  The advents of Artificial Intelligence (AI)-driven models marks a paradigm
shift in risk management strategies for meteorological hazards. This study
specifically employs tropical cyclones (TCs) as a focal example. We engineer a
perturbation-based method to produce ensemble forecasts using the advanced
Pangu AI weather model. Unlike traditional approaches that often generate fewer
than 20 scenarios from Weather Research and Forecasting (WRF) simulations for
one event, our method facilitates the rapid nature of AI-driven model to create
thousands of scenarios. We offer open-source access to our model and evaluate
its effectiveness through retrospective case studies of significant TC events:
Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017), affecting
regions across North America, East Asia, and Australia. Our findings indicate
that the AI-generated ensemble forecasts align closely with the European Centre
for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven
days prior to landfall. This approach could substantially enhance the
effectiveness of weather forecast-driven risk analysis and management,
providing unprecedented operational speed, user-friendliness, and global
applicability.
