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Climate

Aurora: A Foundation Model for General-Purpose Atmospheric Forecasting

Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic

Featured May 25, 2026

Original Paper

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Simply

A new AI model called Aurora learns from tons of Earth data to predict weather, air pollution, and ocean waves much faster and better than old methods, making crucial forecasts easier for everyone.

In depth
Aurora is a large-scale foundation model for Earth system forecasting, trained on over a million hours of diverse data. It employs a flexible 3D Swin Transformer U-Net architecture with Perceiver-based encoders and decoders to process heterogeneous Earth system variables and resolutions. This enables it to outperform specialized operational forecasts for air quality, ocean waves, tropical cyclone tracks, and high-resolution weather, while being orders of magnitude more computationally efficient.

Key Takeaways

  • 1
    Aurora is the first foundation model for Earth systems, demonstrating superior performance across diverse forecasting tasks like air quality, ocean waves, and tropical cyclones.
  • 2
    The model achieves orders of magnitude faster predictions than traditional operational systems, making complex Earth system forecasts more accessible.
  • 3
    Its pretraining-fine-tuning protocol on a vast, diverse dataset enables efficient adaptation to new tasks and resolutions, including high-resolution weather forecasting at 0.1°.

Conceptual Flow

HIGH LEVEL
1
Methodology: How Aurora Works

Aurora takes all kinds of Earth data, squishes it into a standard form, makes predictions using a smart AI brain, and then turns it back into easy-to-understand forecasts.

Mixed Earth Data
AI Brain Processes
Accurate Forecasts
2
Results: Impact on Forecasting

This new AI model beats traditional forecasts for many important Earth predictions, like hurricanes and air quality, while using much less computer power.

Old Forecasts
New AI Beats Old
Better, Faster Predictions