Learning a Stochastic Differential Equation Model of Tropical Cyclone Intensification from Reanalysis and Observational Data

• URL: http://arxiv.org/abs/2601.08116v1
• Date: Tue, 13 Jan 2026 01:11:17 GMT
• Title: Learning a Stochastic Differential Equation Model of Tropical Cyclone Intensification from Reanalysis and Observational Data
• Authors: Kenneth Gee, Sai Ravela,
• Abstract summary: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited dataset size and quality.<n>Models of cyclone intensification fill this data gap by simulating huge ensembles of synthetic hurricanes based on estimates of the storm's large scale environment.<n>Our results show promise that interpretable, physics style models of complex earth system dynamics can be learned using automated system identification techniques.
• Score: 1.5469452301122175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited dataset size and quality. Models of cyclone intensification fill this data gap by simulating huge ensembles of synthetic hurricanes based on estimates of the storm's large scale environment. Both physics-based and statistical/ML intensification models have been developed to tackle this problem, but an open question is: can a physically reasonable and simple physics-style differential equation model of intensification be learned from data? In this paper, we answer this question in the affirmative by presenting a 10-term cubic stochastic differential equation model of Tropical Cyclone intensification. The model depends on a well-vetted suite of engineered environmental features known to drive intensification and is trained using a high quality dataset of hurricane intensity (IBTrACS) with estimates of the cyclone's large scale environment from a data-assimilated simulation (ERA5 reanalysis), restricted to the Northern Hemisphere. The model generates synthetic intensity series which capture many aspects of historical intensification statistics and hazard estimates in the Northern Hemisphere. Our results show promise that interpretable, physics style models of complex earth system dynamics can be learned using automated system identification techniques.

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