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AI Is Learning to Predict the Weather

Microsoft, Google and others are training artificial intelligence to make forecasts thousands of times faster. Will it change the way we prepare for storms?

By Eric Niiler. The Wall Street Journal. Aug. 2, 2024.

Like a digital toddler taking its first steps, artificial intelligence is learning to predict the weather. In time, AI-based programs could calculate forecasts faster and at a lower cost than existing methods, scientists say.

Using AI to predict weather has evolved over the past five years from an academic notion to operational tests at weather agencies in the U.S. and Europe, as well as at companies that provide intelligence to businesses.

In May, Microsoft released a forecast tool called Aurora that produces five-day global air- pollution predictions and 10-day weather forecasts 5,000 times faster than existing models run by the National Oceanic and Atmospheric Administration and the European Center for Medium-Range Weather Forecasts. Companies and labs around the country, including Villanova University, the University of Oklahoma and a California startup firm, are training new weather AIs.

Faster, more accurate forecasts are becoming ever more important. The world is warming, extreme weather has become more deadly, and storms are more costly. “We really need to get the forecasts of the weather really accurate,” says Remi Lam, a research scientist at Google DeepMind, which introduced an AI-based weather model called GraphCast in November.

Beyond equations

For decades, meteorologists have derived weather forecasts using equations that describe the atmosphere, such as the relationship between air pressure and prevailing wind from one region to another, or how quickly temperatures change as cold fronts move through. They populate these equations with measurements of the atmosphere and ocean taken hourly by weather stations, high-altitude balloons, ocean buoys and satellites. The data is fed into supercomputers that produce what is known as numerical weather prediction.

The problem is that small errors in measuring the weather or in the calculations can lead to bigger forecast errors. What’s more, running complicated simulations of the Earth’s weather takes a lot of expensive computing time.

AI algorithms look for patterns in weather data, rather than solving equations as supercomputers do. The pattern-hunting algorithms are trained on decades of weather data to predict what will happen in the days ahead.

“All those AI tools do is recognize patterns,” says Paris Perdikaris, a principal researcher on the Aurora project at Microsoft Research. “And they’re really good at doing that.”

Researchers trained Aurora with a huge amount of historical weather data so it could make those predictions, about 16 times more data than the amount used to train the latest version of the AI-powered chatbot ChatGPT, according to Perdikaris.

Microsoft expects to make Aurora publicly available in coming months to allow more people, including researchers at weather-forecasting agencies, to give it a test drive.

“It is ultimately up to them to decide whether and when they will adopt AI models like Aurora into their operational forecasting workflow,” Perdikaris says. “My personal estimation is that this will happen within the next two to five years.”

Training on history

WindBorne Systems of Palo Alto, Calif., has developed its own AI forecasting model that uses data from its constellation of weather balloons. The balloons, launched from three continents, traverse oceans and circumnavigate the globe. The data is analyzed using similar AI techniques that power ChatGPT.

“You have a large neural network that you train on historical data, you input the current state of weather, it outputs a predictive future state, and nowhere inside do you ever program the laws of physics,” says John Dean, co-founder and CEO of WindBorne.

NOAA has been testing WindBorne’s data, and a recent study found that a small amount of