双月刊

ISSN 1006-9895

CN 11-1768/O4

深度学习模型TAGAN在强对流回波临近预报中的应用
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作者单位:

1.南京信息工程大学大气科学学院;2.南京信息工程大学;3.南京市气象局

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国家重点基础研究发展计划


Application of Deep Learning Model TAGAN in Nowcasting of Strong Convective Echo
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Nanjing University of Information Science and Technology

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    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1h的雷达回波图像,并且与Rover光流法、基于卷积神经网络的3DUnet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3DUnet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。

    Abstract:

    In recent years, deep learning models has been increasingly used in solving nowcasting problems that have a large impact on disaster prevention and mitigation. In this paper, we took nowcasting as a spatio-temporal sequence prediction task, and use radar reflectivity factor as the test object. We use TAGAN deep learning model based on GAN frame to predict the radar echo image of the future 1h, and compared it with Rover optical flow Method and 3DUnet model based on convolutional neural network. The radar echo data set of the 2018 Global Weather AI Challenge is selected for training and testing. The test results show that the TAGAN model shows advances by multiple scores such as the hit rate (POD), false alarm rate (FAR), critical success index (CSI), and correlation coefficient. The TAGAN model performs well in the above test scores and increases with the prediction time compared to the traditional optical flow method and the comparative deep learning model. Compared with the traditional optical flow model, the improvement effect is more significant. The results may shed some light on expanding and improving the application of deep learning models in near-weather forecasting.

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历史
  • 收稿日期:2020-11-07
  • 最后修改日期:2021-04-01
  • 录用日期:2021-09-02
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