GraphCast: Learning skillful medium-range global weather forecasting

CMCC Lectures 22 February 2024, 15:00 CET Watch the video

Speaker Remi Lam, Google DeepMind

Introduction by Giulio Boccaletti, CMCC – Scientific Director

Abstract Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased computer resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems.

WATCH THE VIDEO

HOW TO PARTICIPATE 22 February 2024, 15:00 CET To join the webinar, register here

The event is part of the CMCC Lectures webinar series, which presents frontier topics and solutions in climate sciences and action, through the insights of leading experts. The series provides a platform for distinguished scientists to showcase their cutting-edge research and engage in dialogue with peers and stakeholders.