基于轻量化三维时域卷积网络（3D-TCN）的气象雷达雷暴预测方法

doi:10.12305/j.issn.1001-506X.2025.12.10

Abstract

Abstract:

To address the problem of gradient explosion, weak ability to capture long-term dependencies, and low efficiency caused by the inability to perform parallel computing in most existing thunderstorm prediction models based on recurrent neural networks (RNN), a three-dimensional time-domain convolutional neural network (3D-TCN) is proposed for meteorological radar thunderstorm prediction. A 3D convolution operation is introduced into the residual block of TCN core to extract spatiotemporal features from radar images, achieving accurate prediction for up to 1 h. The thunderstorm evolution prediction performance of the 3D-TCN model is validated based on the Hong Kong Observatory 7 (HKO-7) dataset, and is compared with the spatiotemporal memory decoupled spatiotemperal long short-term memory (ST-LSTM) network, memory enhancement network, and traditional cross-correlation algorithms. The experimental results show that the 3D-TCN model performs better in evaluation metrics such as critical success index, and significantly reduces training time with its lightweight structure. Compared with the ST-LSTM network decoupled from spatiotemporal memory, its critical success index has increased by an average of 0.037, with the highest increase approaching 0.13. The training time has been shortened from 4 min to 6 min to less than 1 min, which verifies the effectiveness of the proposed method.

Key words: temporal convolutional networks (TCN), weather radar, thunderstorm prediction, deep learning

CLC Number:

TN 959.4

Enji LU, Ling WANG, Daiyin ZHU, Ye ZHOU. Weather radar thunderstorm prediction method based on lightweight three-dimensional temporal convolutional network[J]. Systems Engineering and Electronics, 2025, 47(12): 4084-4092.

Figures/Tables 15

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预测	真实
预测	有雷暴	无雷暴
有雷暴	NA	NB
无雷暴	NC	ND

预测时间/min	CSI			POD			FAR
预测时间/min	30 dBz	40 dBz	50 dBz	30 dBz	40 dBz	50 dBz	30 dBz	40 dBz	50 dBz
6	0.833	0.640	0.377	0.875	0.675	0.433	0.055	0.156	0.168
30	0.640	0.389	0.137	0.737	0.492	0.190	0.126	0.180	0.232
60	0.462	0.243	0.057	0.565	0.274	0.059	0.171	0.278	0.295

预测时间/min	CSI			POD			FAR
预测时间/min	30 dBz	40 dBz	50 dBz	30 dBz	40 dBz	50 dBz	30 dBz	40 dBz	50 dBz
6	0.815	0.653	0.360	0.865	0.715	0.428	0.069	0.130	0.170
30	0.590	0.358	0.135	0.649	0.412	0.164	0.131	0.203	0.308
60	0.335	0.149	0.053	0.475	0.228	0.054	0.148	0.240	0.309

预测方法	CSI	POD	FAR
交叉相关法	0.35	0.30	0.46
MIM网络^[30]	0.51	0.58	0.22
ST-LSTM+记忆解耦网络	0.59	0.65	0.13
3D-TCN	0.64	0.74	0.13

模型	CSI	POD	FAR
无膨胀卷积版本	0.57	0.68	0.19
完整模型	0.64	0.74	0.13

Weather radar thunderstorm prediction method based on lightweight three-dimensional temporal convolutional network

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模型	CSI	POD	FAR	收敛迭代数
无残差连接版本	0.58	0.68	0.16	35000
完整模型	0.64	0.74	0.13	20000

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