方案,预报的近地层大气风场偏弱,是造成模式降水预报偏弱的主要原因。模式不同的微物理方案和积云对流参数化方案主要改变了大气风场等动力条件预报,从而影响模式降水强度预报。
英文摘要:
      Using the WRF (Weather Research and Forecasting) model and it’s 3DVAR (3-Dimentional Variational) data assimilation (DA) system, comparative experiments on a local torrential rain event in middle Shandong on 8 May 2011 are carried out adopting 3-hourly data assimilation cycle of 36, 12 and 4 km nested grid to test the effect of initial fields and parameterized processes on rainfall forecast. Results show that surface data assimilation by 3-hourly assimilation cycle is key important for the location of rainfall forecast. Parameterized process mainly impacts the rainfall intensity forecast. Lin and WSM-6 microphysics processes are both better for rainfall forecast and the Lin process can give stronger rainfall amount forecast. K-F cumulus process or no cumulus process at 4 km grid can both give better rainfall forecast. The wind field forecast at low level is much weaker when the 4 km grid uses previous K-F cumulus scheme, which is the main cause why the rainfall amount forecast is much weak than other scheme and observation. Different microphysics and cumulus processes mainly impact the wind field forecast, and thereby impact the rainfall intensity forecast.
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