Use of NVIDIA FourCastNet model to improve tropical cyclones risk modelling.

AXA is developing in-house Natural Hazard models (or Natural Catastrophe models) in order to gain a deeper understanding of, evaluate, and monitor the climate risks underpinning (re)insurance portfolios. Such models simulate large numbers of synthetic weather events to estimate the probability of rare and extreme events, enabling more robust risk management and informed decision-making.

AI-driven weather models offer the capability to rapidly produce thousands of unique ensemble scenarios of low-likelihood high-impact weather events such as tropical cyclones. This study specifically utilizes tropical cyclones (TCs) as a primary illustration of the potential of AI-based weather models for risk management.

In this study we use FourCastNet SFNO, the global data-driven weather forecasting model developed by NVIDIA available on the NVIDIA Earth-2 platform to simulate historical but also synthetic (i.e. never observed) hurricanes. SFNO trained on ECMWF ERA5 reanalysis data provides short to medium-range global predictions at 0.25° resolution. A large ensemble of hurricane simulations is performed using the HENS method, developed at Berkeley, the NVIDIA leveraging Earth2Studio from NVIDIA’s Earth-2 platform.

HENS-SFNO performance is first assessed by evaluating the model's ability to reproduce post-2017 historical hurricanes (intensity, track, landfall location). HENS-SFNO capabilities in simulating synthetic hurricanes are then assessed in a second step by evaluating track density and landfall frequencies by categories of hurricanes against the historical tropical cyclone IBTrACS database.