一種基于深度學(xué)習(xí)模型的雷達(dá)回波臨近外推預(yù)報(bào)方法

doi:10.12306/2025jms.0012

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一種基于深度學(xué)習(xí)模型的雷達(dá)回波臨近外推預(yù)報(bào)方法
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                        10.12306/2025jms.0012
                    
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基金項(xiàng)目:中國(guó)氣象局創(chuàng)新發(fā)展專項(xiàng)(CXFZ2023J008)；山東省自然科學(xué)基金面上資助項(xiàng)目(ZR2021MD121;ZR2022MD072;ZR2022MD088)；山東省氣象局榜單類專項(xiàng)(2023SDBD01)；海河流域氣象科技創(chuàng)新資助項(xiàng)目(HHXM202404)；環(huán)渤海區(qū)域海洋氣象科技博同創(chuàng)新項(xiàng)目(QYXM202301)

A deep learning-based extrapolation method for radar echo nowcasting

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摘要:

針對(duì)氣象雷達(dá)回波深度學(xué)習(xí)外推方法中存在的回波邊緣扭曲、模糊彌散失真及真實(shí)性衰退的問題，在卷積長(zhǎng)短期記憶網(wǎng)絡(luò)(Convolutional Long Short-Term Memory，Conv-LSTM)中引入殘差模塊、生成器與判別器，構(gòu)建生成對(duì)抗—?dú)埐罹矸e長(zhǎng)短期記憶網(wǎng)絡(luò)(Generative Adversarial-Residual Convolutional Long Short-Term Memory Network，GAN-rcLSTM)深度學(xué)習(xí)模型，同時(shí)針對(duì)不同強(qiáng)度回波賦予不同權(quán)重得到自定義的加權(quán)損失函數(shù)并融入GAN-rcLSTM得到自定義損失函數(shù)—生成對(duì)抗—?dú)埐罹矸e長(zhǎng)短期記憶網(wǎng)絡(luò)(Weighted Loss Function-Generative Adversarial-Residual Convolutional Long Short-Term Memory Network，Wloss-GAN-rcLSTM)?；?021—2022年山東省及周邊地區(qū)歷史雷達(dá)回波數(shù)據(jù)集對(duì)Wloss-GAN-rcLSTM模型進(jìn)行回波外推訓(xùn)練與測(cè)試，建立預(yù)報(bào)時(shí)長(zhǎng)為0～2 h、逐6 min更新的時(shí)空型深度學(xué)習(xí)雷達(dá)回波外推模型。檢驗(yàn)評(píng)估表明，Wloss-GAN-rcLSTM雷達(dá)回波外推模型較光流法、預(yù)測(cè)遞歸神經(jīng)網(wǎng)絡(luò)(Predictive Recurrent Neural Network，PredRNN)及GAN-rcLSTM方法在強(qiáng)降水關(guān)注的45 dBZ閾值下其臨界成功指數(shù)(Critical Success Index，CSI)分別提升了0.12、0.07與0.02，清晰度指標(biāo)結(jié)構(gòu)相似性指數(shù)(Structural Similarity Index，SSIM)分別提升了0.009、0.042與0.11，且個(gè)例檢驗(yàn)顯示W(wǎng)loss-GAN-rcLSTM最優(yōu)適用于颮線等中尺度天氣過程預(yù)報(bào)。

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.

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魏海文,郭俊建,周成,王靚,張登旭.一種基于深度學(xué)習(xí)模型的雷達(dá)回波臨近外推預(yù)報(bào)方法.氣象科學(xué),2025,45(4):549-559 WEI Haiwen, GUO Junjian, ZHOU Cheng, WANG Liang, ZHANG Dengxu. A deep learning-based extrapolation method for radar echo nowcasting. Journal of the Meteorological Sciences,2025,45(4):549-559

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收稿日期:2023-05-23
最后修改日期:2024-11-25
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在線發(fā)布日期: 2025-11-29
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