TDA-DARKNet: A Deep Learning Model Based on Dual-Polarization Radar Data for Tornado Detection.
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| Title: | TDA-DARKNet: A Deep Learning Model Based on Dual-Polarization Radar Data for Tornado Detection. |
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| Authors: | Zhang, Guoxiu1,2 (AUTHOR), Zeng, Qiangyu1,2,3,4 (AUTHOR), Zhang, Fugui1,2,3 (AUTHOR) zfg@cuit.edu.cn, Wang, Hao1,2,4 (AUTHOR), Yu, Tiantian1,2 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1124. 22p. |
| Subjects: | Convolutional neural networks, Radar signal processing, Thunderstorms, Deep learning, Artificial neural networks, Weather forecasting, Radar meteorology |
| Abstract: | Highlights: What are the main findings? We propose a novel tornado identification model, TDA-DARKNet, which combines convolutional neural networks with channel and spatial attention mechanisms to strengthen responses to key variables and spatial regions, while incorporating a Kolmogorov–Arnold Network (KAN) to enhance nonlinear representation capability. The model enhances tornado detection capability, particularly for weak tornado events. What are the implications of the main findings? Cross-regional evaluation using multiple independent tornado events indicates that the model maintains stable detection performance and good regional generalization. The results suggest that the transition from "handcrafted feature-driven" approaches to "deep representation-driven" frameworks constitutes a key technological pathway for improving tornado detection probability and extending warning lead time. Tornado is a localized, small-scale severe convective weather phenomenon characterized by extreme destructiveness. Tornado detecting and warning mainly rely on Doppler weather radar, which identifies and tracks tornadoes by recognizing the tornado vortex signature and supercells in radar data. Artificial intelligence technology has been applied to tornado recognition in recent years. However, existing monitoring methods, especially those using unsupervised learning algorithms, still have limited recognition accuracy and timely warning, and usually struggle to strike a balance between detection accuracy and false alarm rate. A novel tornado detection algorithm TDA-DARKNet has been proposed to address the aforementioned issues. The algorithm integrates a dual attention mechanism, dense residual connections, and Kolmogorov–Arnold network (KAN). A tornado dataset suitable for deep learning has been formed, which utilizes features including radial velocity, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient in radar data. The TDA-DARKNet algorithm was trained and tested using the tornado dataset, and evaluated in tornado cases. The experimental results show that TDA-DARKNet improves the detection probability and extends the lead time to a maximum of 42 min in strong tornado situations, while achieving 97.11% accuracy, 95.08% precision, indicating strong overall identification performance. In addition, by directly leveraging radar-based data for tornado identification, the algorithm eliminates the need for manual feature engineering, simplifies data processing, reduces complexity, and further enhances detection effectiveness. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose a novel tornado identification model, TDA-DARKNet, which combines convolutional neural networks with channel and spatial attention mechanisms to strengthen responses to key variables and spatial regions, while incorporating a Kolmogorov–Arnold Network (KAN) to enhance nonlinear representation capability. The model enhances tornado detection capability, particularly for weak tornado events. What are the implications of the main findings? Cross-regional evaluation using multiple independent tornado events indicates that the model maintains stable detection performance and good regional generalization. The results suggest that the transition from "handcrafted feature-driven" approaches to "deep representation-driven" frameworks constitutes a key technological pathway for improving tornado detection probability and extending warning lead time. Tornado is a localized, small-scale severe convective weather phenomenon characterized by extreme destructiveness. Tornado detecting and warning mainly rely on Doppler weather radar, which identifies and tracks tornadoes by recognizing the tornado vortex signature and supercells in radar data. Artificial intelligence technology has been applied to tornado recognition in recent years. However, existing monitoring methods, especially those using unsupervised learning algorithms, still have limited recognition accuracy and timely warning, and usually struggle to strike a balance between detection accuracy and false alarm rate. A novel tornado detection algorithm TDA-DARKNet has been proposed to address the aforementioned issues. The algorithm integrates a dual attention mechanism, dense residual connections, and Kolmogorov–Arnold network (KAN). A tornado dataset suitable for deep learning has been formed, which utilizes features including radial velocity, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient in radar data. The TDA-DARKNet algorithm was trained and tested using the tornado dataset, and evaluated in tornado cases. The experimental results show that TDA-DARKNet improves the detection probability and extends the lead time to a maximum of 42 min in strong tornado situations, while achieving 97.11% accuracy, 95.08% precision, indicating strong overall identification performance. In addition, by directly leveraging radar-based data for tornado identification, the algorithm eliminates the need for manual feature engineering, simplifies data processing, reduces complexity, and further enhances detection effectiveness. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081124 |
