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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 耿焕同 谢佩妍 张录军

气象2017,Vol.43Issue(8):1005-1015,11.
气象2017,Vol.43Issue(8):1005-1015,11.DOI:10.7519/j.issn.1000-0526.2017.08.011

基于WRF-CALMET的精细化方法在大风预报上的应用研究

Research on Application of Fineness Method Based on WRF-CALMET in Gale Forecasting

李俊徽 1耿焕同 2谢佩妍 1张录军3

作者信息

  • 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
  • 折叠

摘要

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.

关键词

WRF/CALMET/动力降尺度/精细化

Key words

WRF/CALMET/dynamic downscale/refining

分类

天文与地球科学

引用本文复制引用

李俊徽,耿焕同,谢佩妍,张录军..基于WRF-CALMET的精细化方法在大风预报上的应用研究[J].气象,2017,43(8):1005-1015,11.

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 (41201045)

气象

OA北大核心CSCDCSTPCD

1000-0526

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