Generate synthetic tropical cyclone tracks impacting North American using statistical and machine learning methods

The generation of synthetic tropical cyclone (TC) tracks for risk assessment is a critical application of preparedness for the impacts of climate change and disaster relief, particularly in North America. For governments and policymakers, understanding the potential impacts of TCs helps in developing effective emergency response strategies, updating building codes, and prioritizing investments in resilience and mitigation projects. Insurance companies use these synthetic tracks to estimate the potential risks and financial impacts of future TCs. In this study, a large number of hypothetical but plausible TC scenarios are created based on historical TC data HURDAT2 (HURricane DATa 2nd generation). A hybrid methodology, combining the ARIMA and K-MEANS methods with Autoencoder, is employed to better capture historical TC behaviors and project future trajectories and intensities. It demonstrates an efficient and reliable in the field of climate modeling and risk assessment. By effectively capturing past hurricane patterns and providing detailed future projections, this approach not only validates the reliability of this method but also offers crucial insights for a range of applications, from disaster preparedness and emergency management to insurance risk analysis and policy formulation.