TailWeather：从尾部到极端，一个用于机器学习天气预报的全球气候数据集
TailWeather: from tail to extremes, a global climatological dataset for machine-learning weather forecasting
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中文总结 AI 辅助
TailWeather是一个基于ERA5的全球0.25度陆地气候数据集，覆盖1981-2023年，提供热浪、寒潮、强降水、极端风和干旱的严重性与强度评分，为机器学习天气预报评估极端事件提供可复用物理目标。
中文摘要 AI 辅助
天气预报模型通常使用短期预报的平均预报技能来评估性能。然而，这并不一定意味着在天气分布的尾部具有技能。评估尾部事件需要一个具有广泛时空覆盖的一致定义的目标。灾害目录记录了社会后果，但它们是稀疏的且依赖于报告；气候学尾部描述了异常天气，但不一定意味着危害。我们提出了TailWeather，一个全球0.25度、仅陆地的数据集，源自ERA5，覆盖1981-2022年并延伸至2023年1月。它每日标记热浪、寒潮、强降水和极端风，每月标记气象干旱。每个事件都有一个序数严重性等级和一个参照当地1991-2020年气候的数值强度评分。这些评分支持在其存储分辨率和有效域内的替代阈值。与记录的灾害进行比较显示，在更严格的尾部下影响富集度更高，但不同灾害之间存在差异，且目录覆盖存在显著缺口。预报示例说明了低平均误差如何与弱事件检测共存，特别是对于风。TailWeather为研究和评估极端事件提供了一个可复用的物理目标，同时补充了灾害目录中的信息。
英文摘要
Weather forecasting models commonly use the average forecast skill for short-range forecasts. However, it does not necessarily imply skill in the tails of the weather distribution. Evaluating tail events requires a consistently defined target with broad spatial and temporal coverage. Disaster catalogs record societal consequences, but are sparse and reporting-dependent; climatological tails describe unusual weather without necessarily implying harm. We present TailWeather, a global 0.25-degree, land-only dataset derived from ERA5, covering 1981-2022 and extending into January 2023. It labels heatwaves, cold waves, heavy precipitation, and extreme wind daily, and meteorological drought monthly. Each event has an ordinal severity tier and a numerical intensity score referenced to the local 1991-2020 climate. The scores support alternative thresholds within their stored resolution and valid domain. Comparison with documented disasters shows greater impact enrichment towards stricter tails, with differences among hazards and substantial gaps in catalog coverage. Forecast examples illustrate how low average errors can coexist with weak event detection, particularly for wind. TailWeather provides a reusable physical target for studying and evaluating extremes, while complementing the information in disaster catalogs.
发表机构
- LUT University（拉赫蒂理工大学）
- University of Helsinki（赫尔辛基大学）
- Finnish Meteorological Institute (FMI)（芬兰气象研究所）
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