SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting
Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical w...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1550 |
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| author | Zhuang Li Zhenyu Lu Yizhe Li Xuan Liu |
| author_facet | Zhuang Li Zhenyu Lu Yizhe Li Xuan Liu |
| author_sort | Zhuang Li |
| collection | DOAJ |
| description | Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation. |
| format | Article |
| id | doaj-art-1bc29346758b41a4a5dc327d10d2a9b8 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-1bc29346758b41a4a5dc327d10d2a9b82025-08-20T02:31:16ZengMDPI AGRemote Sensing2072-42922025-04-01179155010.3390/rs17091550SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation NowcastingZhuang Li0Zhenyu Lu1Yizhe Li2Xuan Liu3School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaPrecipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation.https://www.mdpi.com/2072-4292/17/9/1550precipitation nowcastingSwin Transformermulti-scale featuresgated attention mechanismsextreme weather warningdeep learning |
| spellingShingle | Zhuang Li Zhenyu Lu Yizhe Li Xuan Liu SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting Remote Sensing precipitation nowcasting Swin Transformer multi-scale features gated attention mechanisms extreme weather warning deep learning |
| title | SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting |
| title_full | SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting |
| title_fullStr | SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting |
| title_full_unstemmed | SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting |
| title_short | SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting |
| title_sort | swinnowcast a swin transformer based model for radar based precipitation nowcasting |
| topic | precipitation nowcasting Swin Transformer multi-scale features gated attention mechanisms extreme weather warning deep learning |
| url | https://www.mdpi.com/2072-4292/17/9/1550 |
| work_keys_str_mv | AT zhuangli swinnowcastaswintransformerbasedmodelforradarbasedprecipitationnowcasting AT zhenyulu swinnowcastaswintransformerbasedmodelforradarbasedprecipitationnowcasting AT yizheli swinnowcastaswintransformerbasedmodelforradarbasedprecipitationnowcasting AT xuanliu swinnowcastaswintransformerbasedmodelforradarbasedprecipitationnowcasting |