HurriCast: Synthetic Tropical Cyclone Track Generation for Hurricane Forecasting
This work addresses the need for synthetic TC tracks to aid insurance companies, governments, and policymakers in disaster preparedness and risk analysis, though it is incremental as it builds on existing methods.
The study tackled the problem of generating synthetic tropical cyclone tracks for risk assessment by developing a hybrid method combining ARIMA, K-Means, and Autoencoder based on historical HURDAT2 data, resulting in an efficient and reliable approach for climate modeling and risk assessment.
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. Insurance companies use these synthetic tracks to estimate the potential risks and financial impacts of future TCs. 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. In this study, many 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 capture better 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.