Google said Wednesday that its own artificial intelligence (AI) agent outperformed the world’s best weather predictions.
In a blog post, Ilan Price and Matthew Willson, researchers with Google’s DeepMind, said its recently-made “AI ensemble model” named GenCast “provides better forecasts of both day-to-day weather and extreme events than the top operational system, the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ENS, up to 15 days in advance.”
Wilson and Price said in their post that they taught GenCast “on historical weather data up to 2018” when trying to analyze the skills of the model “and tested it on data from 2019.”
“GenCast showed better forecasting skill than ECMWF’s ENS, the top operational ensemble forecasting system that many national and local decisions depend upon every day,” the researchers said.
The researchers said they assessed the capabilities of ECMWF’s ENS and GenCast through examining “forecasts of different variables at different lead times — 1320 combinations in total.”
According to the American Meteorological Society, a forecast lead time is “the length of time between the issuance of a forecast and the occurrence of the phenomena that were predicted.”
Among the variables tested were wind speed and temperature, the blog post read.
According to the DeepMind researchers, their system beat out ENS on accuracy 97.2 percent of the time when it came to the forecasts of different variables at different lead times.
When lead times were over 36 hours, they said, GenCast beat out ENS for accuracy on forecasts of different variables at the different lead times 99.8 percent of the time.
Wilson and Price said that despite the success of GenCast, “traditional models remain essential for” forecasting because “they supply the training data and initial weather conditions required by models such as GenCast.”
“This cooperation between AI and traditional meteorology highlights the power of a combined approach to improve forecasts and better serve society,” the researchers said.
The Hill has reached out to ECMWF for further comment.
