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Tame meteorologists may be an oil-patch advertising luxury in the age of contradictory You Tube weather reports and interactive global weather animations like EARTH, but fossil fuel lobbysts can't stop the National Oceanographic and Atmospheric Administration from reporting in Science that AI weathermen can out-forecast TV weatherheads and finite element models at far lower cost. So low in fact that everyone's go-to weather guy may soon be their iPhone or laptop AI: |
Running in mere minutes, AI forecasts are surpassing supercomputers in speed and accuracy
BY SARAH CRESPI & PAUL VOOSEN
Meteorologists call it the “quiet revolution”: a gradual but steady improvement in weather forecasting. Today, the 6-day forecast is about as good as the 3-day forecast from 30 years ago. but it also comes with a cost: billions of dollars’ worth of energy-hungry supercomputers that must run 24/7 just to produce a few forecasts a day.
Artificial intelligence (AI) is now spurring another revolution within numerical weather prediction, as the field is known.
In mere minutes on cheap desktop computers, trained AI systems can now make 10-day forecasts that are as good as the best traditional models—and in some cases even better.
The world’s top weather agency, the European Centre for Medium-Range Weather Forecasts (ECMWF), has embraced the technology
“It’s very, very exciting to know we can generate global predictions that are skillful, really cheaply,” says Maria Molina, an AI-focused research meteorologist at the University of Maryland.
Some of the world’s biggest tech giants are jockeying to claim the most skillful model, including Google DeepMind, which describes its GraphCast model in Science this week...
The new AI models skip the expense of solving equations in favor of “deep learning.” They identify patterns in the way the atmosphere naturally evolves, after training on 40 years of ECMWF “reanalysis” data—a combination of observations and short-term model forecasts that represents modelers’ best and most complete picture of past weather.
When fed a starting snapshot of the atmosphere based on the same combination of observations and modeling, GraphCast can outperform the ECMWF forecast out to 10 days on 90% of its verification targets, including hurricane tracks and extreme temperatures. Although it took 32 computers 4 weeks to train the AI model, the resulting algorithm is lightweight enough to work in less than 1 minute on a single desktop computer, says Rémi Lam, lead author of the GraphCast paper. “It is fast, accurate, and useful.”
To improve further, the AI models could be weaned off the reanalysis data, which carry the biases of traditional models. Instead, they could learn directly from the petabytes of raw observation data held by weather agencies, Keisler says. Google’s short-term weather model already does so, training itself on data from weather stations, radar, and satellites.
The potential for these models doesn’t stop at weather prediction, says Christopher Bretherton, an atmospheric scientist at the Allen Institute for AI. They cannot project climate on their own, because the 40-year training data sets are not long enough to capture global warming trends, which are subject to complex feedbacks from clouds, gases, and aerosols that can accelerate or slow climate change.
But they could assist a new generation of high-resolution climate models being developed to run on exascale computers, the latest ultrafast machines. Once those models produce enough output for the AIs to be trained on, the AIs could take over. “We can make emulators of these models and then run them 100 times faster,” Bretherton says...
Adoption might be slowed by unease about the black-box nature of the AI: Researchers often can’t say how such systems reach their conclusions. But that concern can be overstated, says Chantry, who notes that traditional models are also so complicated that “there’s a degree of opaqueness already built into them.”
Ultimately, it will come down to users, Grover says. “If you’re a farmer in the field, would you care about the more accurate forecast, or the one you can write down with physical equations?”
doi: 10.1126/science.adm9275
