May 22, 2025 08:12 PM
https://www.eurekalert.org/news-releases/1084850
INTRO: Increasingly powerful AI models can make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does something that’s unprecedented in recorded history?
A new study led by scientists from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, is testing the limits of AI-powered weather prediction. In research published May 21 in Proceedings of the National Academy of Sciences, they found that neural networks cannot forecast weather events beyond the scope of existing training data—which might leave out events like 200-year floods, unprecedented heat waves or massive hurricanes.
This limitation is particularly important as researchers incorporate neural networks into operational weather forecasting, early warning systems, and long-term risk assesments, the authors said. But they also said there are ways to address the problem by integrating more math and physics into the AI tools.
“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 Pedram Hassanzadeh, an associate professor of geophysical sciences at UChicago and a corresponding author on the study. “We’ve only had these models for a few years, so there’s a lot of room for innovation.”
Gray swan events. Weather forecasting AIs work in a similar way to other neural networks that many people now interact with, such as ChatGPT.
Essentially, the model is “trained” by feeding it a bunch of text or images into a model and asking it to look for patterns. Then, when a user presents the model with a question, it looks back at what it’s previously seen and uses that to predict an answer.
In the case of weather forecasts, scientists train neural networks by feeding them decades’ worth of weather data. Then a user can input data about the current weather conditions and ask the model to predict the weather for the next several days.
The AI models are very good at this. Generally, they can achieve the same accuracy as a top-of-the-line, supercomputer-based weather model that uses 10,000 to 100,000 times more time and energy, Hassanzadeh said.
“These models do really, really well for day-to-day weather,” he said. “But what if next week there’s a freak weather event?”
The concern is.. (MORE - details, no ads)
https://youtu.be/CVeKlogMBl8
https://www.youtube-nocookie.com/embed/CVeKlogMBl8
INTRO: Increasingly powerful AI models can make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does something that’s unprecedented in recorded history?
A new study led by scientists from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, is testing the limits of AI-powered weather prediction. In research published May 21 in Proceedings of the National Academy of Sciences, they found that neural networks cannot forecast weather events beyond the scope of existing training data—which might leave out events like 200-year floods, unprecedented heat waves or massive hurricanes.
This limitation is particularly important as researchers incorporate neural networks into operational weather forecasting, early warning systems, and long-term risk assesments, the authors said. But they also said there are ways to address the problem by integrating more math and physics into the AI tools.
“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 Pedram Hassanzadeh, an associate professor of geophysical sciences at UChicago and a corresponding author on the study. “We’ve only had these models for a few years, so there’s a lot of room for innovation.”
Gray swan events. Weather forecasting AIs work in a similar way to other neural networks that many people now interact with, such as ChatGPT.
Essentially, the model is “trained” by feeding it a bunch of text or images into a model and asking it to look for patterns. Then, when a user presents the model with a question, it looks back at what it’s previously seen and uses that to predict an answer.
In the case of weather forecasts, scientists train neural networks by feeding them decades’ worth of weather data. Then a user can input data about the current weather conditions and ask the model to predict the weather for the next several days.
The AI models are very good at this. Generally, they can achieve the same accuracy as a top-of-the-line, supercomputer-based weather model that uses 10,000 to 100,000 times more time and energy, Hassanzadeh said.
“These models do really, really well for day-to-day weather,” he said. “But what if next week there’s a freak weather event?”
The concern is.. (MORE - details, no ads)
https://youtu.be/CVeKlogMBl8