AI can predict the weather - but what about extreme events?

Artificial intelligence (AI) is already impressing scientists with its ability to forecast the weather days in advance. These new models can run faster and use far less computing power than traditional weather systems.

But there’s a catch: AI can only predict what it’s seen before. And when it comes to extreme weather, that’s a serious problem.

Researchers from the University of Chicago, New York University, and the University of California Santa Cruz set out to explore this issue.

The study shows that while AI can do a great job with typical day-to-day forecasting, it struggles with rare or unprecedented events. These include things like Category 5 hurricanes, once-in-a-century floods, or heat waves that break all historical records.

Clever models with clear limits

AI models like ChatGPT learn from massive amounts of data. Weather-focused neural networks work the same way. Scientists feed them decades of past weather observations. Based on that, the models learn to predict what might happen next.

When you give these models the latest weather data, they can quickly generate forecasts that rival the accuracy of traditional supercomputer models. But the problem comes when something totally unexpected happens.

Pedram Hassanzadeh is an associate professor of geophysical sciences at UChicago and a corresponding author of the study.

“AI weather models are one of the biggest achievements in AI in science. What we found is that they are remarkable, but not magical,” said Hassanzadeh. “We’ve only had these models for a few years, so there’s a lot of room for innovation.”

AI underestimates dangerous weather

The concern is this: if a dangerous weather event hasn’t happened in the training data, will the AI model still catch it?

The researchers tested this by training an AI model with weather data that excluded all hurricanes above Category 2 strength. Then they asked the model to forecast a scenario that would typically lead to a Category 5 hurricane. The result?

“It always underestimated the event. The model knows something is coming, but it always predicts it’ll only be a Category 2 hurricane,” said Yongqiang Sun, research scientist at UChicago and co-author of the study.

This kind of error is called a false negative. In weather forecasting, it can be deadly. Overestimating a storm might cause an unnecessary evacuation, which is costly but not dangerous. Underestimating it, however, can leave people unprepared for disaster.

AI remembers extreme weather

Traditional weather models use equations that describe how the atmosphere works. They include physics, math, and knowledge of how heat, pressure, and wind interact.

Neural networks don’t do this. They’re more like fancy autocomplete tools – offering predictions based only on what they’ve seen before. This difference matters. It means AI models might miss events that fall outside their training history.

Scientists are beginning to use AI to explore long-term risks and future climate scenarios. But if AI can’t forecast extremes, its usefulness for those tasks becomes limited.

Still, there’s hope. The researchers found that if the model had seen a similar extreme event – even in a different part of the world – it could make better predictions. For instance, if the AI never saw an Atlantic hurricane but had seen strong Pacific hurricanes, it could still forecast powerful Atlantic storms.

“This was a surprising and encouraging finding: it means that the models can forecast an event that was unpresented in one region but occurred once in a while in another region,” Hassanzadeh said.

Combining AI with classic tools

So what’s the solution? The researchers believe that blending traditional physics with AI is the next step.

“The hope is that if AI models can really learn atmospheric dynamics, they will be able to figure out how to forecast gray swans,” Hassanzadeh said.

To make that happen, scientists are exploring new techniques. One of them is called active learning. In this approach, AI models help guide physics-based simulations to create more examples of rare events. These examples can then be used to improve the AI’s accuracy.

Study co-author Jonathan Weare is a professor at the Courant Institute of Mathematical Sciences at New York University.

“Longer simulated or observed datasets aren’t going to work. We need to think about smarter ways to generate data,” said Weare.

“In this case, that means answering the question ‘where should I place my training data to achieve better performance on extremes?’ Fortunately, we think AI weather models themselves, when paired with the right mathematical tools, can help answer this question.”

The future of AI weather forecasting

As AI becomes a bigger part of how we forecast and prepare for extreme weather, knowing its limits is key.

The technology is improving fast. But until it can truly grasp the physics of our atmosphere, it won’t be able to predict everything.

That’s not a reason to give up on AI forecasts – it’s just motivation to make them even better.

The full study was published in the journal Proceedings of the National Academy of Sciences.

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