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.

通过利用海量数据学习通用表示方法，基础深度学习模型正在革命性地改变科学的许多方面。本研究介绍了 Aurora，一个基于大量多样化天气和气候数据训练的大规模大气基础模型。Aurora 能够在不到一分钟内生成 5 天的全球空气污染预测和 10 天的高分辨率天气预报，其性能超过了最先进的传统仿真工具和最佳专门深度学习模型。这些结果表明，基础模型可以改变环境预测。