Ars Technica, in not so many words, is saying that AIs could be coming for the meteorologists next. But for now, they’re helping the pros predict hurricanes accurately with greater speed:
Weather forecasters describe the arrival of AI models with language that seems out of place in their forward-looking profession: “Sudden.” “Unexpected.” “It seemed to just come out of nowhere,” says Mark DeMaria, an atmospheric scientist at Colorado State University who recently retired from leading a division of the US National Hurricane Center. When he started a project this year with the US National Oceanographic and Atmospheric Administration to validate Nvidia’s FourCastNet model against real-time storm data, he was a “skeptic” of the new models, he says. “I thought there was no chance that it could work.”
DeMaria has since changed his stance. In the end, Hurricane Lee struck land on the edge of the range of the AI predictions, reaching Nova Scotia on September 16. Even in an active storm season—just over halfway through, there have been 16 named Atlantic storms—it’s too early to make any final judgments. But so far the performance of AI models has been comparable to conventional models, sometimes better on tropical storm tracking. And the AI models do it fast, spitting out predictions on laptops within minutes, while traditional forecasts take hours of supercomputing time.
The new weather models don’t have any physics built in. They work in a way similar to the text-generation technology at the heart of ChatGPT. In that case, the machine-learning algorithms are not told rules of grammar or syntax, but they become able to mimic them after digesting enough data to learn patterns of usage. Similarly, the new weather forecasting models learn the patterns from decades of physical atmospheric data collected in an ECMWF data set called ERA5.
This did not look guaranteed to work, says Matthew Chantry, machine-learning coordinator at the ECWMF, who is spending this storm season evaluating their performance. The algorithms underpinning ChatGPT were trained with trillions of words, largely scraped from the Internet, but there’s no sample so comprehensive for Earth’s atmosphere. Hurricanes in particular make up a tiny fraction of the available training data. That the predicted storm tracks for Lee and others have been so good means that the algorithms picked up some fundamentals of atmospheric physics.
That process comes with drawbacks. Because machine-learning algorithms latch onto the most common patterns, they tend to downplay the intensity of outliers like extreme heat waves or tropical storms, Chantry says.