Dubbed Aurora, the new system -- which has not been commercialised -- generated 10-day weather forecasts and predicted hurricane trajectories more accurately and faster than traditional forecasting, and at lower costs, researchers reported in the journal Nature.
"For the first time, an AI system can outperform all operational centres for hurricane forecasting," said senior author Paris Perdikaris, an associate professor of mechanical engineering at the University of Pennsylvania.
Trained only on historical data, Aurora was able to correctly forecast all hurricanes in 2023 more accurately than operational forecasting centres, such as the US National Hurricane Center.
Traditional weather predicting models are designed on first physical principles -- such as conservation of mass, momentum and energy -- and require massive computer power.
The computational costs of Aurora were several hundred times lower, the study said.
The experimental results follow on the heels of the Pangu-Weather AI model developed and unveiled by Chinese tech giant Huawei in 2023, and could herald a paradigm shift in how the world's major weather agencies forecast weather and potentially deadly extreme events exacerbated by global warming.
- 'Holy grail' -
"I believe that we're at the beginning of a transformation age in air system science," Perdikaris said in a video presentation distributed by Nature.
"In the next five to 10 years the holy grail is how to build systems that can directly work with observations from remote sensing sources like satellites and weather stations to generate forecasts at high resolution anywhere we like."
According to its designers, Aurora is the first AI model to consistently outperform seven forecasting centres in predicting the five-day trajectory of devastating cyclones.
In its simulation, for example, Aurora correctly forecast four days in advance where and when Doksuri -- the most costly typhoon ever recorded in the Pacific -- would hit the Philippines.
Official forecasts at the time, in 2023, had it heading north of Taiwan.
Microsoft's AI model also outperformed the European Centre for Medium-Range Weather Forecasts (ECMWF) model in 92 percent of cases for 10-day global forecasts, on a scale of approximately 10 square kilometres (3.86 square miles).
The ECMWF, which provides forecasts for 35 European countries, is considered the global benchmark for weather accuracy.
In December, Google announced that its GenCast model had surpassed the European centre's accuracy in more than 97 percent of the 1,320 climate disasters recorded in 2019.
These promising performances -- all experimental and based on observed events -- are being closely scrutinised by weather agencies.
Several, including Meteo-France, are developing their own AI learning models alongside traditional digital models.
"This is something we have taken very seriously," Florence Rabier, Director General of the ECMWF, told AFP.
Their first "learning model", made available to member states in February, is "about 1,000 times less expensive in terms of computing time than the traditional physical model", she added.
While operating as a lower resolution (30 sq km) than Aurora, the ECMWF model is already operational.
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