气象科学2025，Vol.45Issue(4)：549-559,11.DOI:10.12306/2025jms.0012
一种基于深度学习模型的雷达回波临近外推预报方法
A deep learning-based extrapolation method for radar echo nowcasting
摘要
Abstract
To address issues such as edge distortion,blurring,and loss of realism in radar echo extrapolation using deep learning methods,a residual module,generator,and discriminator were introduced into the Convolutional Long Short-Term Memory(Conv-LSTM)framework to construct a Generative Adversarial-Residual Convolutional Long Short-Term Memory Network(GAN-rcLSTM)deep learning model.Additionally,a customized weighted loss function,which assigns different weights to radar echoes of varying intensities,was designed and integrated into GAN-rcLSTM,resulting in the Weighted Loss Function-based Generative Adversarial Residual Convolutional Long Short-Term Memory Network(Wloss-GAN-rcLSTM)model.Using a historical radar echo dataset from Shandong Province and its surrounding areas(2021-2022),the Wloss-GAN-rcLSTM model was trained and tested for radar echo extrapolation.A spatiotemporal deep learning radar echo extrapolation model capable of 0-2 hour forecasting with 6-minute updates was established.Evaluation results indicate that,at the Critical Success Index(CSI)threshold of 45 dBZ,which is of particular interest for heavy precipitation,the Wloss-GAN-rcLSTM model outperforms the optical flow method,Predictive Recurrent Neural Network(PredRNN),and GAN-rcLSTM by 0.12,0.07,and 0.02,respectively.For the clarity metric,structural similarity index(SSIM),improvements of 0.009,0.042,and 0.11 units were observed,respectively.Case studies further demonstrate that Wloss-GAN-rcLSTM is effectively suited for forecasting mesoscale weather processes,such as squall-line systems.关键词
加权损失函数/Conv-LSTM/GAN-rcLSTM/雷达回波外推/短临天气预报Key words
weighted loss function/Conv-LSTM/GAN-rcLSTM/radar echo extrapolation/short-term weather forecasting分类
信息技术与安全科学引用本文复制引用
魏海文,郭俊建,周成,王靓,张登旭..一种基于深度学习模型的雷达回波临近外推预报方法[J].气象科学,2025,45(4):549-559,11.基金项目
中国气象局创新发展专项(CXFZ2023J008) （CXFZ2023J008）
山东省自然科学基金面上资助项目(ZR2021MD121 （）
ZR2022MD072 （）
ZR2022MD088) （）
山东省气象局榜单类专项(2023SDBD01) （2023SDBD01）
海河流域气象科技创新资助项目(HHXM202404) （HHXM202404）
环渤海区域海洋气象科技博同创新项目(QYXM202301) （QYXM202301）
