  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 dataset and
evaluation metrics make inter-comparison between studies difficult. Here we
present a benchmark dataset for data-driven medium-range weather forecasting, 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 dataset 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 dataset will
accelerate research in data-driven weather forecasting.
