  Deep learning foundation models are revolutionizing many facets of science by
leveraging vast amounts of data to learn general-purpose representations that
can be adapted to tackle diverse downstream tasks. Foundation models hold the
promise to also transform our ability to model our planet and its subsystems by
exploiting the vast expanse of Earth system data. Here we introduce Aurora, a
large-scale foundation model of the atmosphere trained on over a million hours
of diverse weather and climate data. Aurora leverages the strengths of the
foundation modelling approach to produce operational forecasts for a wide
variety of atmospheric prediction problems, including those with limited
training data, heterogeneous variables, and extreme events. In under a minute,
Aurora produces 5-day global air pollution predictions and 10-day
high-resolution weather forecasts that outperform state-of-the-art classical
simulation tools and the best specialized deep learning models. Taken together,
these results indicate that foundation models can transform environmental
forecasting.
