Articles | Volume 15, issue 6
https://doi.org/10.5194/amt-15-1829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Mar 2022
Research article |
| 25 Mar 2022
| 25 Mar 2022
Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images
Biao Tong
Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
Xiangfei Sun
Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, China
Jiyang Fu
CORRESPONDING AUTHOR
Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
Pakwai Chan
Hong Kong Observatory, Hong Kong, China
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Cited
16 citations as recorded by crossref.
- Cyclone detection with end-to-end super resolution and faster R-CNN M. Moustafa et al. https://doi.org/10.1007/s12145-024-01281-y
- Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images B. Tong et al. https://doi.org/10.3390/rs15174188
- Short-term prediction of tropical cyclone track and intensity via four mainstream deep learning techniques S. Gan et al. https://doi.org/10.1016/j.jweia.2023.105633
- A new and efficient method for tropical cyclone detection and tracking in gridded datasets T. Wu & Z. Duan https://doi.org/10.1016/j.wace.2023.100626
- AI-aided simulation of tropical cyclone genesis under climate change B. Tong et al. https://doi.org/10.1016/j.gloplacha.2025.104809
- Transformer-based full-track simulation of tropical cyclones B. Tong et al. https://doi.org/10.1016/j.jweia.2025.106176
- Assessment of approaching wind field for high-rise buildings based on wind pressure records via machine learning techniques Y. Liu et al. https://doi.org/10.1016/j.engstruct.2023.115663
- Advanced deep learning framework for full-track modeling of tropical cyclones under future climate scenarios B. Tong et al. https://doi.org/10.1016/j.gloplacha.2025.105083
- Enhancing tropical cyclone track and intensity predictions with the OWZP-Transformer model Z. Lin et al. https://doi.org/10.1038/s44387-025-00037-3
- Deep-learning based simulation of tropical cyclone genesis in Northwest Pacific B. Tong et al. https://doi.org/10.1016/j.jweia.2024.106003
- The impact of solar elevation angle on the net radiative effect of tropical cyclone clouds L. Hu et al. https://doi.org/10.1038/s41612-025-00964-7
- Identification of tropical cyclone centre based on satellite images via deep learning techniques T. Long et al. https://doi.org/10.1002/joc.7909
- Probabilistic structure analysis of fluctuating wind speed based on field measurement of super typhoon Doksuri J. Li et al. https://doi.org/10.1016/j.jweia.2024.105878
- Wind characteristics in typhoon boundary layer at coastal areas observed via a Lidar profiler T. Chen et al. https://doi.org/10.1016/j.jweia.2022.105253
- Contrastive Self-Supervised Learning-Based Tropical Cyclone Center Localization Method 仲. 曾 https://doi.org/10.12677/ag.2025.159121
- Short-term prediction of wind vector at multi-heights via deep learning techniques based on marine measurements from Light-Detection-and-Ranging device Y. He et al. https://doi.org/10.1063/5.0297152
16 citations as recorded by crossref.
- Cyclone detection with end-to-end super resolution and faster R-CNN M. Moustafa et al. https://doi.org/10.1007/s12145-024-01281-y
- Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images B. Tong et al. https://doi.org/10.3390/rs15174188
- Short-term prediction of tropical cyclone track and intensity via four mainstream deep learning techniques S. Gan et al. https://doi.org/10.1016/j.jweia.2023.105633
- A new and efficient method for tropical cyclone detection and tracking in gridded datasets T. Wu & Z. Duan https://doi.org/10.1016/j.wace.2023.100626
- AI-aided simulation of tropical cyclone genesis under climate change B. Tong et al. https://doi.org/10.1016/j.gloplacha.2025.104809
- Transformer-based full-track simulation of tropical cyclones B. Tong et al. https://doi.org/10.1016/j.jweia.2025.106176
- Assessment of approaching wind field for high-rise buildings based on wind pressure records via machine learning techniques Y. Liu et al. https://doi.org/10.1016/j.engstruct.2023.115663
- Advanced deep learning framework for full-track modeling of tropical cyclones under future climate scenarios B. Tong et al. https://doi.org/10.1016/j.gloplacha.2025.105083
- Enhancing tropical cyclone track and intensity predictions with the OWZP-Transformer model Z. Lin et al. https://doi.org/10.1038/s44387-025-00037-3
- Deep-learning based simulation of tropical cyclone genesis in Northwest Pacific B. Tong et al. https://doi.org/10.1016/j.jweia.2024.106003
- The impact of solar elevation angle on the net radiative effect of tropical cyclone clouds L. Hu et al. https://doi.org/10.1038/s41612-025-00964-7
- Identification of tropical cyclone centre based on satellite images via deep learning techniques T. Long et al. https://doi.org/10.1002/joc.7909
- Probabilistic structure analysis of fluctuating wind speed based on field measurement of super typhoon Doksuri J. Li et al. https://doi.org/10.1016/j.jweia.2024.105878
- Wind characteristics in typhoon boundary layer at coastal areas observed via a Lidar profiler T. Chen et al. https://doi.org/10.1016/j.jweia.2022.105253
- Contrastive Self-Supervised Learning-Based Tropical Cyclone Center Localization Method 仲. 曾 https://doi.org/10.12677/ag.2025.159121
- Short-term prediction of wind vector at multi-heights via deep learning techniques based on marine measurements from Light-Detection-and-Ranging device Y. He et al. https://doi.org/10.1063/5.0297152
Saved (final revised paper)
Latest update: 17 Jun 2026
Short summary
In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.
In recent years, there has been numerous research on tropical cyclone (TC) observation based on...
