摘要
为了更好的利用降水历史先验概率分布函数信息修订的集合概率预报,基于贝叶斯概率决策理论,分别使用1981~2010年西江流域降雨量实况资料和2016年4~9月欧洲集合预报产品作为先验信息,通过建立基于贝叶斯的西江流域大雨概率预报模型,用以订正欧洲集合概率预报产品,并进行评分检验,从预报试验的结果来看:基于贝叶斯方法修正的集合概率预报产品对于汛期大雨的预报能力有一定的提高;除了9月份外,汛期其他月份在24~72h预报时效内的BS评分均优于集合概率预报,并可以在一定程度上减小大雨预报空报的可能性;前汛期TS评分高值区分布存在月际差异,4月至6月随着降水强度和范围的扩大,大部分流域评分升高;此项工作可为预报员开展西江流域强降雨预报提供参考依据。
In order to make better use of the revised ensemble probability forecast based on the information of the historical prior probability precipitation distribution function, based on the Bayesian probability decision theory, this paper used the ac- tual rainfall data from 1981 to 2010 in Xijiang River Basin and the European ensemble forecast products from April to Septem- ber in 2016 as the prior information, and established Bayes based probability prediction model for heavy rain in Xijiang River Basinto correct the European ensemble probability prediction products and to carry out the grading test. From the results of forecasting experiments, it can be seen that the ensemble probability forecasting product modified by Bayesian method can im- prove the forecasting ability of heavy rain in flood season.Except for September, the BS scores of other months in flood season within 24-72 hours are better than those of ensemble probability forecast, and the possibility of false heavy rain forecast can be reduced to a certain extent.There is a monthly difference in the distribution of the TS high score areas in pre-rainy season; with the increase of precipitation intensity and scope from April to June, the scores of most river basins increase. In conclusion, this work can provide a reference for forecasters to carry out the prediction of heavy rainfall in the Xijiang River Basin.
作者
曾鹏
钟利华
李勇
Zeng Peng;Zhong Lihua;Li Yong(Guangxi Meteorological Service Center,Nanning Guangxi 53002)
出处
《气象研究与应用》
2018年第3期21-25,I0003,共6页
Journal of Meteorological Research and Application
基金
广西气象服务中心重点科研项目(201501)
关键词
西江流域
集合概率预报
贝叶斯方法
大雨
检验
Xijiang River Basin
ensemble probability prediction
Bayes method
heavy rain
inspection
