TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | 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 |
| author_facet | 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 |
| contents | 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 dynamical (TIGGE) and neural weather models (AIFS, Pangu-Weather, FourCastNet v2, GenCast). If not readily available, baseline tracks are consistently derived from model outputs using the TempestExtremes library. For evaluation, TCBench provides deterministic and probabilistic storm-following metrics. On 2023 test cases, neural weather models skillfully forecast TC tracks, while skillful intensity forecasts require additional steps such as post-processing. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23268 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale 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 Computational Engineering, Finance, and Science 86A10 J.2 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 dynamical (TIGGE) and neural weather models (AIFS, Pangu-Weather, FourCastNet v2, GenCast). If not readily available, baseline tracks are consistently derived from model outputs using the TempestExtremes library. For evaluation, TCBench provides deterministic and probabilistic storm-following metrics. On 2023 test cases, neural weather models skillfully forecast TC tracks, while skillful intensity forecasts require additional steps such as post-processing. 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. |
| title | TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale |
| topic | Computational Engineering, Finance, and Science 86A10 J.2 |
| url | https://arxiv.org/abs/2601.23268 |