Global 10,000-Year Stochastic Tropical Cyclone Track & Wind Catalog

Access, Licensing, and Sponsored Research (Columbia Technology Ventures)

WHITS provides an unbiased global 10,000-year stochastic catalog of tropical cyclone (TC) tracks and associated maximum sustained wind speeds generated by the Wind-focused Hurricane Interactive Track Simulator (WHITS). The framework is designed to support climate risk analysis, insurance applications, and extreme event research over operational planning time scales.

The model preserves track memory and produces realistic tracks, track densities, and hurricane/typhoon-force exceedence probabilities, validated against IBTrACS in all six tropical cyclone basins. WHITS builds on its predecessor HITS with additional wind parameters and smoother transitions to better support risk and hazard modeling applications. The catalog package includes the full stochastic catalog, supporting Python code used for data generation.

Access to the WHITS stochastic catalog and generation framework is available through Columbia Technology Ventures for research collaborations, sponsored research, and licensing. Click here to explore current sponsored research directions and collaboration opportunities.

For access to the catalog, licensing inquiries, or to discuss collaborative research tailored to specific risk, climate, or insurance applications, please contact Dovina Qu at techtransfer@columbia.edu.

Unbiased Tracks and Landfalls

Limited historical data on TC landfalls makes it difficult to accurately estimate and correct model biases, especially for rare, high-impact events. Simple bias corrections often assume uniformity across regions and storm intensities, which is rarely accurate, and correcting for specific locations is especially challenging due to data sparsity and uncertainty. Overfitting is a risk, and ignoring dependencies between landfall frequency and storm characteristics can produce unrealistic results. The WHITS model addresses these issues by using non-parametric semi-Markov resampling to adapt and reassemble historical storm track segments. It preserves track memory while generating 10,000 realistic synthetic seasons for global insurance risk analysis and coastal planning, without requiring downstream bias correction. The resulting tracks are not exact reproductions of historical segments; they are adapted from historical data to improve realism and applicability for risk modeling.

Who we are

• Jennifer Nakamura
Research Scientist in Ocean and Climate Physics at the Lamont-Doherty Earth Observatory of Columbia University. Her background is in atmospheric science and hydroclimatology. She studies the extremes of precipitation in both floods and droughts and their respective causes (like TC!), along with climate variability and ocean-atmosphere interactions.

• Upmanu Lall
Visiting Professor at the Columbia University Department of Earth and Environmental Engineering and founder of the Columbia Water Center. Professor at the School of Complex Adaptive Systems and Director of the Water Institute at Arizona State University. He has broad interests in hydrology, climate dynamics, water resource systems analysis, risk management and sustainability, machine learning, deep learning, and AI. He is motivated by challenging questions at the intersection of these fields, especially where they have relevance to societal outcomes or to the advancement of science toward innovative application.