Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7303-2026
https://doi.org/10.5194/gmd-19-7303-2026
Model description paper
 | 
06 Aug 2026
Model description paper |  | 06 Aug 2026

SPIN (v1.0): A spontaneous synthetic tropical cyclone model empowered by NeuralGCM for hazard assessment

Yurong Gao and Dazhi Xi

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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Cited articles

Baxter, I., Pahlavan, H., Hassanzadeh, P., Rucker, K., and Shaw, T.: Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM, https://doi.org/10.48550/arXiv.2510.04466, 2025. 
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. 
Bieli, M., Sobel, A. H., Camargo, S. J., and Tippett, M. K.: A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones, Weather Forecast., 35, 451–466, https://doi.org/10.1175/WAF-D-19-0045.1, 2020. 
Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., and Aerts, J. C. J. H.: Generation of a global synthetic tropical cyclone hazard dataset using STORM, Sci. Data, 7, 40, https://doi.org/10.1038/s41597-020-0381-2, 2020. 
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Artificial intelligence offers a new way to assess tropical cyclone hazards. We developed a hybrid hazard model that combines a Neural General Circulation Model for storm tracks with a dynamical method for intensity. Our results highlight the potential of rapid, low-cost, hourly simulations of synthetic tropical cyclones to assess compound hazards, exemplified by the model's ability to represent tropical cyclone clusters with dynamical connections.
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