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西北太平洋热带气旋生成客观预测模型

郑倩 高猛

郑倩, 高猛. 西北太平洋热带气旋生成客观预测模型. 应用气象学报, 2022, 33(5): 594-603. DOI:  10.11898/1001-7313.20220507..
引用本文: 郑倩, 高猛. 西北太平洋热带气旋生成客观预测模型. 应用气象学报, 2022, 33(5): 594-603. DOI:  10.11898/1001-7313.20220507.
Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI:  10.11898/1001-7313.20220507.
Citation: Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI:  10.11898/1001-7313.20220507.

西北太平洋热带气旋生成客观预测模型

DOI: 10.11898/1001-7313.20220507
资助项目: 

山东中国科学院海洋大科学研究中心重点部署项目 COMS2019J02

中国科学院前沿科学重点研究计划“从0到1”原始创新项目 ZDBS-LY-7010

山东省自然科学基金项目 ZR2020KF031

山东省自然科学基金项目 ZR2020QD055

详细信息
    通信作者:

    高猛, 邮箱:gaomeng03@hotmail.com

An Objective Prediction Model for Tropical Cyclone Genesis in the Northwest Pacific

  • 摘要: 该文提出一种西北太平洋热带气旋年生成活动的客观预测模型。研究大尺度环境因子对西北太平洋热带气旋年生成频次的作用,使用最小角回归算法对初始14个预测因子进行选择和降维,将资料集分为训练集(1979—2015年)和验证集(2016—2020年),建立随机森林回归模型预测热带气旋年生成频次。分析环境因子对西北太平洋热带气旋生成位置的作用,使用逐步回归算法筛选影响显著的预测因子,建立局部泊松回归模型预测热带气旋生成空间位置的概率。结果表明:随机森林回归模型可以预测西北热带气旋频次的主要变化和趋势,揭示环境因子对西北太平洋热带气旋年生成频次的影响。局部泊松回归模型对于气旋生成位置概率有一定预测能力。利用随机森林回归模型和局部泊松回归模型模拟1979—2020年西北太平洋热带气旋生成,结果与观测基本一致,可见模型可为热带气旋危险性分析提供参考。
  • 图  1  西北太平洋热带气旋年生成频次与海表温度相关分析

    (填色,仅显示达到0.05显著性水平的相关系数)(方框表示预测因子的位置)

    Fig. 1  Correlations between the annual frequency of tropical cyclone in the Northwestern Pacific and sea surface temperature

    (the shaded, only correlations passing the test of 0.05 level are given) (the box denotes the predictor location)

    图  2  西北太平洋热带气旋年生成频次与夏秋季大尺度环境因子相关分析

    (填色,仅显示达到0.05显著性水平的相关系数)(方框表示预测因子的位置)

    Fig. 2  Correlations between the annual frequency of tropical cyclone in the Northwestern Pacific and the large-scale environmental factors from Jun to Nov

    (the shaded, only correlations passing the test of 0.05 level are given)(the box denotes the predictor location)

    图  3  西北太平洋热带气旋年生成频次的预测结果

    Fig. 3  The predicted tropical cyclone annual frequency in the Northwest Pacific

    图  4  热带气旋生成位置的观测及预测概率

    Fig. 4  Observed and predicted probability of tropical cyclone genesis location

    图  5  热带气旋生成位置的观测与模拟

    Fig. 5  Observed and simulated tropical cyclone genesis position location

    表  1  预测因子

    Table  1  Selected predictors

    预测因子 相关系数 描述
    X1 -0.53 12°~22°N, 35°~60°W区域平均春季海温异常
    X2 -0.50 3°~12°S, 45°~52°E区域平均春季海温异常
    X3 -0.48 20°~30°S, 155°~175°E区域平均夏秋季海温异常
    X4 0.45 30°~40°N,130°~140°W区域平均夏秋季海表温度异常
    X5 -0.57 10°~23°N,130°~150°E区域平均夏秋季500 hPa高度场异常
    X6 -0.56 24°~32°N,180°~210°W区域平均夏秋季500 hPa高度场异常
    X7 -0.61 20°~30°N,127°~140°E区域平均夏秋季600 hPa相对湿度异常
    X8 0.57 16°~26°N,155°~165°W区域平均夏秋季600 hPa相对湿度异常
    X9 0.63 16°~22°N,128°~152°E区域平均春季850 hPa相对涡度异常
    X10 -0.62 4°~12°N,130°~140°E区域平均夏秋季850 hPa与250 hPa纬向风垂直切变
    X11 0.45 10°~17°N,157°E~177°W区域平均夏秋季850 hPa与250 hPa纬向风垂直切变
    X12 -0.09 春季Nio3.4海温
    X13 0.09 夏秋季平流层准两年振荡指数
    X14 0.06 前年夏秋季南方涛动指数
    下载: 导出CSV

    表  2  预测因子重要性排序

    Table  2  Importance ranking of predictors

    因子 重要性
    X2 0.247933
    X10 0.174951
    X7 0.170453
    X5 0.120027
    X9 0.113977
    X13 0.067993
    X11 0.061185
    X4 0.043481
    下载: 导出CSV

    表  3  最优半径

    Table  3  Optimized scale

    月份 最优半径/km
    1 1200
    2 1500
    3 1400
    4 1300
    5 1200
    6 1100
    7 900
    8 800
    9 800
    10 900
    11 1100
    12 1200
    下载: 导出CSV
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  • 收稿日期:  2022-07-15
  • 修回日期:  2022-08-19
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