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文章摘要
引用本文:王佳,梅钦,陈钰文.2017.WRF模式不同微物理方案水凝物的预报能力检验与集成试验.气象,43(5):552-559.
WRF模式不同微物理方案水凝物的预报能力检验与集成试验
Performance Verification and Ensemble Experiments of Hydrometeors Forecasting by Different Microphysical Schemes in WRF Model
投稿时间:2016-10-23  最后修改时间:2017-01-06
DOI:10.7519/j.issn.1000-0526.2017.05.004
中文关键词: WRF模式,微物理方案,水凝物,TRMM/TMI Version 7,定量检验,集成试验
英文关键词: WRF model,microphysical schemes,hydrometeors,TRMM/TMI Version 7,quantitative verification,ensemble experiments
基金项目:国家自然科学基金面上项目(41575104)、江苏气象科研基金(KM201403)和江苏气象科研基金(Q201516)共同资助
作者单位
王佳 江苏省气候中心南京 210000;南京信息工程大学南京 210044 
梅钦 南京信息工程大学南京 210044 
陈钰文 江苏省气候中心南京 210000 
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中文摘要:
      文章利用TRMM(Tropical Rainfall Measuring)卫星TMI(TRMM Microwave Imager)探测反演的云水、雨水和冰水新版改进资料,定量化检验了WRF(Weather Research and Forecasting) 模式6种微物理方案(Lin,WSM6,Thompson,Morrison 2 mom,CAM5.1,NSSL 2 mom)对江苏近海及周边海域上空云中水凝物的预报能力。19次个例统计检验结果表明,6种微物理方案基本都能预报出水凝物的量级及大致范围,对于云水的预报,除NSSL 2 mom方案误差较大外,其余5种方案误差均较小;CAM5.1方案对雨水含量较大时的预报效果较好,但对于雨水含量较小时的预报误差较大;Lin方案对于冰水的预报效果较好,更接近于实况。在此基础上,选用等权集成(EMN)和消除偏差集成(BREM)两种方法开展集成预报试验,结果发现,两种方法均可以减小预报的误差,且消除偏差集成的方法比等权集成的改进效果更好。
英文摘要:
      The new improved retrieval data of cloud water, rain water and ice water detected by the TRMM (Tropical Rainfall Measuring) satellite TMI (TRMM Microwave Imager) are applied to quantitatively verify the performance of six microphysical schemes of WRF (Weather Research and Forecasting) model for hydrometeors over Jiangsu offshore and surrounding areas. The statistical verification results of 19 cases show that the six schemes can predict the magnitude and approximate range of the hydrometeors. For cloud water content forecasting there is a large deviation in NSSL 2 mom scheme but little deviation in other schemes. CAM5.1 scheme has a better performance in forecasting rain water when the content is larger, but worse performance when the content is small. Lin scheme is better in forecasting ice water than other schemes compared to the observation. On the basis of quantitative verification, the ensemble forecasting experiments are carried out by the two methods of ensemble mean (EMN) and the bias removed ensemble mean (BREM). The results show that both methods can reduce the error of prediction, and the BREM is better than the EMN.
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