| 几种统计预测方法对1998年南京降水的跨季节预测试验及分析 |
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| 引用本文： | 胡凤良,王丽琼,左瑞亭,张舰齐.几种统计预测方法对1998年南京降水的跨季节预测试验及分析[J].气候与环境研究,2017,22(1):23-34. |
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| 作者姓名： | 胡凤良 王丽琼 左瑞亭 张舰齐 |
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| 作者单位： | 解放军理工大学气象海洋学院, 南京 211101,解放军理工大学气象海洋学院, 南京 211101,解放军理工大学气象海洋学院, 南京 211101,中国人民解放军95871部队, 衡阳 421002 |
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| 基金项目： | 国家自然科学基金项目41490642、41475071 |
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| 摘 要： | 对1998年南京降水分别设计并开展了求和自回归滑动平均（Auto-Regressive Integrated Moving Average，ARIMA）模型预测、经验模态分解（Empirical Mode Decomposition，EMD）预测和基于Hilbert变换（HilbertTransformation，HT）的幅频分离预测等3种跨季节统计预测试验。结果表明：ARIMA模型预测结果存在明显的系统性误差且对夏季的降水突变现象预测困难；EMD分解预测的结果虽在降水演变趋势上有明显提高，但仍未能预测出夏季的强降水突变现象，究其原因可能是对高频分量预测效果不好所致；而基于Hilbert变换的幅频分离预测方法能够对各模态分量的瞬时频率和瞬时振幅实施隔离预测，消除两者的相互影响，显著改善高频模态的预测效果，使得最终预测结果最为理想，不仅具有最高的趋势相关性和最小的偏差，而且还较好地预测出了夏季两次强降水过程。不仅如此，在对2003年的降水预测验证中，基于Hilbert变换的幅频分离预测方法同样具有最好的预测效果，表明该方法预测效果较为稳定，为改进跨季节短期气候统计预测技术提供了一个新思路。
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| 关 键 词： | 短期气候预测 求和自回归滑动平均(ARIMA) 经验模态分解(EMD) Hilbert变换(HT) 最小二乘支持向量机 |
| 收稿时间： | 2015/11/10 0:00:00 |
Extra-seasonal Predicting Tests and Analyses of Several Statistical Forecasting Methods on Precipitation over Nanjing in 1998 |
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| HU Fengliang,WANG Liqiong,ZUO Ruiting and ZHANG Jianqi.Extra-seasonal Predicting Tests and Analyses of Several Statistical Forecasting Methods on Precipitation over Nanjing in 1998[J].Climatic and Environmental Research,2017,22(1):23-34. |
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| Authors: | HU Fengliang WANG Liqiong ZUO Ruiting and ZHANG Jianqi |
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| Institution: | Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101,Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 and 95871 Troop, People''s Liberation Army of China, Hengyang 421002 |
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| Abstract: | Three statistical forecasting methods, i.e. ARIMA (Auto-Regressive Integrated Moving Average) model prediction, EMD (Empirical Mode Decomposition) decomposition prediction, and isolated prediction of frequency and amplitude based on Hilbert transformation, are designed and employed to make extra-seasonal prediction tests on the precipitation over Nanjing in 1998. Results show that the ARIMA model exhibits severe system errors and is hard to reproduce the abrupt variation of precipitation. Although the EMD decomposition prediction makes an obvious improvement in the evolution trend of precipitation, it still fails in the depiction of precipitation catastrophes in the summer due to its incapability of predicting high frequency modes. The isolated prediction method improves the prediction of high frequency modes since it can separately predict the frequency and amplitude of each mode and their interactions are avoided. Thereby the isolated prediction method gives a pretty good final prediction with the highest trend correlation and the smallest deviation. The two precipitation catastrophes in the summer of 1998 are realistically predicted. Additionally, a further verification of the precipitation prediction for 2003 also indicates that the isolated prediction method performs best among the three methods proposed in this study. The above results suggest that the isolated prediction method may provide a new idea for the technological improvement on extra-seasonal short-term climate prediction. |
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| Keywords: | Short-term climate prediction Auto-Regressive Integrated Moving Average (ARIMA) Empirical mode decomposition Hilbert transformation Least square support vector machine |
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