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作 者:Chenwei SHEN Qingyun DUAN Wei GONG Yanjun GAN Zhenhua DI Chen WANG Shiguang MIAO
机构地区:[1]State Key Laboratory of Earth Surface Processes and Resource Ecology,Faculty of Geographical Science,Beijing Normal University,Beijing 100875 [2]State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering and College of Hydrology&Water Resources,Hohai University,Nanjing 210098 [3]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081 [4]South China Botanical Garden,Chinese Academy of Sciences,Guangzhou 510650 [5]Institute of Urban Meteorology,China Meteorological Administration,Beijing 100081
出 处:《Journal of Meteorological Research》2020年第3期601-620,共20页气象学报(英文版)
基 金:Supported by the Chinese Academy of Sciences Strategic Pioneering Program(XDA20060401);China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002);National Basic Research Program of China(2015CB953703);Intergovernment Key International S&T Innovation Cooperation Program(2016YFE0102400).
摘 要:Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting(WRF)model users.This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area.The ensemble is created by using statistical techniques and some heuristics.An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling(LHS).Then,after several rounds of screening,a final ensemble of 40 combinations were chosen.The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics,including threat score(TS),Brier score(BS),relative operating characteristics(ROC),and ranked probability score(RPS).The results show that TS of the final ensemble improved by 9%-33%over that of the initial ensemble.The reliability was improved for rain≤10 mm day^-1,but decreased slightly for rain>10 mm day^-1 due to insufficient samples.The resolution remained about the same.The final ensemble forecasts were better than that generated from randomly sampled scheme combinations.These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.
关 键 词:ensemble precipitation forecast Weather Research and Forecasting(WRF)model MULTI-PHYSICS verification BOOTSTRAPPING
分 类 号:P456.7[天文地球—大气科学及气象学]
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