A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

TL;DR

This paper introduces WHITS, a semi-Markov tropical cyclone track generator that creates a 10,000-year global synthetic catalog, improving risk assessment by capturing realistic storm behaviors conditioned on wind speed.

Contribution

The novel WHITS model extends previous frameworks by incorporating wind-dependent transitions, sharpening kernel selection, and smoothing transitions, producing a long-term, physically plausible cyclone catalog.

Findings

The 10,000-year catalog reproduces observed track density.

It accurately estimates hurricane/typhoon wind-hit probabilities.

The catalog is useful for catastrophe risk applications.

Abstract

Reliable assessment of tropical cyclone (TC) risk is limited by the brevity and spatial sparsity of the historical record, particularly for the rare, high-intensity landfalls that dominate insured loss. We present WHITS (Wind-focused Hurricane Interactive Track Simulator), a non-parametric semi-Markov track generator that extends the HITS framework of Nakamura et al. (2015) in three ways: transitions between historical track segments are conditioned on local wind speed in addition to position, age, and forward vector; the kernel selection on the comparative-vector term is sharpened to suppress dynamically inconsistent jumps; and a short smoothing window is applied across each transition to remove the position and wind discontinuities reported by downstream surge users. WHITS is fit to the full available best-track record in each of six basins in IBTrACS, extending in the North Atlantic to 1851 and in other basins to the earliest year of reliable best-track data. The resulting 10,000-yr global synthetic catalog reproduces observed track density and the annual hurricane/typhoon-force wind-hit probability across all basins. The catalog is intended for catastrophe-risk applications where a large, low-bias sample of physically plausible tracks is more useful than a small, statistically corrected one.