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气象:2017,43(8):1005-1015
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基于WRF-CALMET的精细化方法在大风预报上的应用研究
李俊徽1,耿焕同2,谢佩妍1,张录军3
(1 南京信息工程大学大气科学学院, 南京 210044 2 南京信息工程大学滨江学院, 南京 210044 3 南京大学大气科学学院, 南京 210093)
Research on Application of Fineness Method Based on WRF-CALMET in Gale Forecasting
LI Junhui1,GENG Huantong2,XIE Peiyan1,ZHANG Lujun3
(1 School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044 2 Binjiang College, Nanjing University of Information Science and Technology, Nanjing 210044 3 School of Atmospheric Science, Nanjing University, Nanjing 210093)
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投稿时间:2016-07-29    修订日期:2017-03-31
中文摘要: 针对当前风场预报普遍精细程度偏低,动力降尺度改善风场预报应用偏少的问题,利用CALMET模式的动力诊断模块,结合高分辨率的地形资料对WRF模式输出的风场预报数据进行了动力降尺度处理,进而提高风场的精细化预报。其方法充分借助地形动力学效应,使大尺度的近地层风场经过坡度流与阻碍效应等修正后得到更精细的风场且体现出与局地地形相符的特征。试验部分以广东省为研究区域,并选取个例用观测资料与陆地同化系统资料对模拟结果进行对比检验,结果表明动力降尺度之后的风场较之前更为精细且含有更多与地形相关的信息;与观测资料对比的总体相关性较好,误差也更小,与陆地同化系统资料对比变化趋势基本一致,且误差不大,这表明WRF CALMET方法不仅能有效地提高风场预报数据的时空分辨率而且其结果更趋近于真实,对风场的精确预报具有重要的参考价值。
Abstract:Aiming at the problem that the resolution of the current wind forecasts is not high and dynamic downscaling method is less applicated to wind forecasting, this paper used the diagnostic wind field function of CALMET model and high spatial resolution terrain data to dynamically downscaling the wind forecast data outputted by WRF model. The main theory is kinematic effect of terrain. After the large scale surface wind field was adjusted by slope flows and terrain blocking effects, the wind field became finer and showed the feature corresponding to terrain. In the experiment part, we took Guangdong Province as study region, using observation data and CLDAS (CMA land data assimilation system) data to examine the simulation result by a case. The result indicated that the resolution of wind field was more precise after downscaling and contained more sophisticated information related to terrain. Correlation coefficients between the simulation and observation results of wind speed were at a high level and the RMSE (root mean square error) was much smaller. The comparison between simulation and CLDAS showed the similar result. To sum up, the combination of WRF CALMET is an outstanding downscaling method which could effectively improve the temporal spatial resolution of wind forecast data. Meanwhile it might be able to make the result closer to observation. Thus, this method could probably be a reference for wind forecasting in the future.
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基金项目:国家自然科学基金项目(41201045)、江苏省自然科学基金(BK20151458)和江苏省青蓝工程(2016)共同资助
引用文本:
李俊徽,耿焕同,谢佩妍,张录军,2017.基于WRF-CALMET的精细化方法在大风预报上的应用研究[J].气象,43(8):1005-1015.
LI Junhui,GENG Huantong,XIE Peiyan,ZHANG Lujun,2017.Research on Application of Fineness Method Based on WRF-CALMET in Gale Forecasting[J].METEOROLOGICAL MONTHLY,43(8):1005-1015.