中文摘要：

以新一代全球/区域多尺度通用同化与数值预报系统？热带气旋路径数值预报系统（global/regional assimilation andprediction system-tropical cyclone model, GRAPES-TCM）为试验模式, 采用组合不同的物理参数化方案（MP）方法和随机全倾向扰动（STTP）方法, 生成反映模式不确定性的集合成员, 在此基础上设计包含6 个成员的3 种集合方案, 方案1 和方案3 的成员分别用MP 方法和STTP 方法生成, 方案2 的成员同时采用MP 和STTP 方法生成, 用3 种集合方案对1109台风“梅花”进行了36 次72h 的集合预报试验.结果显示： 对于路径预报, 3 种集合方案中预报效果最好的是方案3, 其次为方案2, 最差的是方案1; 对于强度预报, 方案1 和方案2 的预报效果差异不大, 都远好于方案3.方案2 和方案3的路径预报与强度预报都好于控制试验的预报, 方案1 的路径预报好于大部分成员的预报, 强度预报好于所有成员的预报.3 种方案的路径离散度都偏小, 方案3 偏小最多, 其次为方案2; 方案3 的强度离散度也过于偏小, 是3 种方案中最小的, 方案1 和方案2 的强度离散度在积分前期明显偏小, 积分后期则有偏大的趋势, 其中方案2 的强度离散度大于方案1.与国内外8 个业务数值模式的预报结果比较, 对于路径预报, 方案1 优于5 个业务模式的预报, 方案2 和方案3 则优于除欧洲数值以外的7 个业务模式的预报; 对于强度预报, 方案1 和方案2 优于所有8 个业务模式的预报, 方案3 优于6 个业务模式的预报.总体而言, 3 种集合方案的路径和强度预报都表现出优于确定性预报的预报能力, 相对于各业务数值模式都表现出一定的预报优势, 具有业务应用的价值, 其中同时应用STTP 和MP 方法的方案2 对台风的综合预报效果是最优的.

英文摘要：

The GRAPES-TCM （global/regional assimilation and prediction system-tropical cyclone model） is used tomake ensemble prediction experiments for typhoon Muifa （1109） in 2011. Three kinds of ensemble schemes are designed forthe experiments. Every scheme has six ensemble members, which reflect the uncertainty of the model. The method of multiplephysics （MP） is used to form the members of scheme 1. The method of stochastic total tendency perturbation （STTP） is used toform the members of scheme 3. Both the MP method and the STTP method are used to form the members of scheme 2.Thirty-six experiments are made and the integration time is 72 h.The experiment results are as follows. In the three ensemble schemes, the track prediction of scheme 3 is the best, that ofscheme 2 is the second, and that of scheme 1 is the worst. The intensity prediction of scheme 1 is close to that of scheme 2.They are both much better than that of scheme 3. The track and intensity predictions of scheme 2 and scheme 3 are better thanthose of their control experiments. The track prediction of scheme 1 is better than that of its most members. The intensityprediction of scheme 1 is better than that of all of its members. The track dispersions of the three schemes are all small. In the three schemes, the track dispersion of scheme 3 is the smallest and that of scheme 2 is the second. They are both very small. The intensity dispersion of scheme 3 is also too small and is the smallest in the three schemes. In the early integration, the intensity dispersions of scheme 1 and scheme 2 are obviously small. In the late part of the integration, they are overall a little large. The intensity dispersion of scheme 2 is larger than that of scheme 1. Compared with the predictions of the eight domestic and abroad operational numerical weather prediction （NWP） models, the track prediction of scheme 1 is better than that of five operational models, and the track predictions of scheme 2 and scheme 3 are better than that of seven operational models