National Hurricane Center (US) 5-day, ECMWF (Europe), and GraphCast models for 8pm ET on July 1, 2024. All times on the map are Eastern Time.
By William B. Davis
When Hurricane Beryl struck the Caribbean in early July, Europe’s main meteorological agency Predicted It outlined the extent of the eventual landfall and warned that Mexico was the most likely location for the storm. The warning was based on global observations from planes, buoys and spacecraft, which were translated into a forecast by a room-sized supercomputer.
That same day, experts running artificial intelligence software on a much smaller computer predicted landfall in Texas, based solely on what the machine had learned so far about the planet’s atmosphere.
Four days later, on July 8, Hurricane Beryl ripped through Texas with ferocity, flooding roads, killing at least 36 people, and leaving millions without power. In Houston, at least two victims were crushed to death when strong winds tore trees onto homes.
NOAA via European Press Agency via Shutterstock
The Texas forecast offers a glimpse into the new world of AI weather forecasting, in which a growing number of smart machines are predicting future global weather patterns with new speed and accuracy. In this case, the experimental program is GraphCast, built in London by DeepMind, a subsidiary of Google, that does in minutes or seconds what once took hours.
“This is a really exciting step,” said Matthew Chantry, an AI expert at the European Centre for Medium-Range Weather Forecasts who gained prominence with the Beryl forecasts, adding that on average, GraphCast and its smart brethren can outperform the centre at predicting hurricane paths.
Christopher S. Bretherton, professor emeritus of meteorology at the University of Washington, said that superfast AI would generally be better at spotting future dangers: Regular warnings about extreme heat, high winds and heavy rains would be “much more up-to-date,” potentially saving countless lives, he said.
Rapid AI weather forecasts could also be useful for scientific discovery, said Amy McGovern, a professor of meteorology and computer science at the University of Oklahoma who directs the AI Weather Institute. Weather detectives currently use AI to create thousands of subtle variations of forecasts to find unexpected factors that contribute to forecasters’ predictions of tornadoes and other extreme weather, she said.
“This allows us to look at fundamental processes,” Dr. McGovern says, “and is a valuable tool for discovering new ones.”
Importantly, AI models can be run on desktop computers, making the technology much easier to deploy than the room-sized supercomputers that currently dominate the world of global forecasting.
Brandon Bell/Getty Images
“This is a turning point,” said Maria Molina, a research meteorologist at the University of Maryland who studies AI programs for predicting extreme weather. “You don’t need a supercomputer to make a forecast. You can do it on a laptop. It makes science more democratized.”
People rely on accurate weather forecasts to make decisions about how to dress, where to travel, and whether to escape a severe storm.
And yet reliable weather forecasting has proven extremely difficult to achieve. The problem is complexity: astronomers can predict the orbits of planets in our solar system for centuries into the future because a single factor controls their motion: the sun and its enormous gravitational pull.
In contrast, weather patterns on Earth result from a variety of factors: the Earth’s tilt, rotation, wobble, and day-night cycles turn the atmosphere into a turbulent swirl of wind, rain, clouds, temperature, and air pressure. To make matters worse, the atmosphere is inherently chaotic: certain zones can rapidly shift from stable to volatile, even without any outside stimuli.
As a result, weather forecasts are wrong by days, sometimes hours. The error grows proportional to the length of the forecast period, which can now be as long as 10 days, up from three days a few decades ago. Slow improvement is due to upgrades to supercomputers that monitor and forecast Earth.
That doesn’t mean the supercomputing work gets any easier: It takes skill and preparation: Modelers build a virtual planet crisscrossed with millions of data gaps, then fill in the gaps with current weather observations.
The University of Washington’s Dr Bretherton called these inputs crucial and somewhat improvisational: “To infer what’s going on in the atmosphere right now, you have to combine data from many sources,” he said.
Complex equations of fluid dynamics transform the blended observations into predictions. Despite the enormous processing power of supercomputers, the numerical calculations can take more than an hour. And of course, as the weather changes, the forecasts need to be updated.
The AI approach is fundamentally different: instead of relying on current measurements and millions of calculations, AI agents leverage what they have learned about the causal relationships that govern Earth’s weather.
