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投稿时间：2015-12-29 修订日期：2016-05-28
投稿时间：2015-12-29 修订日期：2016-05-28
中文摘要: 本文采用百分位法对观测极端降水的阈值进行定义，根据贝叶斯理论探讨了极端降水的概率预报方法，进行了贝叶斯极端降水模拟概率预报试验和检验。以观测和模式极端降水阈值分别进行的贝叶斯概率预报试验结果表明：以观测极端降水为阈值时，先验概率与后验概率的极端降水空报情况较多，主要降水雨带的概率预报也偏强；而以模式极端降水为阈值时，两者的空报均较少，且对主要雨带也起到了明显的预警作用。对比两种阈值取法下的先验概率和后验概率的极端降水预报，前者的概率预报值较后者的更小。检验结果表明，经过贝叶斯方法修订后的极端降水预报，提高了极端降水产生的正确率，但空报也有所增加。
Abstract:The threshold of extreme precipitation is defined by the method of percentile method in this paper. Based on Bayesian theory, probabilistic forecasting of extreme precipitation is discussed and we do the Bayesian extreme precipitation experiment and verification. Experiments which are based on observational and modal extreme precipitation threshold show the following conclusions. False prediction of extreme precipitation is proved to be more when the threshold is based on the observational extreme precipitation. The forecast of main rain band is stronger. False prediction is less while the threshold is based on the modal extreme precipitation and the early warning to the heavy rain is obvious as well. By contrast, the probabilistic forecasting based on the prior probability is smaller. The verification result shows that the accuracy of the extreme precipitation is improved by the Bayesian method, but the false prediction ratio has increased.
keywords: Bayesian theory, extreme precipitation, ensemble forecast, “7.21” torrential rain in Beijing
文章编号： 中图分类号： 文献标志码：
基金项目:中国气象局预报员专项（CMAYBY2014 013）资助
| 作者 | 单位 |
| 张宇彤 | 大连市气象台，大连 116001 |
| 矫梅燕 | 中国气象局，北京 100081 |
| 陈静 | 中国气象局数值预报中心，北京 100081 |
| 夏葳 | 大连市气象信息中心，大连 116001 |
| 宋玉强 | 大连市气象台，大连 116001 |
引用文本：
张宇彤,矫梅燕,陈静,夏葳,宋玉强,2016.基于贝叶斯方法的极端降水概率预报试验[J].气象,42(7):799-808.
ZHANG Yutong,JIAO Meiyan,CHEN Jing,XIA Wei,SONG Yuqiang,2016.Probabilistic Forecasting of Extreme Precipitation Experiment Based on Bayesian Theory[J].Meteor Mon,42(7):799-808.
张宇彤,矫梅燕,陈静,夏葳,宋玉强,2016.基于贝叶斯方法的极端降水概率预报试验[J].气象,42(7):799-808.
ZHANG Yutong,JIAO Meiyan,CHEN Jing,XIA Wei,SONG Yuqiang,2016.Probabilistic Forecasting of Extreme Precipitation Experiment Based on Bayesian Theory[J].Meteor Mon,42(7):799-808.