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

Milton Gomez; Marie McGraw; Saranya Ganesh S.; Frederick Iat-Hin Tam; Ilia Azizi; Samuel Darmon; Monika Feldmann; Stella Bourdin; Louis Poulain--Auzéau; Suzana J. Camargo; Jonathan Lin; Dan Chavas; Chia-Ying Lee; Ritwik Gupta; Andrea Jenney; Tom Beucler

arXiv · 2601.23268

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

Milton Gomez·Marie McGraw·Saranya Ganesh S.·Frederick Iat-Hin Tam·Ilia Azizi·Samuel Darmon·Monika Feldmann·Stella Bourdin·Louis Poulain--Auzéau·Suzana J. Camargo·Jonathan Lin·Dan Chavas·Chia-Ying Lee·Ritwik Gupta·Andrea Jenney·Tom Beucler

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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, FourCastNet v2, GenCast, FNV3). If not readily available (e.g., from the NOAA website as is done with TIGGE), 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 skillful 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.

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Milton Gomez, Marie McGraw, Saranya Ganesh S., Frederick Iat-Hin Tam, Ilia Azizi, Samuel Darmon, Monika Feldmann, Stella Bourdin, Louis Poulain--Auzéau, Suzana J. Camargo, Jonathan Lin, Dan Chavas, Chia-Ying Lee, Ritwik Gupta, Andrea Jenney, Tom Beucler. 2026-01-30. TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale. https://arxiv.org/abs/2601.23268

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