In a paper published in Nature, DeepMind researchers said they found that GenCast outperforms the European Centre for Medium-Range Weather Forecasts’ ENS—apparently the world’s top operational forecasting system.
Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere.
Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk.
Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts4.
GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production.
This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.
And in a blog post, the DeepMind team offered a more accessible explanation of the tech: While its previous weather model was “deterministic and provided a single, best estimate of future weather,” GenCast “comprises an ensemble of 50 or more predictions, each representing a possible weather trajectory,” creating a “complex probability distribution of future weather scenarios.”
As for how it stacks up against ENS, the team said it trained GenCast on weather data up to 2018, then compared its forecasts for 2019, finding that GenCast was more accurate 97.2 percent of the time.
Google says GenCast is part of its suite of AI-based weather models, which it’s starting to incorporate into Google Search and Maps. It also plans to release real-time and historical forecasts from GenCast, which anyone can use in their own research and models.
