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.

翻译：深度学习基础模型正通过利用海量数据学习通用表征，进而适应多样化的下游任务，从而在科学研究的诸多领域引发革命性变革。基础模型有望通过利用广阔的地球系统数据，彻底改变我们对地球及其子系统进行建模的能力。本文介绍Aurora——一个基于超过一百万小时多样化天气与气候数据训练而成的大规模大气基础模型。Aurora充分发挥基础建模方法的优势，为各类大气预测问题（包括训练数据有限、变量异构及极端事件等场景）生成业务化预报结果。该模型在一分钟内即可完成5天全球空气质量预测和10天高分辨率天气预报，其性能超越最先进的经典模拟工具与最优的专用深度学习模型。这些成果共同表明，基础模型具有变革环境预测领域的潜力。