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Microsoft Research introduced Aurora, a versatile model trained on over one million hours of diverse geophysical data. Researchers claim Aurora can predict weather, air quality, ocean waves, and tropical cyclone tracks more accurately and efficiently than current operational systems. The model achieves state-of-the-art performance across multiple domains: it beats the Copernicus Atmosphere Monitoring Service (CAMS) on 74 percent of air pollution forecasting targets, surpasses ocean wave models on 86 percent of targets, outperforms seven operational centers for tropical cyclone tracking, and exceeds high-resolution weather models on 92 percent of targets. Aurora’s architecture uses a 3D Swin Transformer that can handle different resolutions, variables, and pressure levels, making it adaptable to various Earth system prediction tasks through fine-tuning. The model operates at computational speeds that are orders of magnitude faster than traditional numerical models — for example, generating air pollution forecasts approximately 100,000 times faster than CAMS while running on a single GPU. For machine learning researchers, Aurora may help develop architectures that can efficiently process 3D spatiotemporal data while maintaining physical consistency across multiple scales and modalities. (Nature)