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增长模叠加方式及其对集合预报初始扰动的影响
引用本文:张立凤,于永锋.增长模叠加方式及其对集合预报初始扰动的影响[J].高原气象,2007,26(1):75-82.
作者姓名:张立凤  于永锋
作者单位:1. 解放军理工大学,气象学院,江苏,南京,211101;中国科学院,大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京,100029
2. 海军南海舰队海洋水文气象中心,广东,湛江,524001
基金项目:国家重点基础研究发展计划(973计划) , 国家重点实验室基金
摘    要:用增长模繁殖法(BGM)有效地构造集合预报初始扰动的一个前提是, 任两个扰动之间应尽可能的发散.本文从增长模发展和饱和的角度分析了增长模叠加方式与集合预报初始扰动"取样性"的关系, 无论是从扰动形势演变得出的定性结论还是定量计算的结果都显示: 在相同初始模的情况下, 通过改变扰动叠加方式分别构造多个扰动样本的做法是近似等效的.用这种方法增加的集合样本从"取样性"的角度来看是没有多大意义的, 更多的是在浪费有限的计算资源.构造多个初值的有效方法应该是从不同初始模出发进行增长模的繁殖.

关 键 词:增长模繁殖  叠加方式  初始扰动  取样性  集合预报
文章编号:1000-0534(2007)01-0075-08
修稿时间:2006-01-182006-06-20

The Effect of Superposition Manners of the Bred Modes on the Initial Perturbations in Ensemble Forecasting
ZHANG Li-feng,YU Yong-feng.The Effect of Superposition Manners of the Bred Modes on the Initial Perturbations in Ensemble Forecasting[J].Plateau Meteorology,2007,26(1):75-82.
Authors:ZHANG Li-feng  YU Yong-feng
Institution:1. Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China; 2. Hydrometeorological Center of South China Sea Navy, Zhanjiang 524001, China ; 3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029, China
Abstract:Generating initial perturbation in ensemble forecasting by using the breeding growing modes should be based on the premise that either perturbation should spread from other possibly. Focusing on the saturation of growing modes, the relation between the way of superposing the perturbation upon the analysis and sampling of initial perturbations in ensemble forecasting is discussed in this paper. The result shows that the ensemble samples generated by changing the manner to superposing on growing modes have the identical effect if the same initial modes are given, which is meaningless as the sampling is concerned. Therefore, the effective method to create more initial perturbations should begin with more different initial random modes.
Keywords:Breeding growing modes  Superposition manners  Initial perturbation  Sampling  Ensemble forecasting
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