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TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale

Year: 2026
Type: preprint
Abstract: TCBench is a benchmark for evaluating global, short to medium-range (1-5 days) forecasts of tropical cyclone (TC) track and intensity. To allow a fair and model-agnostic comparison, TCBench builds on the IBTrACS observational dataset and formulates TC forecasting as predicting the time evolution of an existing tropical system conditioned on its initial position and intensity. TCBench includes state-of-the-art physics-based (TIGGE) and Artificial Intelligence Weather Prediction (AIWP) models (AIFS, Pangu-Weather, FourCastNetV2, GenCast, FNV3P2). If not readily available, TC tracks are consistently derived from model outputs using the TempestExtremes library. TCBench provides deterministic and probabilistic storm-following metrics. On 2023 test cases, AIWP models skillfully forecast TC tracks, while realistic intensity forecasts require additional steps such as post-processing or task-specific training. Designed for accessibility, TCBench helps AI practitioners tackle domain-relevant TC challenges and equips tropical meteorologists with data-driven tools and workflows to improve prediction and TC process understanding. By lowering barriers to reproducible, process-aware evaluation of extreme events, TCBench aims to democratize data-driven TC forecasting.
DOI: 10.48550/arxiv.2601.23268
Authors: Gomez, Milton, McGraw, Marie, S., Saranya Ganesh, Tam, Frederick Iat-Hin, Azizi, Ilia, Darmon, Samuel, Feldmann, Monika, Bourdin, Stella, Poulain--Auzéau, Louis, Camargo, Suzana J., Lin, Jonathan, Chavas, Dan, Lee, Chia-Ying, Gupta, Ritwik, Jenney, Andrea, Beucler, Tom
Institutions: University of Lausanne, Colorado State University, University of Bern, ETH Zurich, University of Oxford, Lamont-Doherty Earth Observatory, Columbia University, Cornell University, Purdue University West Lafayette, University of California, Berkeley, Oregon State University
Language: English
Open Access status: green
Funders: National Science Foundation, National Aeronautics and Space Administration, Nvidia, DeepMind, Agence Nationale de la Recherche, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Natural Environment Research Council
Awards: RobustSR: Improving the Robustness of Super-Resolution Algorithms to Climate Change and Extreme Events, G1610782679, Idex Paris-Saclay, G3390771024, NSFGEO-NERC: HUrricane Risk Amplification and Changing North Atlantic Natural disasters (Huracan), G4866930200, AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES)
OpenAlex ID: W7127088427
Cites: 4
Cited by: 8
Related to: 10