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基于贝叶斯理论的单站地面气温的概率预报研究 预览 被引量:11

On the probabilistic forecast of 2 meter temperature of a single station based on Bayesian theory
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摘要 基于贝叶斯理论,建立了将确定性预报向概率预报转换的基本模式,并利用TIGGE资料中欧洲中期天气预报中心(ECMWF)地面气温预报资料及地面气温观测资料,对概率化后的预报进行了评估与释用.结果表明,概率化后的预报结果不但能提供丰富的预报产品,而且所提供的预报均值优于原始的确定性预报.应用贝叶斯模式平均法(BMA)将中国气象局(CMA)、美国国家环境预报中心(NCEP)和ECMWF 3个模式的预报结果进行多模式集成,得到了更为合理的概率分布,其中分布的均值可作为模式的预报结果,方差和置信区间反映了预报量的可变范围.因此,基于贝叶斯预报模式的概率预报相对于确定性预报,不但能够提供更高精度的预报,而且能提供更全面的预报信息.BMA集成预报结果不但优于集合平均预报,而且还能定量描述预报的不确定性.利用ECMWF预报中心51个预报成员进行集成贝叶斯概率预报试验,发现BMA预报融合了各成员对预报不确定性的描述,还对概率预报的均值进行了调整,使之与观测值更为接近.BMA预报的概率密度分布更能反映大气的真实分布情况. Based on Bayesian theory, a basic model of the probabilistic forecast was established out of the deterministic forecast. The probability forecast was evaluated and put into use by using European Centre for Medium-Range Weather Forecast (ECMWF) ensemble forecast of 2 m temperature taken from the TIGGE data and the observed data.The results show that the probability forecast can provide a plenty of forecast products as well as better forecast average value than the original deterministic fore- cast.The Bayesian mode/averaging(BMA) approach was applied to conduct the multimodel ensemble probabilistic forecast by using China Meteorological Administration (CMA), US National Centers for Environmental Prediction (NCEP) and ECMWF ensemble forecast data. More reasonable probability distribution was acquired from the BMA forecast, the distribution average value can be used as the mod-el forecast, and the standard deviation and the confidence interval reflect the variable range of the fore- cast.Therefore, the probabilistic forecast based on the Bayesian forecast model is able to provide more accurate forecast as well as more comprehensive forecast information.The BMA ensemble forecast is su- perior to the ensemble mean forecast in terms of its forecast accuracy, and it can quantitatively provide the uncertainty of the forecast as well.The BMA probabilistic forecast experiment was carried out by u- sing 51 members of the ECMWF ensemble forecasts.The results show that the BMA forecast merges the description of uncertainty of each ensemble member, and it adjusts the average value of probabilistic forecast in order to make it closer to the observed value. The probability density distribution (PDF) of BMA forecast can better reflect the realistic distribution of atmosphere.
作者 智协飞 李刚 彭婷 ZHI Xie-fei ,LI Gang ,PENG Ting ( 1. School of Atmospheric Sciences, NUIST, Nanjing 210044, China; 2. Key Laboratory of Meteorological Disaster( NUIST), Ministry of Education, Nanjing 210044, China 3.Guizhou Meteorological Observatory, Guiyang 550002, China)
出处 《大气科学学报》 CSCD 北大核心 2014年第6期740-748,共9页 Journal of Nanjing Institute of Meteorology
基金 公益性行业(气象)科研专项(GYHY200906009) 江苏高校优势学科建设工程资助项目(PAPD)
关键词 预报不确定性 概率预报 贝叶斯模式平均 forecasting uncertainty probabilistic forecast Bayesian model averaging (BMA)
作者简介 通信作者:智协飞,博士,教授,博士生导师,研究方向为季风动力学、数值天气预报,zhi@nuist.edu.cn.
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