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The authors introduce Aurora, a foundation model for Earth system forecasting trained on over one million hours of diverse geophysical data. Aurora achieves state-of-the-art performance in predicting air quality, ocean waves, tropical cyclone tracks, and high-resolution weather. Critically, Aurora attains these results at orders of magnitude lower computational cost compared to traditional numerical weather prediction models, demonstrating the potential of AI for democratizing access to accurate environmental forecasts.
A foundation model trained on a million hours of geophysical data crushes operational weather forecasts while slashing compute costs.
Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information. Aurora, a new large-scale foundation model trained on more than one million hours of diverse geophysical data, outperforms operational forecasts in predicting air quality, ocean wave dynamics, tropical cyclone tracks and high-resolution weather.
Microsoft Research