Hurricane Lee wasn’t bothering anyone in early September, churning far out at sea somewhere between Africa and North America. A wall of high pressure stood in its westward path, poised to deflect the storm away from Florida and in a grand arc northeast. Heading where, exactly? It was 10 days out from the earliest possible landfall—eons in weather forecasting—but meteorologists at the European Centre for Medium-Range Weather Forecasts, or ECMWF, were watching closely. The tiniest uncertainties could make the difference between a rainy day in Scotland or serious trouble for the US Northeast.

Typically, weather forecasters would rely on models of atmospheric physics to make that call. This time, they had another tool: a new generation of AI-based weather models developed by chipmaker Nvidia, Chinese tech giant Huawei, and Google’s AI unit DeepMind. For Lee, the three tech-company models predicted a path that would strike somewhere between Rhode Island and Nova Scotia—forecasts that generally agreed with the official physics-based outlook. Land-ho, somewhere. The devil, of course, was in the details.

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.

Looking ahead

Conventional weather models are made up of equations describing the complex dynamics of Earth’s atmosphere. Feed in real-time observations of factors like temperature, wind, and humidity and you receive back predictions of what will happen next. Over the decades, they have gotten more accurate as scientists improve their understanding of atmospheric physics and the data they gather grows more voluminous.

Fundamentally, meteorologists are trying to tame the physics of chaos. In the 1960s, meteorologist and mathematician Edward Lorenz laid the foundations of chaos theory by noticing that small uncertainties in weather data could result in wildly different forecasts—like the proverbial butterfly whose wing flap causes a tornado. He estimated that the state of the atmosphere can be predicted at most by two weeks ahead. Anyone who has watched the approach of a distant hurricane or studied the weekly outlook ahead of an outdoor wedding knows that forecasting still falls far short of that theoretical limit.