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

ICLR 2026 Conference Submission18536 Authors

Keywords: Tropical Meteorology, Data-Driven Weather Forecasting, Neural Weather Model, AI Weather Prediction, Benchmark, Dataset, Tropical Cyclone Forecasting, Numerical Weather Prediction

TL;DR: Benchmark dataset for evaluating Tropical Cyclone track and intensity predictions

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 physical (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.

Primary Area: datasets and benchmarks

Submission Number: 18536