| 基于最小二乘支持向量机的副热带高压预测模型 |
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| 引用本文： | 刘科峰,张韧,洪梅,余丹丹,王辉赞.基于最小二乘支持向量机的副热带高压预测模型[J].应用气象学报,2009,20(3):354-359. |
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| 作者姓名： | 刘科峰 张韧 洪梅 余丹丹 王辉赞 |
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| 作者单位： | 1.解放军理工大学气象学院, 南京211101 |
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| 基金项目： | 中国科学院大气物理研究所联合创新青年学者计划 |
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| 摘 要： | 采用EOF时空分解、小波频牢分解和最小二乘支持向量机（LS-SVM）交叉互补方法，建立夏季500 hPa位势高度场的预测模型，用以描绘和表述副热带高压形势场的形态和变化。首先用经验正交函数分解（EOF）方法将NCEP/NCAR再分析资料500 hPa位势高度场序列分解为彼此正交的特征向量及其对应时间系数，随后提取前15个主要特征向龟的时间系数（方差贡献96.2%），采用小波分解方法将其分解为相对简单的带通信号，再利用LS-SVM方法建立各分量信号的预测模型，最后通过小波时频分量重构和EOF时空重构，得到500hPa位势高度场的预测结果以及副热带高压形势场的预测。通过对预测模型的试验情况和分析对比，结果表明:基于上述思想提出的算法模型能较为准确地描述500 hPa位势高度场的形态分布并预测1~7 d的副热带高压活动，对10~15 d的副热带高压活动预测结果也有参考意义。
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| 关 键 词： | EOF分解 小波分解 遗传算法 支持向量机 副热带高压预测 |
| 收稿时间： | 2008-04-17 |
Subtropical High Forecast Model of Least Square Support Vector Machine |
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| Liu Kefeng,Zhang Ren,Hong Mei,Yu Dandan and Wang Huizan.Subtropical High Forecast Model of Least Square Support Vector Machine[J].Quarterly Journal of Applied Meteorology,2009,20(3):354-359. |
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| Authors: | Liu Kefeng Zhang Ren Hong Mei Yu Dandan and Wang Huizan |
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| Institution: | 1.Institute of Meteorology, PLA University of Science and Technology, Nanjing 2111012.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | Based on the methods of empirical orthogonal decomposition (EOF), wavelet frequency decomposition and least square support vector machine, a summer 500 hPa potential height forecasting model is established to describe the form and change of the subtropical high situation. First, 500 hPa potential height fields sequences on NCEP/NCAR are separated into the time coefficients and corresponding eigenvectors which are orthogonal to each other with the method of empirical orthogonal decomposition. Then fifteen time coefficient series corresponding with major eigenvector (square contribution of 96.2%) are extracted and each time coefficient is decomposed to relatively simple signals with the method of wavelet analysis. Then, each signal prediction model is set up with the method of least square support vector machine. Finally, the forecasting simple signals are used to reconstruct the corresponding forecasting time series with the method of wavelet decomposition, then, the forecasting time series and corresponding major eigenvector are used to reconstruct 500 hPa potential fields with the methods of empirical orthogonal decomposition. The reconstructed potential fields are the fields which are forecasting results. Through experiments and analysis of contrast on the prediction model, the results show that the proposed algorithm model based on the above ideas can basically describe the distribution of 500 hPa potential situation and basically forecast the location and intensity of subtropical high within seven days. And the results also show that the 10-15 day forecasting results by the model can be used for reference for the medium and long-term activity of the subtropical high. The results also show that the model exhibits its properties of simplicity, stability, flexibility and good prospect of application. |
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