中文摘要：

怎么获得快生长的错误，它比得上实际预报生长错误，是在整体预报(EF ) 的一个关键问题。方法，生长模式(BGM ) 繁殖，它被用来在 NCEP 为中等范围的 EF 产生不安，在分析周期模仿快生长的错误的发展，并且在捕获的一种合理选择正在种错误模式，特别为由 BGM 的极端天气。理想的 supercell 暴风雪，由天气研究预报模型(WRF ) 模仿了，在 1977 年 5 月 20 日发生在中央俄克拉何马。这模拟被用来在 meso-seale 学习 BGM 方法的申请强壮的对流整体预言系统(EPS ) 。我们由不同 pertu-bation 方法比较了 EPS 的预报技巧，象 Monte-Carlo 和 BGM 一样。结果证明整体一般水准与统计意思基于 Monte-Carlo 预报比单人赛确定的预言优异，但是方法的一个不太有活力的过程比期望导致更小的传播。BGM 的快生长的错误比得上实际短期预报错误和一个更适当的整体传播了。就评估索引和分数而言，由 BGM 的 EPS 的预报技巧比 Monte-Carlo 的家高。而且，各种各样的需要周期在降水和非降水领域上有不同效果，合理周期的证实需要考虑在变量之间的平衡。

英文摘要：

How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.