Traditionally reliant on physics-based models and supercomputers, forecasting is now being accelerated and enhanced by machine learning systems that can process massive datasets and detect subtle atmospheric patterns. This shift provides faster, more accurate and more localised weather and environmental forecasting.
Aurora from Microsoft is an AI foundational model that predicts the weather with precision and also forecasts a broad range of environmental events, from ocean waves to air pollution.
As detailed in a recent paper published in Nature, Aurora represents a significant advancement in using AI to understand and anticipate Earth system phenomena.
Aurora is a large-scale AI foundation model, trained initially on over one million hours of diverse atmospheric data, including satellite imagery, radar readings, weather station data and simulation outputs.
“It’s not just about weather anymore,” said Megan Stanley, a senior researcher on the Aurora project.
According to Microsoft’s blog post, Aurora beat traditional numerical models and prior AI systems in 91% of forecasting benchmarks for medium-range forecasts, up to 14 days, at a resolution of 0.25 degrees. It also outperformed major global forecasting centres in cyclone tracking, setting a new standard by correctly predicting Typhoon Doksuri’s landfall in the Philippines, four days ahead of the event, while official forecasts missed the mark.
Leveraging high-performance GPUs, the model can produce forecasts in seconds, around 5,000 times faster than current supercomputer-based weather systems.
In one test, it accurately predicted a massive sandstorm in Iraq 24 hours in advance, despite limited data, by using its foundational understanding of atmospheric patterns. It also demonstrated high accuracy in forecasting ocean wave heights and directions, essential for maritime safety and disaster preparedness.
Moreover, MSN Weather is already integrating Aurora’s capabilities to provide users with more accurate hourly forecasts and expanded weather parameters.
“There’s a huge opportunity here, especially for countries underserved by traditional forecasting tools. Aurora allows for high-resolution, localised predictions with much lower operational costs,” Stanley said.
“If it truly is learning physics correctly, it can be adapted to different climate settings with confidence,” she explained. “Aurora is the first of its kind—but it won’t be the last.”
