| 基于Lorenz系统提取数值模式可预报分量的初步试验 |
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| 引用本文： | 王启光,封国林,郑志海,支蓉,丑纪范.基于Lorenz系统提取数值模式可预报分量的初步试验[J].大气科学,2012,36(3):539-550. |
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| 作者姓名： | 王启光 封国林 郑志海 支蓉 丑纪范 |
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| 作者单位： | 1.兰州大学大气科学学院, 兰州 730000 |
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| 基金项目： | 国家自然科学基金资助项目40930952、41105070和41105055, 全球变化重大研究计划2012CB955902和公益性行业 (气象) 科研专项GYHY201106016 |
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| 摘 要： | 针对数值预报模式中存在的非线性混沌特性, 从提取可预报分量的思路出发, 阐述了在数值模式中提取可预报分量的方法, 并利用Lorenz系统进行了相关数值试验。研究发现, Lorenz系统初始误差在相空间中的增长速度是不同的, 某些方向的误差增长速度较慢, 即存在对初值扰动不敏感、相对稳定的可预报分量。根据数值模式切线性误差算子的特征值演化规律, 提取出数值模式的可预报分量, 并将模式变量在其基底上进行投影变换, 建立了可预报分量数值模式。在此基础上, 研究了Lorenz系统的混沌状态、模式参数误差及外部随机噪声对提取可预报分量的影响, 发现基于可预报分量的数值模式, 具有更好的预报技巧。
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| 关 键 词： | 数值预报 可预报分量 奇异值分解 Lorenz系统 |
| 收稿时间： | 2011/5/23 0:00:00 |
| 修稿时间： | 2011/6/24 0:00:00 |
The Preliminary Analysis of the Procedures of Extracting Predicable Components in Numerical Model of Lorenz System |
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| WANG Qiguang,FENG Guolin,ZHENG Zhihai,ZHI Rong and CHOU Jifan.The Preliminary Analysis of the Procedures of Extracting Predicable Components in Numerical Model of Lorenz System[J].Chinese Journal of Atmospheric Sciences,2012,36(3):539-550. |
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| Authors: | WANG Qiguang FENG Guolin ZHENG Zhihai ZHI Rong and CHOU Jifan |
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| Institution: | 1.College of Atmospheric Sciences, Lanzhou University, Lanzhou 7300002.Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081 |
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| Abstract: | The authors have proposed to extract the predictable components to make prediction in the numerical model which has nonlinear chaos. The method of extracting predicable components was introduced in a simple numerical model, and the numerical experiments were done based on Lorenz system. In the experiment, the authors found that the velocity of initial error increase is different for different components in the phase space, and there are some particular directions with slow error increase. That is to say, there exist predictable components which are relatively stable and insensitive to initial perturbation. The numerical model of the predictable components was established by extracting predicable components based on the evolution of the eigenvalues of the tangent operator error, and projecting the model variables onto the substrates. On the basis of these, the impacts of chaotic states, the errors of model parameters, and the external random noise on extracting the predicable components were studied. And the authors found that the numerical model of the predicable components has a better forecasting skill. |
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| Keywords: | numerical prediction predictable components singular value decomposition Lorenz system |
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