基于卷积神经网络的长江流域夏季日最高温度延伸期预报方法研究
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国家自然科学基金资助项目(42088101;42075032)


Extended-range forecasting method of summer daily maximum temperature in the Yangtze River Basin based on convolutional neural network
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    摘要:

    长江流域是我国夏季高温热浪灾害的多发区之一,该地区日最高温度(Tmax)具有显著的低频(10~30 d和30~60 d周期)变化特征,超前-滞后相关分析和气温方程诊断的结果显示,影响长江流域Tmax低频变化的大尺度环流/对流信号包含:自欧亚大陆东移南下的低频波列,自东北亚向西南方向传播的异常环流,以及由西太平洋向东亚传播的低频对流;这些低频对流/环流活动通过改变辐射加热过程及绝热过程,导致长江流域Tmax的低频变化。为了客观且有效地辨识和捕捉这些先兆信号,并考虑长江流域Tmax与大尺度因子间的非线性作用,本文采用机器学习方法中的卷积神经网络(Convolutional Neural Network,CNN)对大量历史数据进行训练,并构建了长江流域Tmax的延伸期预报模型。在独立预报阶段,CNN预报模型对长江流域区域平均Tmax的预报时效达30 d,提前5~30 d预报的Tmax与观测Tmax的时间相关系数介于0.63~0.70(通过99%置信度的显著性检验),量级偏差(均方根误差)小于1个标准差,显示出CNN在延伸期灾害天气预报的应用潜力。

    Abstract:

    The Yangtze River Basin(YRB) is one of the areas with a high frequency of heatwave occurrences in China.The daily maximum temperature (Tmax) in this area shows significant low-frequency oscillation signals for (10—30 d and 30—60 d) time periods.Based on the results of the lead-lag correlation analysis between the YRB Tmax and the 10—30 d/30—60 d convection and circulation anomalies,we identify the main low-frequency signals affecting the YRB Tmax.There are three types of signals that travel in different directions:1) the eastward and southward signals from the Eurasian continent;2) circulation anomalies propagating southwestward from Northeast Asia;and 3) low-frequency convective signals propagating from the western Pacific toward East Asia.The temperature diagnostic equation results show that when the low-frequency convection/circulation anomalies approach the YRB,both the diabatic (clear-sky radiative heating) and adiabatic (associated with sinking motion) heating processes lead to variations in the YRB temperature.To identify these precursory signals objectively and efficiently,as well as consider the nonlinear interaction between YRB Tmax and the large-scale predictors,we use Convolutional Neural Network (CNN),a type of deep neural network,to train the historical data,and then develop an extended-range forecast model for YRB Tmax.The independent forecast results show that the CNN-based forecast model is capable of predicting the YRB Tmax at a 30-day lead time,with the temporal correlation coefficient between the forecast and observed Tmax of 0.63—0.70 (exceeding the 99% confidence level).The current results suggest the potential of CNN in the application of extended-range forecasting as the magnitude of error (root-mean-square error) is less than one standard deviation.

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雷蕾,徐邦琪,高庆九,谢洁宏,2022.基于卷积神经网络的长江流域夏季日最高温度延伸期预报方法研究[J].大气科学学报,45(6):835-849. LEI Lei, HSU Pang-chi, GAO Qingjiu, XIE Jiehong,2022. Extended-range forecasting method of summer daily maximum temperature in the Yangtze River Basin based on convolutional neural network[J]. Trans Atmos Sci,45(6):835-849.

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  • 收稿日期:2021-11-01
  • 最后修改日期:2022-09-07
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  • 在线发布日期: 2022-12-15
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