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深度学习模型TAGAN在强对流回波临近预报中的应用

胡家晖 卢楚翰 姜有山 何婧

胡家晖, 卢楚翰, 姜有山, 等. 2022. 深度学习模型TAGAN在强对流回波临近预报中的应用[J]. 大气科学, 46(4): 805−818 doi: 10.3878/j.issn.1006-9895.2104.20225
引用本文: 胡家晖, 卢楚翰, 姜有山, 等. 2022. 深度学习模型TAGAN在强对流回波临近预报中的应用[J]. 大气科学, 46(4): 805−818 doi: 10.3878/j.issn.1006-9895.2104.20225
HU Jiahui, LU Chuhan, JIANG Youshan, et al. 2022. Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 805−818 doi: 10.3878/j.issn.1006-9895.2104.20225
Citation: HU Jiahui, LU Chuhan, JIANG Youshan, et al. 2022. Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 805−818 doi: 10.3878/j.issn.1006-9895.2104.20225

深度学习模型TAGAN在强对流回波临近预报中的应用

doi: 10.3878/j.issn.1006-9895.2104.20225
基金项目: 国家重大研发计划项目2019YFC1510201,上海市气象局面上项目MS202005
详细信息
    作者简介:

    胡家晖,男,1998年出生,硕士研究生,主要从事模式识别与气象交叉应用、极端降水与洪涝灾害方面的研究。E-mail: 2473992731@qq.com

    通讯作者:

    卢楚翰,E-mail: luchuhan@nuist.edu.cn

  • 中图分类号: P456

Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo

Funds: National Key Research and Development Program of China (Grant 2019YFC1510201), Shanghai Meteorological Situation Program (Grant MS202005)
  • 摘要: 近年来深度学习模型在解决对防灾减灾影响巨大且极具挑战性的临近预报问题的应用中日益增多。本文中,我们把临近预报作为一个时空序列预测的任务,将雷达反射率因子作为试验对象,使用基于对抗神经网络(GAN)优化构建的TAGAN深度学习模型预测未来1小时的雷达回波图像,并且与Rover光流法、基于卷积神经网络的3D U-Net模型进行对比试验。选取2018年全球气象AI挑战赛雷达回波数据集进行训练与测试,检验结果表明TAGAN模型在命中率(POD),虚警率(FAR),临界成功指数(CSI)以及相关系数等多种评分上要优于传统的光流法和对比的3D U-Net深度学习模型,TAGAN模型在以上的检验评分表现出色,并且随预测时间的增加较之传统光流模型效果更优,这为拓展和提升深度学习模型在临近天气预报中的应用提供了参考依据。
  • 图  1  TAGAN模型生成器模型图

    Figure  1.  TAGAN model generator model diagram

    图  2  TAGAN模型结构图

    Figure  2.  TAGAN model structure diagram

    图  3  CBAM模块结构图,包含通道注意力模块(左部)和空间注意力模型(右部)

    Figure  3.  CBAM module structure diagram, including the channel attention module (left part) and the spatial attention model (right part)

    图  4  Self-attention模块结构图,$\otimes $表示矩阵乘法运算

    Figure  4.  Self-attention module structure diagram ($\otimes $: matrix multiplication operation)

    图  5  3DUnet模型概念示意图

    Figure  5.  Conceptual schematic diagram of the 3DUnet model

    图  6  30 dBZ阈值下三种模型FAR和HSS测试集平均得分随时间变化(折线),阴影上下界代表测试集所有样本得分的上下四分位数

    Figure  6.  Average scores of the False Alarm Rate and Heidke Skill Score test sets of the three models under 30 dBZ threshold change over time (broken line): the upper and lower bounds of the shade represent the upper and lower quartiles, respectively, of the scores of all samples in the test set

    图  7  四组预测个例最后一帧对比。(a–d)分别为四组回波过程,每一列分别为真实过程、Rover、TAGAN、3DUnet在四组预测个例中的最后一帧

    Figure  7.  Comparison of the last frame of the four sets of prediction cases, where (a–d) are the four sets of echo processes, and each column is the last frame of the real process, Rover, TAGAN, and 3DUnet in the four sets of process predictions

    图  8  四组预测个例三种模型(红色:3DUnet,绿色:Rover,蓝色:TAGAN)6~60 min 30 dBZ阈值的FAR(实线)和HSS(虚线)技巧评分

    Figure  8.  Four groups of prediction cases and three models (red: 3DUnet; green: Rover; and blue: TAGAN) under 6–60 min of 30 dBZ threshold FAR (solid line) and HSS (dashed line) skill scores

    图  9  四组预测个例第一帧回波图真实值,其中红框为计算质心的范围(覆盖未来9帧回波主体范围),蓝线轮廓为回波值大于30 dBZ的回波主体

    Figure  9.  True value of the first frame of the four groups of prediction cases: the red box represents the range of the calculated centroid covering the range of the echo subject of the next nine frames), while the blue line outline is the echo subject with an echo value greater than 30 dBZ

    图  10  四组预测个例10个时次回波值大于30 dBZ回波主体的质心移动轨迹。其中,红色:真实回波;棕色:3DUnet;灰色:Rover;绿色:TAGAN

    Figure  10.  Four groups of prediction cases during the 10 times the echo value is greater than 30 dBZ. Center of mass movement trajectory of the echo subject: real echo (red); 3DUnet (brown); Rover (gray); TAGAN (green)

    表  1  混淆矩阵

    Table  1.   Confusion matrix

    预测正类预测负类
    观测正类TPFN
    观测负类FPTN
    下载: 导出CSV

    表  2  测试集检验对比

    Table  2.   Test set comparison

    阈值30 min预测阈值60 min预测
    模型10 dBZ20 dBZ30 dBZ40 dBZ10 dBZ20 dBZ30 dBZ40 dBZ
    CSITAGAN0.710.660.480.160.610.550.340.08
    3DUnet0.660.600.420.110.540.470.240.04
    Rover0.630.570.400.150.520.440.270.06
    FARTAGAN0.200.230.300.390.270.310.400.51
    3DUnet0.270.290.380.560.380.400.530.75
    Rover0.230.290.430.660.330.410.600.82
    PODTAGAN0.860.810.600.190.800.710.440.10
    3DUnet0.870.790.540.130.810.660.310.05
    Rover0.780.730.570.190.700.630.430.09
    HSSTAGAN0.780.750.610.250.690.650.460.12
    3DUnet0.710.670.530.180.610.560.330.06
    Rover0.720.700.540.240.600.540.380.11
    注:加粗的数字表示对比模型中检验评分最优
    下载: 导出CSV

    表  3  测试集检验对比

    Table  3.   Test set comparison

    模型均方误差(MSE)平均绝对误差(MAE)相关系数
    (COR)
    TAGAN2046.308513.190.853
    3DUnet2610.6910372.130.812
    Rover2922.3610427.860.793
    注:加粗的数字表示对比模型中检验评分最优
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-07
  • 录用日期:  2021-09-02
  • 网络出版日期:  2021-09-09
  • 刊出日期:  2022-07-19

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