The latest weather forecasting AI model from Google DeepMind can beat the leading providers more than 97 per cent of the time, and it is quicker and cheaper to run
By Matthew Sparkes
4 December 2024
Today’s weather forecasts rely on simulations that require a lot of computing power
Petrovich9/Getty Images/iStockphoto
Google DeepMind claims its latest weather forecasting AI can make predictions faster and more accurately than existing physics-based simulations.
GenCast is the latest in DeepMind’s ongoing research project to use artificial intelligence to improve weather forecasting. The model was trained on four decades of historical data from the European Centre for Medium-Range Weather Forecasts’s (ECMWF) ERA5 archive, which includes regular measurements of temperature, wind speed and pressure at various altitudes around the globe.
Read more
Meta AI tackles maths problems that stumped humans for over a century
Advertisement
Data up to 2018 was used to train the model and then data from 2019 was used to test its predictions against known weather. The company found that it beat ECMWF’s industry-standard ENS forecast 97.4 per cent of the time in total, and 99.8 per cent of the time when looking ahead more than 36 hours.
Last year, DeepMind worked with ECMWF to create an AI that beat the “gold-standard” high-resolution HRES 10-day forecast more than 90 per cent of the time. Prior to that, it had developed “nowcasting” models that predicted the chance of rain in a given 1-square-kilometre area from 5 to 90 minutes ahead using 5 minutes of radar data. And Google is also working on ways of using AI to replace small parts of deterministic models to speed up computation while retaining accuracy.
Existing weather forecasts are based on physics simulations run on powerful supercomputers that deterministically model and extrapolate weather patterns as accurately as possible. Forecasters usually run dozens of simulations with slightly different inputs in groups called ensembles to better capture a range of possible outcomes. These increasingly complex and numerous simulations are extremely computationally intensive and require ever more powerful and energy-hungry machines to operate.