Articles | Volume 19, issue 10
https://doi.org/10.5194/gmd-19-4009-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4009-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 May 2026
Development and technical paper |
| 18 May 2026
| 18 May 2026
From reanalysis to climatology: deep learning reconstruction of tropical cyclogenesis in the western North Pacific
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Tran-Binh Dang
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Anh-Duc Hoang Gia
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Duc-Hai Nguyen
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Minh-Hoa Tien
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Xuan-Truong Ngo
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Quang-Trung Luu
Université Paris-Saclay – CNRS – CentraleSupélec – L2S, Gif-sur-Yvette, 91192, France
Quang-Lap Luu
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
Tai-Hung Nguyen
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
Thanh T. N. Nguyen
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN 47405, USA
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Minh-Khanh Luong and Chanh Kieu
EGUsphere, https://doi.org/10.5194/egusphere-2025-1074, https://doi.org/10.5194/egusphere-2025-1074, 2025
Preprint archived
Short summary
Short summary
This work presents a deep learning (DL) model to retrieve tropical cyclone (TC) information from gridded data, a critical task for forecasting or downscaling TC intensity from climate outputs. Our DL model shows good capability for retrieving TC intensity/size when applied to climate data at 0.5-degree resolution. However, the model performance strongly depends on sampling methods, underscoring the complexities of applying DL models to new TC data. Potential improvements are also discussed.
The-Anh Vu and Chanh Kieu
EGUsphere, https://doi.org/10.5194/egusphere-2023-974, https://doi.org/10.5194/egusphere-2023-974, 2023
Preprint archived
Short summary
Short summary
Tropical cyclone (TC) formation has been well documented to possess distinct clusters globally. Using theoretical analyses and numerical simulations, this study proposes a new mechanism for global TC clustering, which is a result of tropical large-scale dynamics even in the absence of zonal sea surface temperature (SST) variations. Our findings offer a new understanding into the role of tropical waves in producing global TC clusters beyond traditional explanations based on zonal SST anomalies.
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Short summary
We study how and where tropical storms, i.e., tropical cyclogenesis, begin in the Western North Pacific. Using many years of global weather data and a modern pattern-recognition method, i.e., deep learning, we built a model that learns signals that come before storm formation and maps when and where formation is likely. It reproduces known seasonal and regional patterns and identifies key environmental cues. These results can support better risk planning and help refine climate projections.
We study how and where tropical storms, i.e., tropical cyclogenesis, begin in the...
