https://www.nytimes.com/2025/05/21/climate/ai-weather-models-aurora-microsoft.html
Weather forecasters rely on models to help them make decisions that can have life-or-death consequences, so any advantage is welcome. Artificial intelligence holds promise to deliver more accurate forecasts quickly, and tech companies including Google, Nvidia and Huawei have produced A.I.-based forecasting models.
The latest entrant is Aurora, an A.I. weather model from Microsoft, and it stands out for several reasons, according to a report published Wednesday in the journal Nature. It’s already in use at one of Europe’s largest weather centers, where it’s running alongside other traditional and A.I.-based models.
The Aurora model can make accurate 10-day forecasts at smaller scales than many other models, the paper reports.
And it was built to handle not only weather, but also any Earth system with data available. That means it can be trained, relatively easily, to forecast things like air pollution and wave height in addition to weather events like tropical cyclones. Users could add almost any system they like down the road; for instance, one start-up has already honed the model to predict renewable energy markets.
“I’m most excited to see the adoption of this model as a blueprint that can add more Earth systems to the prediction pipeline,” said Paris Perdikaris, a professor at the University of Pennsylvania who led the development of Aurora while working at Microsoft.
It’s also fast, able to return results in seconds as opposed to the hours that non-A.I. models can take.
Traditional models, the basis of weather forecasting over the last 70 years, use layers of complex mathematical equations to represent the physical world: the sun heating the planet, winds and ocean currents swirling around the globe, clouds forming, and so on.
Researchers then add real weather data and ask the computer models to predict what will happen next. Human forecasters look at results from many of these models and combine those with their own experience to tell the public what scenario is most likely.
“Final forecasts are ultimately made by a human expert,” Dr. Perdikaris said. (That is true for A.I.-based forecasts, too.)
This system has worked well for decades. But the models are incredibly complex and require expensive supercomputers. They also take many years to build, making them difficult to update, and hours to run, slowing down the forecasting process.
Artificial intelligence weather forecasting models are faster to build, run and update. Researchers feed the models on huge amounts of weather and climate data and train them to recognize patterns. Then, based on these patterns, the model predicts what comes next.
But the A.I. models still need equation-based models and real-world data for their starting points, and for reality checks.
“It doesn’t know the laws of physics, so it could make up something completely crazy,” said Amy McGovern, a computer scientist and meteorologist at the University of Oklahoma who was not involved in the study. So most, but not all, A.I. weather forecasting models still rely on data and the physics-based models in some capacity, and human forecasters need to interpret results carefully.
Dr. Perdikaris and his collaborators built Aurora using this method, training it on data from physics-based models and then making purely A.I. predictions, but they didn’t want it to be limited to weather. So they trained it on multiple, big Earth system data sets, creating a broad background of artificial expertise
Aurora “is an important step toward more versatile forecasting systems,” said Sebastian Engelke, a professor of statistics at the University of Geneva who was not involved in the study. The model’s flexibility and resolution are its most novel contributions, he said.
As in other areas, there’s been a big push toward using A.I. for weather forecasting in the past few years, but the major A.I. forecasting models are still global, not local. Forecasts at the scale of a single storm barreling toward a city need to come from a specialized model, and those are mostly the old-school variety, at least for now.
Extreme weather events like heat waves or heavy downpours are still challenging for both traditional and A.I. models to predict.
A.I. forecasting models need careful calibration and human verification before they’re widely used, Dr. Perdikaris said. But some are already being tested in the real world. The European Center for Medium-Range Weather Forecasting, which provides meteorological forecasts to dozens of countries, developed their own A.I. forecasting model, which they deployed in February. They use that, along with Aurora and other A.I. models, for their weather services. They’ve had a good experience using A.I. models so far.
“It’s absolutely an exciting time,” said Peter Düben, who leads the European center’s Earth modeling team.
Other researchers are more conservative, given the checks and improvements the models need. And artificial intelligence tools come with a significant energy cost to train, though Dr. Perdikaris said this would be worth it in the long run as more people use the models.
“We’re all in the hype right now,” said Dr. McGovern, who leads the NSF’s institute that studies trust in A.I. applications to climate and weather problems. “A.I. weather is amazing. But I think there’s still a long way to go.”
And the Trump administration’s cuts to agencies including the National Oceanic and Atmospheric Administration, the National Science Foundation and the National Weather Service could stymie further improvements in A.I. forecasting tools, because federal data sets and models are critical to developing and improving A.I. models, Dr. Perdikaris said.
“It’s quite unfortunate, because I think it’s going to slow down progress,” he said.
