Google’s DeepMind team has unveiled an AI model for weather prediction called GenCast which has outperformed the top weather forecast system in the world.
In a paper published in the journal Nature, researchers at DeepMind said GenCast outperforms the European Centre for Medium-Range Weather Forecasts’ (ECMWF), which is the world’s top operational forecasting system.
“New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead,” said Google in a statement.
According to the tech giant, GenCast marks a critical advance in AI-based weather prediction that builds on its previous weather model, which was deterministic, and provided a single, best estimate of future weather.
By contrast, a GenCast forecast comprises an ensemble of 50 or more predictions, each representing a possible weather trajectory.
GenCast is a diffusion model, the type of generative AI model that underpins the recent, rapid advances in image, video and music generation.
“However, GenCast differs from these, in that it’s adapted to the spherical geometry of the Earth, and learns to accurately generate the complex probability distribution of future weather scenarios when given the most recent state of the weather as input,” said Google.
More accurate forecasts of risks of extreme weather can help officials safeguard more lives, avert damage, and save money.
“Consider tropical cyclones, also known as hurricanes and typhoons. Getting better and more advanced warnings of where they’ll strike land is invaluable. GenCast delivers superior predictions of the tracks of these deadly storms,” said Google.
The company will soon be releasing real-time and historical forecasts from GenCast, and previous models, which will enable anyone to integrate these weather inputs into their own models and research workflows.
GenCast is part of Google’s growing suite of next-generation AI-based weather models, including Google DeepMind’s AI-based deterministic medium-range forecasts, and Google Research’s NeuralGCM, SEEDS, and floods models.
(With inputs from IANS)
