Google's new weather forecasting system combines AI with traditional physics

New machine learning techniques that predict the weather by learning from years of historical data are extremely fast and efficient, but can struggle with long-term forecasts. Meanwhile, the general circulation models that have dominated weather forecasting for the past 50 years use complex equations to model atmospheric changes and produce accurate predictions, but they are very slow and expensive to run. Experts are divided on which tool will be the most reliable going forward. But Google's new model is trying to combine the two.

“This isn't really physics versus AI. It's really a combination of physics and AI,” said Stephan Heuer, an AI researcher at Google Research and co-author of the paper.

The system still uses traditional models to calculate some of the large-scale atmospheric changes needed to make predictions. Then it injects AI, which tends to work well in areas where larger models don't work well, such as forecasting at scales smaller than about 25 kilometers, dealing with cloud formations and local microclimates (like fog in San Francisco). So we inject AI very selectively to correct errors that accumulate on a smaller scale, Heuer says.

The result is a model that can generate higher-quality forecasts faster and with less computing power, the researchers say, and NeuralGCM's accuracy is comparable to the 1- to 15-day forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), a partner organization on the study.

But the real potential of such technology isn't in improving local weather forecasts, says Aaron Hill, an assistant professor in the University of Oklahoma's meteorology department who was not involved in the study. Rather, it's in larger-scale weather phenomena that are prohibitively expensive to model with traditional techniques. These possibilities range from predicting tropical storms earlier to modeling more complex climate changes years into the future.

Simulating the entire planet over and over, or for long periods of time, requires massive amounts of computation, Hill says, meaning that even the best climate models are hampered by the high cost of computing power, creating a major bottleneck for research.

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