[Accurately predicting the weather is hard – really, really hard. But a new AI-powered forecast model has hit a milestone that has experts saying your forecast could soon get more accurate, and further out, too.
A five-day forecast in the early 1980s was only accurate about 65% of the time. But better weather observations, more robust computing power and innovations in the way weather is modeled by computers has improved forecasts by leaps and bounds. Today, the same forecast hits the mark nine times out of 10.
Forecasts took another step forward this month, experts said, thanks to GenCast, a new artificial intelligence forecast model by Google’s DeepMind. Its forecasts through 15 days were significantly more accurate than one of the most well-respected traditional non-AI forecast models, according to a study published by DeepMind in the journal Nature.
“It’s an impressive result,” said Peter Dueben, a machine learning expert and head of Earth system modeling at the European Centre for Medium-Range Weather Forecasts, home to the model bested by GenCast. “It’s a big step.”
GenCast isn’t ready for the public yet. It and other AI models still have a few key kinks to work out, particularly in forecasting the more frequent and severe weather of a warming world, before they change forecasting and save lives in the process.
The skill and usefulness of weather forecast models has always been closely tied to technology. The majority of weather forecast models used today are based on a complex series of mathematical equations that model the physics of the atmosphere and use hundreds of millions of datapoints from real-time weather observations to paint a picture of how the weather will play out a day, a week or even a season from now.
GenCast takes a different approach. Rather than relying on observations plugged into physics-based equations, it predicts how Earth’s atmosphere might behave in the future by analyzing verified past weather data to understand how the atmosphere behaved in similar situations. This helps improve accuracy over traditional models by eliminating errors from real-time weather data.
AI forecast models also run simulations a lot faster and use less computing power and energy than traditional models once they’re trained and ready to go. This means they can be run more frequently and model a wider range of possibilities, improving forecasts as they do.
GenCast is not the first AI model, but it is the first to run dozens of simulations simultaneously, giving it an edge over other models. “Once you have multiple possible futures it gives you a sense of both the range of what might happen and it also lets you calculate how likely some (futures) are rather than others,” according to Ilan Price, the lead author of the new study and a senior research scientist with DeepMind.
The European Centre for Medium-Range Weather Forecasts’ model is widely considered the gold standard. It was what Google wanted to beat with its first-of-its-kind AI version and it did. Researchers trained GenCast on 40 years of weather data up to 2018 and then used the trained model to predict more than 1,300 combinations of conditions like temperatures, precipitation and wind speeds, in 2019’s weather.
The AI model produced more accurate forecasts than the ECMWF’s traditional model for more than 97% of these variables within a 15-day timeframe, but showed particular skill within the first week of forecasts. It showed anywhere from a 10 to 30% accuracy improvement on forecasts in the three-to-five-day range, depending on the exact combination of variables tested, according to Price. GenCast also had more accurate forecasts than the ECMWF’s model up to 15 days in the future, the study said.
The results mark an “inflection point” in AI weather modeling technology, Price said. “AI-based weather forecasting is ready for prime time,” Price added. “It’s ready to start being incorporated alongside traditional models in operation.”
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