Data‐driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data‐driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common data set and evaluation metrics make intercomparison between studies difficult. Here we present a benchmark data set for data‐driven medium‐range weather forecasting (specifically 3–5 days), a topic of high scientific interest for atmospheric and computer scientists alike. We provide data derived from the ERA5 archive that has been processed to facilitate the use in machine learning models. We propose simple and clear evaluation metrics which will enable a direct comparison between different methods. Further, we provide baseline scores from simple linear regression techniques, deep learning models, as well as purely physical forecasting models. The data set is publicly available at https://github.com/pangeo‐data/WeatherBench and the companion code is reproducible with tutorials for getting started. We hope that this data set will accelerate research in data‐driven weather forecasting.

中文翻译：

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WeatherBench：基于数据的天气预报的基准数据集

数据驱动的方法（最著名的是深度学习）已成为许多领域进行预测的强大工具。一个自然的问题是，是否也可以使用数据驱动的方法提前几天预测全球天气状况。最初的研究显示出希望，但缺乏通用的数据集和评估指标使研究之间的相互比较变得困难。在这里，我们为数据驱动的中期天气预报（特别是3至5天）提供了一个基准数据集，这是大气科学和计算机科学家都高度关注的主题。我们提供从ERA5档案中获取的数据，该数据已过处理，以方便在机器学习模型中使用。我们提出了简单明了的评估指标，从而可以在不同方法之间进行直接比较。进一步，我们提供来自简单线性回归技术，深度学习模型以及纯粹物理预测模型的基线得分。该数据集可在https://github.com/pangeo‐data/WeatherBench上公开获得，并且随附的代码可通过入门教程进行复制。我们希望该数据集将加速以数据为依据的天气预报的研究。