ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing
Radar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions, and, therefore, severely restrict their use in e...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4792 |
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| author | Lei Zhang Ruoyang Zhang Yu Wu Yadong Wang Yanfeng Zhang Lijuan Zheng Chongbin Xu Xin Zuo Zeyu Wang |
| author_facet | Lei Zhang Ruoyang Zhang Yu Wu Yadong Wang Yanfeng Zhang Lijuan Zheng Chongbin Xu Xin Zuo Zeyu Wang |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | Radar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions, and, therefore, severely restrict their use in extreme weather situations. A deep learning method was recently applied for extrapolating radar echoes; however, its accuracy declines too quickly over a short time. In this study, we introduce a solution: Residual Transformer and Unet (ResTUnet), a novel model that improves prediction accuracy and exhibits good stability with a slow rate of accuracy decline. This presented Rest-Net model is designed to solve the issue of declining prediction accuracy by integrating a 1*1 convolution to diminish the neural network parameters. We constructed an observed dataset by Zhengzhou East Airport radar observation from July 2022 to August 2022 and performed 90 min experiments comprising five aspects, including extrapolation images, the Probability of Detection (POD) index, the Critical Success Index (CSI), the False Alarm Rate (FAR) index, and the Heidke Skill Score (HSS) index. The experimental results show that the ResTUnet model improved the CSI, HSS index, and the POD index by 17.20%, 11.97%, and 11.35%, compared to current models, including Convolutional Long Short-Term Memory (convLSTM), the Convolutional Gated Recurrent Unit (convGRU), the Trajectory Gated Recurrent Unit (TrajGRU), and the improved recurrent network for video predictive learning, the Predictive Recurrent Neural Network++ (predRNN++). In addition, the mean squared error of the ResTUnet model remains stable at 15% between 0 and 60 min and starts to increase after 60–90 min, which is 12% better than the current models. This enhancement in prediction accuracy has practical applications in meteorological services and decision making. |
| format | Article |
| id | doaj-art-fa24e5871c0f420c988dcdd97fdf87ca |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-fa24e5871c0f420c988dcdd97fdf87ca2025-08-20T02:56:51ZengMDPI AGRemote Sensing2072-42922024-12-011624479210.3390/rs16244792ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote SensingLei Zhang0Ruoyang Zhang1Yu Wu2Yadong Wang3Yanfeng Zhang4Lijuan Zheng5Chongbin Xu6Xin Zuo7Zeyu Wang8Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, ChinaBeijing Institute of Space Mechanics & Electricity, Beijing 100094, ChinaBeijing Institute of Space Mechanics & Electricity, Beijing 100094, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaRadar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions, and, therefore, severely restrict their use in extreme weather situations. A deep learning method was recently applied for extrapolating radar echoes; however, its accuracy declines too quickly over a short time. In this study, we introduce a solution: Residual Transformer and Unet (ResTUnet), a novel model that improves prediction accuracy and exhibits good stability with a slow rate of accuracy decline. This presented Rest-Net model is designed to solve the issue of declining prediction accuracy by integrating a 1*1 convolution to diminish the neural network parameters. We constructed an observed dataset by Zhengzhou East Airport radar observation from July 2022 to August 2022 and performed 90 min experiments comprising five aspects, including extrapolation images, the Probability of Detection (POD) index, the Critical Success Index (CSI), the False Alarm Rate (FAR) index, and the Heidke Skill Score (HSS) index. The experimental results show that the ResTUnet model improved the CSI, HSS index, and the POD index by 17.20%, 11.97%, and 11.35%, compared to current models, including Convolutional Long Short-Term Memory (convLSTM), the Convolutional Gated Recurrent Unit (convGRU), the Trajectory Gated Recurrent Unit (TrajGRU), and the improved recurrent network for video predictive learning, the Predictive Recurrent Neural Network++ (predRNN++). In addition, the mean squared error of the ResTUnet model remains stable at 15% between 0 and 60 min and starts to increase after 60–90 min, which is 12% better than the current models. This enhancement in prediction accuracy has practical applications in meteorological services and decision making.https://www.mdpi.com/2072-4292/16/24/4792radar echoneural network modelweather predictionremote sensingclimate change |
| spellingShingle | Lei Zhang Ruoyang Zhang Yu Wu Yadong Wang Yanfeng Zhang Lijuan Zheng Chongbin Xu Xin Zuo Zeyu Wang ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing Remote Sensing radar echo neural network model weather prediction remote sensing climate change |
| title | ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing |
| title_full | ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing |
| title_fullStr | ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing |
| title_full_unstemmed | ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing |
| title_short | ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing |
| title_sort | restunet a novel neural network model for nowcasting using radar echo sequences by ground based remote sensing |
| topic | radar echo neural network model weather prediction remote sensing climate change |
| url | https://www.mdpi.com/2072-4292/16/24/4792 |
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