MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation
Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods fail to fully utilize historical radar echo data, resulting i...
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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3956 |
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| author | Huantong Geng Han Zhao Zhanpeng Shi Fangli Wu Liangchao Geng Kefei Ma |
| author_facet | Huantong Geng Han Zhao Zhanpeng Shi Fangli Wu Liangchao Geng Kefei Ma |
| author_sort | Huantong Geng |
| collection | DOAJ |
| description | Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods fail to fully utilize historical radar echo data, resulting in limited accuracy for future radar echo prediction. Existing deep learning echo extrapolation methods often face issues such as high-threshold echo attenuation and blurring distortion. In this paper, we propose a UNet-based multi-branch feature extraction model named MBFE-UNet for radar echo extrapolation to mitigate these issues. We design a Multi-Branch Feature Extraction Block, which extracts spatiotemporal features of radar echo data from various perspectives. Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. Experimental results indicate that, compared to the Transformer-based Rainformer, the MBFE-UNet achieves an average increase of 4.8% in the critical success index (CSI), 5.5% in the probability of detection (POD), and 3.8% in the Heidke skill score (HSS). |
| format | Article |
| id | doaj-art-76fcceabf8ad429883a2fcce173154ba |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-76fcceabf8ad429883a2fcce173154ba2025-08-20T02:49:56ZengMDPI AGRemote Sensing2072-42922024-10-011621395610.3390/rs16213956MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo ExtrapolationHuantong Geng0Han Zhao1Zhanpeng Shi2Fangli Wu3Liangchao Geng4Kefei Ma5School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaRadar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods fail to fully utilize historical radar echo data, resulting in limited accuracy for future radar echo prediction. Existing deep learning echo extrapolation methods often face issues such as high-threshold echo attenuation and blurring distortion. In this paper, we propose a UNet-based multi-branch feature extraction model named MBFE-UNet for radar echo extrapolation to mitigate these issues. We design a Multi-Branch Feature Extraction Block, which extracts spatiotemporal features of radar echo data from various perspectives. Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. Experimental results indicate that, compared to the Transformer-based Rainformer, the MBFE-UNet achieves an average increase of 4.8% in the critical success index (CSI), 5.5% in the probability of detection (POD), and 3.8% in the Heidke skill score (HSS).https://www.mdpi.com/2072-4292/16/21/3956radar echo extrapolationdeep learningUNetmulti-branch |
| spellingShingle | Huantong Geng Han Zhao Zhanpeng Shi Fangli Wu Liangchao Geng Kefei Ma MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation Remote Sensing radar echo extrapolation deep learning UNet multi-branch |
| title | MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation |
| title_full | MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation |
| title_fullStr | MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation |
| title_full_unstemmed | MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation |
| title_short | MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation |
| title_sort | mbfe unet a multi branch feature extraction unet with temporal cross attention for radar echo extrapolation |
| topic | radar echo extrapolation deep learning UNet multi-branch |
| url | https://www.mdpi.com/2072-4292/16/21/3956 |
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