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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

胡凤良 王丽琼 左瑞亭 张舰齐

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. doi: 10.3878/j.issn.1006-9585.2016.15251
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. doi: 10.3878/j.issn.1006-9585.2016.15251
Fengliang HU, Liqiong WANG, Ruiting ZUO, Jianqi ZHANG. 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. doi: 10.3878/j.issn.1006-9585.2016.15251
Citation: Fengliang HU, Liqiong WANG, Ruiting ZUO, Jianqi ZHANG. 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. doi: 10.3878/j.issn.1006-9585.2016.15251

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

doi: 10.3878/j.issn.1006-9585.2016.15251
基金项目: 

国家自然科学基金项目 (NSFC, Grants 41490642 and 41475071)

详细信息
    作者简介:

    胡凤良,男,1993年出生,硕士研究生,主要从事气候数值模拟研究。E-mail:15895904544@163.com

    通讯作者:

    左瑞亭,E-mail:ratinzuo@126.com

  • 中图分类号: P468.0+24

Extra-seasonal Predicting Tests and Analyses of Several Statistical Forecasting Methods on Precipitation over Nanjing in 1998

Funds: 

National Natural Science Foundation of China (NSFC, Grants 41490642 and 41475071)

  • 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。
  • 图  1  1998 年3~9 月南京地区(a)逐日降水量和对应的(b)降水候距平

    Figure  1.  Daily precipitation and(b)pentad-mean precipitation anomalies in Nanjing from Mar to Sep in 1998

    图  2  ARIMA 模型对历史训练序列的拟合

    Figure  2.  Fitting results of the ARIMA model to the historical training series

    图  3  1998 年3~9 月各预测试验降水候距平结果对比

    Figure  3.  Comparison of the pentad-mean precipitation anomalies from the prediction tests and the real data for the period of Mar to Sep of 1998

    图  4  1994 年3 月至1998 年9 月共330 候降水距平EMD 分解结果

    Figure  4.  EMD decomposition results of 330 pentad-mean precipitation anomalies from Mar 1994 to Sep 1998

    图  5  1998 年3~9 月降水候距平前4 个IMF 高频分量的预测结果(粗实线表示近似真值,细实线表示试验2 结果,虚线表示试验3 结果)

    Figure  5.  The first four IMFs of pentad-mean precipitation anomalies from Mar to Sep of 1998(the bold solid line indicates the real data, the thin solid line indicates the results of test 2, and the dash line indicates the results of test 3)

    图  6  1994 年3 月至1998 年2 月降水候距平序列EMD 分解各特征模态的振幅和频率

    Figure  6.  Instantaneous amplitudes and frequencies of IMFs for pentad-mean precipitation anomalies from Mar 1993 to Feb 1998

    图  7  2003 年3~9 月各预测试验降水候距平结果对比

    Figure  7.  Comparison of pentad-mean precipitation anomalies of prediction tests and the real data from Mar to Sep of 2003

    表  1  各模态分量的方差贡献率和累积贡献

    Table  1.   Variance contribution rates of each component and cumulative contribution

    方差贡献率累积贡献
    IMF143.9%43.9%
    IMF234.9%78.8%
    IMF39.5%88.3%
    IMF47.0%95.3%
    IMF51.4%96.7%
    IMF62.3%99.0%
    IMF70.1%99.1%
    RES0.9%100.0%
    下载: 导出CSV

    表  2  全序列与历史样本序列EMD 分解的差异对比

    Table  2.   Differences between the EMD decomposition results of the whole sequences and historical sample sequences

    rRRMSEke
    IMF10.99940.03501.0%
    IMF20.99550.09555.6%
    IMF30.99290.11934.5%
    IMF40.99580.09288.3%
    IMF50.96260.274617.0%
    IMF60.91600.444426.4%
    IMF70.82340.832199.0%
    RES0.71820.641089.2%
    下载: 导出CSV

    表  3  试验2 预测模型各模态分量的γσ

    Table  3.   γ andσ of each component of prediction model inexperiment 2

    γ(×105σ
    IMF11.6130
    IMF21.04000
    IMF30.3500
    IMF44.524
    IMF51.42100
    IMF61.020
    IMF71.42
    RES16.00.8
    下载: 导出CSV

    表  4  高频分量模态预测结果与EMD 分解结果之间的相关系数和均方根误差

    Table  4.   Correlation coefficients and RMSEs of predicted high frequency components and EMD decomposition results

    rRMSE/mm
    试验1试验2试验3 试验1试验2试验3
    IMF10.290.424.734.51
    IMF20.200.453.663.54
    IMF30.410.351.841.81
    IMF40.330.761.771.31
    剩余分量0.63−0.221.750.93
    合成0.110.290.477.326.516.26
    下载: 导出CSV

    表  5  预测结果与近似真值的相关系数和均方根误差

    Table  5.   Correlation coefficients and RMSEs between the predicted results and the approximate real values

    rRMSE
    振幅频率振幅频率
    IMF10.520.143.310.12
    IMF20.480.322.600.04
    IMF30.28−0.401.320.03
    IMF40.780.030.540.02
    下载: 导出CSV

    表  6  2003 年3~9 月各预测试验降水候距平与观测数据之间的相关系数和均方差

    Table  6.   Correlation coefficients and RMSEs between the predicted pentad-mean precipitations anomalies and observed data from Mar to Sep of 2003

    rRMSE
    ARIMA试验0.0314.06
    EMD分解预测试验0.1810.24
    Hilbert预测试验0.429.32
    下载: 导出CSV
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