26-May-2025 :

Microsoft has unveiled Aurora, a foundation AI model designed to integrate the power of machine learning with physical simulations to achieve unprecedented performance in weather forecasting and Earth system modeling. The model was detailed in a peer-reviewed Nature article that outlines Aurora’s architecture, training method, and real-world performance benchmarks.
Trained on over 1.3 million hours of diverse weather and satellite data using 1.3 trillion parameters, Aurora surpasses traditional weather simulation models in both accuracy and efficiency. Microsoft built the model using its Azure AI supercomputing infrastructure in collaboration with leading research institutions such as ECMWF, the UK Met Office, and academic labs from Oxford and Cambridge.
Aurora’s hybrid architecture allows it to simulate atmospheric dynamics, cloud behavior, ocean interactions, and other physical systems in a globally coherent yet highly granular fashion. Unlike pure LLMs, it encodes scientific constraints and real-world rules into its predictive logic, reducing hallucination risks in sensitive domains like climate policy or disaster preparedness.
In benchmark comparisons, Aurora achieved more accurate medium-range forecasts than traditional numerical weather prediction (NWP) systems like ECMWF’s HRES, while running orders of magnitude faster. The team claims Aurora can forecast temperature, wind, and precipitation with finer resolution across scales from hours to weeks.
Microsoft envisions Aurora as a foundation not just for weather forecasting, but also for climate modeling, agriculture planning, disaster readiness, and energy grid optimization. The project underscores a growing movement to fuse scientific computing with foundation model approaches to tackle global-scale challenges.