MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling
To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological informa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2281 |
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| author | Yida Liu Zhuang Li Guangzhen Cao Qiong Wang Yizhe Li Zhenyu Lu |
| author_facet | Yida Liu Zhuang Li Guangzhen Cao Qiong Wang Yizhe Li Zhenyu Lu |
| author_sort | Yida Liu |
| collection | DOAJ |
| description | To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling. |
| format | Article |
| id | doaj-art-484b3ef451ef46af8ede7b7c96a41e41 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-484b3ef451ef46af8ede7b7c96a41e412025-08-20T02:36:27ZengMDPI AGRemote Sensing2072-42922025-07-011713228110.3390/rs17132281MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation DownscalingYida Liu0Zhuang Li1Guangzhen Cao2Qiong Wang3Yizhe Li4Zhenyu Lu5School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaSchool of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaTo address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling.https://www.mdpi.com/2072-4292/17/13/2281precipitation downscalinggenerative adversarial networkmulti-scale featureshigh-resolution reconstructiondeep learningextreme precipitation detection |
| spellingShingle | Yida Liu Zhuang Li Guangzhen Cao Qiong Wang Yizhe Li Zhenyu Lu MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling Remote Sensing precipitation downscaling generative adversarial network multi-scale features high-resolution reconstruction deep learning extreme precipitation detection |
| title | MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling |
| title_full | MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling |
| title_fullStr | MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling |
| title_full_unstemmed | MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling |
| title_short | MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling |
| title_sort | msrgan a multi scale residual gan for high resolution precipitation downscaling |
| topic | precipitation downscaling generative adversarial network multi-scale features high-resolution reconstruction deep learning extreme precipitation detection |
| url | https://www.mdpi.com/2072-4292/17/13/2281 |
| work_keys_str_mv | AT yidaliu msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling AT zhuangli msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling AT guangzhencao msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling AT qiongwang msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling AT yizheli msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling AT zhenyulu msrganamultiscaleresidualganforhighresolutionprecipitationdownscaling |