TC-SINDy: Improving physics-based deterministic tropical cyclone track and intensity model via data-driven sparse identification of Nonlinear Dynamics.

Physics-based deterministic tropical cyclone (TC) simulation models play an important role in TC risk analysis, for their unique capability to represent physical principles and incorporate climate change impacts. However, establishing effective deterministic models can be challenging. In this paper, we present a simple yet powerful framework that distills physics-based TC models from historical TC data. By treating TC evolutions as dynamical systems, we adaptively discover the TC track and intensity governing equations from historical TC and environmental data via the Sparse Identification of Nonlinear Dynamics (SINDy) approach. Our proposed TC-SINDy approach is able to identify parsimonious, interpretable yet effective physics-based TC models. Compared to alternative physics-based deterministic models, our models match historical records much better in terms of the intensity evolution, prevailing tracks, and key parameter statistics across the coastlines of the Western North Pacific (WNP) basin and the North Atlantic (NA) basin. Our proposed models were finally coupled with a widely used parametric wind field, and the obtained probabilistic distribution of the surface wind also matched the historical histograms. Overall, the proposed method presents a general data-driven framework for discovering physics-based TC models, enabling reliable effective TC simulations while reserving the capacity to accommodate the changing climate. • Data-driven improvement to physics-based deterministic tropical cyclone (TC) models. • Maintain physical consistency and applicability to climate change. • Sparse machine learning to discover TC governing equations from historical data. • Results outperform existing deterministic models in both WNP and NA basins. [ABSTRACT FROM AUTHOR]