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《Meteorological Monthly》 2013-01
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Study on the Method of Rainfall Ensemble Probability Forecast Based on Bayesian Theory and Its Preliminary Experiments

HAN Yanhong~1 JIAO Meiyan~2 CHEN Jing~3 CHEN Fajing~3 1 School of Atmospheric Science.Nanjing University of Information Science and Technology,Nanjing 210044 2 China Meteorological Administration,Beijing 100081 3 CMA Numerical Prediction Centre,Beijing 100081  
The paper applies BPO(Bayesian Processor of Output) method based on Bayesian theory to the method of rainfall ensemble probability forecast.Using ensemble prediction data and historical observational data,we develop a rainfall probability forecast model,and then revise a set of precipitation predicted value into a set of Bayesian precipitation probability forecast in the form of continuous probability distribution or continuous probability density.Besides,we obtain a group value of Informativeness Score(IS), which can express the prediction ability of each ensemble member.Furthermore,we fuse the probability forecast results of each member into an integration Bayesian precipitation probability forecast on the basis of IS and test the results with Continuous Ranked Probablity Score(CRPS).Experiment results show that the reliability of integration Bayesian precipitation probability forecast is higher than ensemble direct probability forecast.
【Fund】: 国家自然科学基金(41075035)资助
【CateGory Index】: P456.7
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