本文基于中国T213集合预报系统资料，借鉴欧洲中期天气预报中心极端天气预报指数的数学处理方案，在分析T213模式数据特征的基础上，研究了T213集合预报系统极端天气预报指数的数学处理方法，建立了适合T213集合预报模式的极端天气预报指数，并利用该指数对2008年1月极端低温天气进行了预报试验和评估检验，分析模式气候累积概率分布生成方法对极端天气预报指数的影响，得到以下结论：（1）确定了利用T213集合预报系统所有预报成员生成的模式气候累积概率分布计算极端天气预报指数；（2）利用TS评分确定极端天气预报指数发布极端低温预警信号的阈值为-0．3并进行预报试验。试验结果表明，极端天气预报指数对极端低温天气具有较好的识别能力，可提前3-5天发出极端低温预警信号。利用相对作用特征曲线对极端天气预报指数识别极端天气的技巧进行评估检验，检验结果显示，基于T213集合预报生成的极端天气预报指数对极端低温的预报存在正的识别技巧，随着预报时效的延长，识别技巧逐渐降低。（3）评估不同模式气候累积概率分布对极端天气预报指数识别极端低温天气技巧的影响，结果表明：产生模式气候累积概率分布的模式数据误差一致性是关键因素。%Abstract: Based on the T213 global ensemble prediction system (EPS), a new ensemble forecast prod- uct extreme forecast index (EFI), which combines the cumulative distribution functions (CDF) de- rived from climate and the EPS forecast, is designed. EFI is an index which can measure the continuous differences between the climate CDF and the EPS forecast CDF. When the EFI gets close to 1 or -- 1, ex- treme weather is likely to occur. Because EFI is calculated by both climate CDF and the EPS forecast CDF, then there is negative deviation in T213 EPS forecast. In order to ensure the result of the EFI is unaffected by the model error, we choose the T213 EPS model data to form the climate CDF. Next take the 2 m temperature for example, we use EFIa to forecast the extreme low temperature inJanuary 2008. The results show when the threshold of EFI3 that gives extreme low temperature signal is set to --0.3, most areas of extreme low temperature can be predicted on EFI3 map in 3 to 5 days in ad- vance. We should note that the areas of extreme low temperature do not agree very well with where the EFI3 signals the extreme low temperature. It may be there are some deficiencies in the present formula of EFI3 and the EFI3 is less sensitive to extreme value of the EPS forecast. Later, a relative operating characteristic (ROC) curve is used to verify F, FI3. In all three leading times shows that the averaged ROC curves valid for 2 m temperature EFIa during 25 29 January 2008 are above the diagonal, the hreas of the averaged ROC of three leading times are 0. 680, 0. 657, and 0. 542, re- spectively. This means that the EFI3 is skillful in forecasting the extreme low temperature, with the lead- ing time growing, the EFI3 is less skillful. Finally, another model climate cumulative distribution function is used to generate the EFI3 and a comparison of skill derived from two kinds of EFI3 is presented. The results show that the skill of EFI3 generated by the former model climate is slightly higher than that of EFI3 generated by the latter model climate. The reason may be that the initial field of the former model climate data is derived from the same as similation scheme while the initial field of the latter model climate data is derived from two kinds of schemes. Therefore the more consistent the model data are, the more equitable the cumulative probability distribution of model climate is. We need not to collect much model data if they be not consistent.
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