Artificial-intelligence tools have transformed weather forecasting, thanks to their ability to learn past patterns from observations and predict how future patterns might unfold. But until now, models have sometimes struggled to forecast extreme weather events that they’ve never seen before, but that are happening more frequently as the planet warms.
It’s like trying to “forecast the future with yesterday’s climate”, as Jacob Landsberg, a data scientist at Boston University in Massachusetts, puts it.
One new approach that’s showing success combines an AI model with a conventional climate model, plus mathematical tools that describe rare events, to forecast weather extremes more effectively. In early tests, this hybrid approach simulated the probabilities of extreme heatwaves as accurately as the older, non-AI method, which takes much longer to run1.
“We think this is the way forward,” says Pedram Hassanzadeh, a climate physicist at the University of Chicago in Illinois who is involved in several of the early studies. Team members posted the results in a series of preprints on arXiv this year, and will discuss the work at upcoming conferences, including the American Geophysical Union meeting this month.
Limited training data
Extreme weather can be particularly dangerous when it goes beyond what people usually experience. Examples include the deep freeze in Texas in 2021 that killed hundreds of people, and the 2010 heatwave in Moscow that killed more than 10,000. But it’s hard to reproduce the statistics of such rare events and accurately predict how they might change in the future, Hassanzadeh says. AI models might be working with only 40 years of training data but need to forecast a weather extreme that happens just once every 1,000 years.