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.

翻译：TCBench 是一个用于评估全球范围内短至中期（1-5天）热带气旋路径与强度预报的基准。为确保公平且模型无关的比较，TCBench 基于 IBTrACS 观测数据集，将热带气旋预报定义为在给定初始位置和强度的条件下，预测现有热带天气系统的时间演变。TCBench 涵盖了最先进的动力模型（TIGGE）与神经天气模型（AIFS、Pangu-Weather、FourCastNet v2、GenCast）。对于无法直接获取的基线路径，均使用 TempestExtremes 库从模型输出中一致地推导得出。在评估方面，TCBench 提供了确定性与概率性的风暴追踪指标。在 2023 年的测试案例中，神经天气模型能够熟练地预报热带气旋路径，而要获得熟练的强度预报则需要额外的步骤，例如后处理。TCBench 设计注重易用性，旨在帮助人工智能从业者应对领域相关的热带气旋挑战，并为热带气象学家提供数据驱动的工具与工作流程，以改进预测并加深对热带气旋过程的理解。通过降低对极端事件进行可复现、过程感知评估的门槛，TCBench 致力于推动数据驱动的热带气旋预报的普及。