Artificial intelligence is powering weather forecasts that are generally more accurate than conventional forecasts and are faster and cheaper to produce. But new research shows A.I. may fail to predict unprecedented weather events, a troubling finding as warming fuels new extremes.
Weather prediction relies on neural networks, a form of A.I. that can learn to make predictions by identifying patterns in vast amounts of data. The problem, the study finds, is that neural networks may not be able to predict events that lie outside their training data. For A.I. weather models, that means failing to forecast droughts, storms, and heat waves that have little or no precedent in the weather record.
For the new study, scientists trained an A.I. model on decades of weather data, but omitted any hurricane stronger than Category 2. When the A.I. was given the conditions that would lead to a Category 5 hurricane and asked to make a forecast, it consistently came up short.
“It always underestimated the event,” said lead author Yongqiang Sun, of the University of Chicago. “The model knows something is coming, but it always predicts it’ll only be a Category 2 hurricane.” The findings were published in the Proceedings of the National Academy of Sciences.
Recent research shows how A.I. can outperform conventional weather models, producing more detailed forecasts that look further into the future, but concerns about extreme weather persist. To address this issue, researchers plan to use conventional models to generate examples of extreme events for the purposes of training A.I.
A.I. weather models are “remarkable, but not magical,” said study coauthor Pedram Hassanzadeh, of the University of Chicago. “We’ve only had these models for a few years, so there’s a lot of room for innovation.”
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