Lead: Cong Gao
Counselor: Ning Lin, Michael Oppenheimer

This research project, led by Cong Gao, aims to advance both our understanding and ability to predict hurricanes (tropical cyclones) in the MACH region and around the world. A central part of this work is refining the Princeton environment‑dependent probabilistic tropical cyclone (PepC) model, which generates synthetic storms for hurricane risk assessment. By incorporating global environmental information, the updated model will be able to operate consistently across all major ocean basins.

While the Hurricane Analysis: Modeling Risk project focuses on generating databases of historical and future storms, Hurricane Analysis: Predictability and Environmental Drivers, examines the environmental processes that govern how storms form, intensify, weaken, and move. Insights from this approach help to improve model structure and interpretation.

Together, these efforts support more robust projections of how hurricanes and their associated hazards may change under future climate conditions. The core idea of this project can be thought of as a two-way loop: (1) better understanding enables better predictions, and (2) model evaluation against real observations helps identify gaps in understanding that motivate new scientific questions.