AI outperforms conventional weather forecasting for the first time: Google study

Enlarge / A file photo of Tropical Storm Fiona as seen in a satellite image from 2022. (credit: Getty Images)

On Tuesday, the peer-reviewed journal Science published a study that shows how an AI meteorology model from Google DeepMind called GraphCast has significantly outperformed conventional weather forecasting methods in predicting global weather conditions up to 10 days in advance. The achievement suggests that future weather forecasting may become far more accurate, reports The Washington Post and Financial Times.

In the study, GraphCast demonstrated superior performance over the world’s leading conventional system, operated by the European Centre for Medium-range Weather Forecasts (ECMWF). In a comprehensive evaluation, GraphCast outperformed ECMWF’s system in 90 percent of 1,380 metrics, including temperature, pressure, wind speed and direction, and humidity at various atmospheric levels.

And GraphCast does all this quickly: “It predicts hundreds of weather variables, over 10 days at 0.25° resolution globally, in under one minute,” write the authors in the paper “Learning skillful medium-range global weather forecasting.”

Read 6 remaining paragraphs | Comments

Author: Benj Edwards. [Source Link (*), Ars Technica – All content]