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利用神经网络方法建立热带气旋强度预报模型

黄小刚 费建芳 陈佩燕

黄小刚, 费建芳, 陈佩燕. 利用神经网络方法建立热带气旋强度预报模型. 应用气象学报, 2009, 20(6): 699-705..
引用本文: 黄小刚, 费建芳, 陈佩燕. 利用神经网络方法建立热带气旋强度预报模型. 应用气象学报, 2009, 20(6): 699-705.
Huang Xiaogang, Fei Jianfang, Chen Peiyan. A neural network approach to predict tropical cyclone intensity. J Appl Meteor Sci, 2009, 20(6): 699-705.
Citation: Huang Xiaogang, Fei Jianfang, Chen Peiyan. A neural network approach to predict tropical cyclone intensity. J Appl Meteor Sci, 2009, 20(6): 699-705.

利用神经网络方法建立热带气旋强度预报模型

资助项目: 

中国气象局上海台风研究所开放课题 2006ST B02

中国科学院大气物理研究所大气科学和地球流体力学国家重点实验室开放课题 2709

A Neural Network Approach to Predict Tropical Cyclone Intensity

  • 摘要: 以神经网络方法为基础,建立西北太平洋热带气旋强度预测模型,模型首先进行历史相似热带气旋选择。从选择的样本出发,计算得到一组气候持续因子、天气学经验因子和动力学因子, 对这些因子采用逐步回归方法进行筛选,将筛选得到的因子同对应时效的热带气旋强度输入神经网络训练模块,从而得到优化的预测模型。从2004-2005年西北太平洋26个热带气旋过程对12,24,36,48,72h等不同预报时效分别进行的634,582,530,478,426次预测试验结果的统计来看,相对于线性回归模型预测水平,该模型显著降低了各时段的预测误差。从几个热带气旋个例的预测结果来看, 该模型对超强台风, 以及具有强度迅速加强、再次加强等特征的热带气旋过程均有很好的描述能力。
  • 图  1  热带气旋强度预报流程图

    Fig. 1  The flow chart of tropical cyclone intensity forecast

    图  2  0406, 0505, 0402, 0425号热带气旋 12 h 和 24 h 强度预报结果与观测比较

    Fig. 2  Intensity predictions for tropical cyclone 0406, 0505, 0402, 0425 for 12, 24 hours

    图  3  最大风速预报误差分布柱状图

    Fig. 3  Error distribution of intensity forecast for the tropical cyclone sample

    图  4  预报因子被调用次数统计

    Fig. 4  The using times of different variables for the tropical cyclone sample

    表  1  不同时效超强台风强度预报最大值

    Table  1  The maximum intensity for super typhoon at different forecast periods

    表  2  12, 24, 36, 48, 72h预报时效平均绝对预报误差

    Table  2  Average absolute prediction error for 12, 24, 36, 48, 72 hours of the tropical cyclone sample

  • [1] Neumann C J. An Alternate to the Hurran Tropical Cyclone Forecast System. NOAA Tech Memo NWS SR-62, 1972.
    [2] Pike A C. Geopotential heights and thicknesses as predictors of Atlantic tropical cyclone motion and intensity. Mon Wea Rev, 1985, 113: 931-939. doi:  10.1175/1520-0493(1985)113<0931:GHATAP>2.0.CO;2
    [3] Kurihara Y, Bender M A, Tuleya R E, et al. Improvements in the GFDL hurricane prediction system. Mon Wea Rev, 1995, 123: 2791-2801. doi:  10.1175/1520-0493(1995)123<2791:IITGHP>2.0.CO;2
    [4] 王诗文.国家气象中心台风数值模式的改进及其应用试验. 应用气象学报, 1999, 10(3): 347-353. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19990384&flag=1
    [5] DeMaria M, Mainelli M, Shay L K, et al. Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea Forecasting, 2005, 20 :531-543. doi:  10.1175/WAF862.1
    [6] DeMaria M. Statistical Tropical Cyclone Intensity Forecast Improvements Using GOES And Aircraft Reconnaissance Data. 27th Conference on Hurricanes and Tropical Meteorology, 2006. https://www.researchgate.net/publication/290741215_Statistical_tropical_cyclone_intensity_forecast_improvements_using_goes_and_aircraft_reconnaissance_data
    [7] Rhome J R. On the Calculation of Vertical Shear : An Operational Perspective. 27th Conference on Hurricanes and Tropical Meteorology, 2006. https://ams.confex.com/ams/pdfpapers/108724.pdf
    [8] 金龙.神经网络气象预报建模理论方法与应用.北京: 气象出版社, 2004.
    [9] Baik J, Hwang H. Tropical cyclone intensity prediction using regression method and neural network. J Meteor Soc Japan, 1998, 76 :711-717. http://cat.inist.fr/?aModele=afficheN&cpsidt=1662148
    [10] McGauley M G. Hurricane Intensity Forecasting with Neural Networks. http://rsmas.miami.edu/divs/mpo/AboutMPO/Seminars/2004/0405_McGauley_Abstract.pdf  http://rsmas.miami.edu/divs/mpo/AboutMPO/Seminars/2004/0405_McGauley_Abstract.pdf
    [11] Anthony V C. A Neural Network Approach to Predict Hurricane Intensity in the North Atlantic Basin. University of Puerto Rico, 2004. http://citeseerx.ist.psu.edu/showciting?cid=6512854
    [12] 钮学新.热带气旋动力学.北京:气象出版社, 1992.
    [13] Smyth P, Ide K, Ghil M. Multiple regimes in northern hemisphere height fields via mixture model clustering. J Atmos Sci, 1999, 56 :3704-3723. doi:  10.1175/1520-0469(1999)056<3704:MRINHH>2.0.CO;2
    [14] http://www.nhc.noaa.gov/verification/pdfs/Verification2004.pdf http://www.nhc.noaa.gov/verification/pdfs/Verification2004.pdf
    [15] http://www.nhc.noaa.gov/verification/pdfs/Verification2005.pdf  http://www.nhc.noaa.gov/verification/pdfs/Verification2005.pdf
    [16] 黄嘉佑.气象统计分析与预报方法.北京:气象出版社, 2000.
    [17] Ramage C S. Hurricane development. J Meteor, 1959, 16 : 227-237. doi:  10.1175/1520-0469(1959)016<0227:HD>2.0.CO;2
    [18] Merrill R T. Environmental influences on hurricane intensification. J Atmos Sci, 1988, 45 : 1678-1687. doi:  10.1175/1520-0469(1988)045<1678:EIOHI>2.0.CO;2
    [19] Christopher S V, Leslie L M. The basic relationship between tropical cyclone intensity and the depth of environmental steering layer in the Australian region. Wea Forecasting, 1991, 6 : 244-253. doi:  10.1175/1520-0434(1991)006<0244:TBRBTC>2.0.CO;2
    [20] DeMaria M. The effect of vertical shear on tropical cycloneintensity change. J Atmos Sci, 1996, 53 : 2076-2087. doi:  10.1175/1520-0469(1996)053<2076:TEOVSO>2.0.CO;2
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出版历程
  • 收稿日期:  2009-05-06
  • 修回日期:  2016-01-13
  • 刊出日期:  2009-12-31

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