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深度学习在印度洋偶极子预报中的应用研究

刘俊 唐佑民 宋迅殊 孙志林

刘俊, 唐佑民, 宋迅殊, 等. 2022. 深度学习在印度洋偶极子预报中的应用研究[J]. 大气科学, 46(3): 590−598 doi: 10.3878/j.issn.1006-9895.2105.21048
引用本文: 刘俊, 唐佑民, 宋迅殊, 等. 2022. 深度学习在印度洋偶极子预报中的应用研究[J]. 大气科学, 46(3): 590−598 doi: 10.3878/j.issn.1006-9895.2105.21048
LIU Jun, TANG Youmin, SONG Xunshu, et al. 2022. Prediction of the Indian Ocean Dipole using Deep Learning Method [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 590−598 doi: 10.3878/j.issn.1006-9895.2105.21048
Citation: LIU Jun, TANG Youmin, SONG Xunshu, et al. 2022. Prediction of the Indian Ocean Dipole using Deep Learning Method [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 590−598 doi: 10.3878/j.issn.1006-9895.2105.21048

深度学习在印度洋偶极子预报中的应用研究

doi: 10.3878/j.issn.1006-9895.2105.21048
基金项目: 国家重点研发计划项目2017YFA0604202,国家自然科学基金项目41530961
详细信息
    作者简介:

    刘俊,男,1994年出生,硕士研究生,主要从事物理海洋相关方向的研究。E-mail: 1187638890@qq.com

    通讯作者:

    唐佑民,E-mail: tangy@hhu.edu.cn

  • 中图分类号: P466

Prediction of the Indian Ocean Dipole using Deep Learning Method

Funds: National Key Research and Development Program of China (Grant 2017YFA0604202), National Natural Science Foundation of China (Grant 41530961)
  • 摘要: 印度洋偶极子(IOD)是热带印度洋秋季最强的年际变率,它会通过大气遥相关来影响世界许多地区的气候。目前耦合气候模式对IOD预报技巧仍非常有限,远低于热带太平洋的厄尔尼诺事件的预报技巧。鉴于深度学习具备高效的数据处理能力,本文使用深度学习中的卷积神经网络(CNN)与人工神经网络中的多层感知机(MLP)处理再分析观测资料,从而进行IOD预报。由于当预报初始时刻为北半球冬春季时,对IOD事件的预报技巧较低。因此,为探索CNN的预报能力,本文仅使用三种(1~3月、2~4月、3~5月)初始时刻的海表温度异常(SSTA)作为CNN的输入数据,来预报其后续七个月的印度洋偶极子指数(DMI)、东极子指数(EIOI)和西极子指数(WIOI)。结果表明:CNN对DMI、EIOI和WIOI的有效预测时效均超过了6个月。与现在耦合动力模式相比,CNN模型能够显著提升DMI和EIOI的预报技巧,但对WIOI的预报技巧提升有限。当预报提前时间为7个月时,CNN模型能够比较准确地预报1994、1997与2019年的IOD事件。由于CNN模型能够更好地抓住印度洋海温的空间结构特征,它对IOD事件的预报技巧比传统神经网络MLP高。
  • 图  1  CNN模型框架图

    Figure  1.  Architecture of the CNN (convolutional neural network) model

    图  2  1990~2019年以JFM、FMA、MAM为起始态,分别使用CNN模型(实线)与MLP模型(虚线)预报的DMI与观测值的(a)相关系数和(b)均方根误差(RMSE)

    Figure  2.  (a) Correlation coefficients and (b) RMSE (root mean square errors) between the forecasted and observed DMI during 1990–2019 using CNN (solid lines) and MLP (dashed lines) models, respectively, with JFM (January–March), FMA (February–April), and MAM (March–May) as the initial conditions

    图  3  1990~2019年观测和CNN模式预报的北半球秋季(9月、10月)平均的DMI(标准化的)

    Figure  3.  Normalized DMI observed and forecasted by CNN model averaged in boreal autumn (September and October) during 1990–2019

    图  4  1990~2019年以JFM、FMA、MAM为起始态,分别使用CNN模型(实线)与MLP模型(虚线)预报的EIO指数与观测值的(a)相关系数和(b)RMSE

    Figure  4.  (a) Correlation coefficients and (b) RMSE between the forecasted and observed EIOI (East Pole Index for Indian Ocean) during 1990–2019 using CNN (solid lines) and MLP (dashed lines) models, respectively, with JFM, FMA, and MAM as the initial conditions

    图  5  1990~2019年观测和CNN模式预报的北半球秋季(9、10月)平均EIO指数(标准化的)

    Figure  5.  Normalized EIOI index observed and forecasted by CNN mode averaged in boreal autumn (September and October) during 1990–2019

    图  6  1990~2019年以JFM、FMA、MAM为起始态,分别使用CNN模型(实线)与MLP模型(虚线)预报的WIO指数与观测值的(a)相关系数和(b)RMSE

    Figure  6.  (a) Correlation coefficients and (b) RMSE between the forecasted and observed WIOI (West Pole Index for Indian Ocean) during 1990–2019 using CNN and MLP models, respectively, with JFM, FMA, and MAM as the initial conditions

    图  7  1990~2019年观测和CNN模式预报的北半球秋季(9、10月)平均WIO指数(标准化的)

    Figure  7.  Normalized WIOI observed and forecasted by CNN mode averaged in boreal autumn (September and October) during 1990–2019

    表  1  CNN模型、MLP模型的输入(SSTA)与输出(DMI)

    Table  1.   Input (Sea Surface Temperature Anomaly, SSTA) and output (Indian Ocean Dipole Mode Index, DMI) of CNN model and MLP (multi-layer perceptron) model

    输入(SSTA)输出
    提前1个月提前2个月提前3个月提前4个月提前5个月提前6个月提前7个月
    JFM4月DMI5月DMI6月DMI7月DMI8月DMI9月DMI10月DMI
    FMA5月DMI6月DMI7月DMI8月DMI9月DMI10月DMI11月DMI
    MAM6月DMI7月DMI8月DMI9月DMI10月DMI11月DMI12月DMI
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-22
  • 录用日期:  2021-06-18
  • 网络出版日期:  2021-06-21
  • 刊出日期:  2022-05-19

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