More generally, this progress comes from the ongoing revolution in machine learning, the branch of AI that mimics how humans learn. This approach has been hugely successful because AI excels at pattern recognition: it can quickly sort through vast amounts of information and spot complexities that humans can’t. This has led to breakthroughs in speech recognition, drug discovery, computer vision, and cancer detection.
In weather forecasting, AI scans repositories of real-world observational data to learn about atmospheric forces, then identifies subtle patterns and uses that knowledge to predict the weather – and does so with incredible speed and accuracy.
Recently, the DeepMind team behind GraphCast won the UK’s highest engineering award, given by the Royal Academy of Engineering. The judge, Sir Richard Friend, a physicist at the University of Cambridge, praised the team’s “revolutionary advances.”
GraphCast’s chief scientist, Remi Lamm, said in an interview that his team trained its AI program on 40 years of global weather observation data collected by the European Forecasting Centre. “It learns directly from the past,” he said, adding that GraphCast can produce 10-day forecasts in seconds that would take a supercomputer more than an hour to complete.
Dr Lam said GraphCast runs fastest on computers designed for AI, but can also run on desktops and laptops, albeit more slowly.
Dr. Lamb reported that in a series of tests, GraphCast outperformed the European Centre for Medium-Range Weather Forecasts’ best forecasting model by more than 90 percent. “If we can know where the cyclone is going, that’s really important,” he added. “It’s important for saving lives.”
Brandon Bell/Getty Images
In response to questions, Dr Lam said he and his team are computer scientists, not cyclone experts, and so have not assessed how accurate Graphcast’s forecast for Hurricane Beryl is compared to other forecasts.
But DeepMind did conduct a study of Hurricane Lee, an Atlantic hurricane that was seen as potentially threatening New England or even further east into Canada in September, he added. The study found that GraphCast predicted landfall in Nova Scotia three days before a supercomputer reached the same conclusion, Lam said.
Impressed by these results, the European center recently adopted GraphCast, as well as other AI forecasting programs developed by Nvidia, Huawei, and China’s Fudan University. The center’s website now features a global map of AI tests, including the extent to which smart machines predicted the path of Hurricane Beryl on July 4.
DeepMind’s GraphCast projected path (labeled DMGC on the July 4 map) shows Beryl making landfall in the Corpus Christi, Texas area, not far from where the hurricane actually hit.
The European Centre’s Dr Chantry said the centre believes the experimental technique will become a standard for global weather forecasting, including for cyclones, adding that a new team is now building on the experimenters’ “fantastic work” to develop an operational AI system for the agency.
Dr Chantry said the introduction of AI technology could be in the near future, but added that it may co-exist as a regular tool with the centre’s traditional forecasting systems.
Bretherton, who now serves as team leader at the Allen Institute for AI (founded by Microsoft co-founder Paul G. Allen), said the European institute is considered the best weather agency in the world because comparative tests regularly show its forecasts are more accurate than anyone else’s. As a result, he added, the institute’s interest in AI has led to the world of meteorologists “looking at this and saying, ‘Hey, we need to match that.'”
Weather experts say AI systems are likely to complement supercomputer approaches, as each method has its own strengths.
“All models are wrong to some extent,” says Dr Molina of the University of Maryland, adding that AI machines “may be able to correctly predict the path of a hurricane, but what about the rainfall, the maximum wind speed, the storm surge? There are hugely diverse influences” that need to be predicted reliably and carefully evaluated.
Still, Dr. Molina noted, AI scientists are rushing to publish papers demonstrating new predictive techniques. “The revolution continues,” she said. “It’s amazing.”
Jamie Rome, deputy director of the National Hurricane Center in Miami, agreed that multiple tools are needed, calling AI “evolutionary rather than revolutionary” and predicting that humans and supercomputers will continue to play large roles.
“Having a human at the table to apply situational awareness is one of the reasons we achieve such high accuracy,” he said.
Rome added that the hurricane center has been using elements of artificial intelligence in its forecasts for more than a decade, and that the center will evaluate and potentially use clever new programs.
“As AI becomes more widespread, many people think the role of humans is diminishing,” Rome added, “but our forecasters are making big contributions. The role of humans is still very large.”
Sources and Notes
US National Hurricane Center (NHC) and European Centre for Medium-Range Weather Forecasts (ECMWF) | Note: Beryl’s “actual track” uses NHC best-case provisional track data.