An Environment-Dependent Probabilistic Tropical Cyclone Model

The Princeton environment-dependent probabilistic tropical cyclone (PepC) model is developed for generating synthetic tropical cyclones (TCs) to support TC risk assessment. PepC consists of three components: a hierarchical Poisson genesis model, an analog-wind track model, and a Markov intensity model. The three model components are dependent on environmental variables that vary with the climate, including potential intensity, advection flow, vertical wind shear, relative humidity, and ocean-cooling parameters. The present model is developed for the North Atlantic Basin. The three model components and the integrated model are verified against observations using out-of-sample testing. The model can generally capture the TC climatology and reproduce statistics of TC genesis, movement, rapid intensification, and lifetime maximum intensity, as well as local landfall frequency and intensity. It can be coupled with climate models and TC hazard models to quantify TC-related wind, surge, and rainfall risks under various climate conditions. The modeling framework can be further improved when more relevant environmental variables are identified and become available in climate model outputs. Plain Language Summary The Princeton environment-dependent probabilisitic tropical cyclone model (PepC) is developed to generate synthetic tropical cyclones (TCs). The PepC has three components: a genesis model that simulates the time and location of a storm's formation, a track model that simulates the storm's movement over the ocean, and an intensity model that simulates the storm's maximum wind speed evolving along the track. We evaluate PepC's performance by comparing simulated TCs with historical records in the North Atlantic Basin. Our results show that under the current climate, simulated TCs from PepC match well with observations in many aspects, including wide TC counts and local landfall intensity. As all of its three model components are dependent on environmental variables that vary with the climate, PepC is able to generate synthetic storms under various climate conditions. The synthetic TCs generated from PepC can be applied to quantify TC-related wind, surge, and rainfall risks in a changing climate.