GraphCast's AI-powered technology uses cutting edge machine-learning algorithms and vast data sets to provide highly accurate and more timely weather predictions that, among other advantages, could help mitigate the impact of severe weather events and, ultimately, save lives.
GraphCast was announced as the winner of this year’s MacRobert Award at the Royal Academy of Engineering’s annual Awards Dinner at The Peninsula Hotel in London, where the Google DeepMind team behind the winning innovation were presented with the MacRobert Award gold medal and a prize of £50,000 by Vice Admiral Sir Tim Laurence KCVO CB, on behalf of the Academy’s Royal Fellow, HRH The Princess Royal. Sir Tim is also Chair of Trustees at the Science Museum Group.
Now in its 55th year, the MacRobert Award celebrates UK engineering skills and innovation that continue the UK’s distinguished engineering heritage. To win the Award, teams must demonstrate outstanding engineering ingenuity, proven commercial success and tangible social benefit.
Instead of the traditional numerical weather prediction methodologies, GraphCast uses machine learning to provide highly accurate and timely weather predictions up to ten days in advance. It harnesses the power of graph neural networks to model complex weather patterns and offers unprecedented precision in forecasts and an enhanced ability to predict extreme weather conditions. The model currently takes just 45 seconds to generate a forecast that would take traditional forecasting methods over an hour on a supercomputer. This has been equated to a five-year leap forward in weather forecasting.
By significantly improving the speed and reliability of weather predictions, GraphCast has the potential to support critical decision-making across various industries, optimise resource allocation and help mitigate impacts of severe weather events, allowing authorities to issue safety and evacuation warnings ahead of time and potentially save lives.
The MacRobert Award winning Google DeepMind team are:
- Ferran Alet, Research Scientist
- Peter Battaglia, Research Scientist
- Meire Fortunato, Staff Research Scientist
- Remi Lam, Staff Research Scientist
- Shakir Mohamed, Director of Research
- Alexander Pritzel, Senior Staff Research Scientist
- Alvaro Sanchez-Gonzalez, Staff Research Engineer
- Jacklynn Stott, Research Program Manager
- Matthew Willson, Staff Research Engineer
- Peter Wirnsberger, Staff Research Scientist
Professor Sir Richard Friend FREng FRS, Chair of the Royal Academy of Engineering MacRobert Award judging panel, said: “The heroes behind the UK’s world-changing engineering innovations deserve to be celebrated, and the GraphCast team has made a revolutionary advance.
“Modern weather forecasting depends on supercomputers to solve the many coupled equations that project forward in time and the only way to improve that methodology has been to use more computing power. But GraphCast, trained on the huge data set of past weather, speeds up what used to take hours to less than a minute and presents a new roadmap for improving both the timeliness and accuracy of weather forecasting.
“GraphCast is a great example of how engineering innovation can be an important driving force behind economic growth and a sustainable future. In an age of climate change and increasingly volatile weather events, accurate forecasting is an essential tool that can benefit society worldwide.”
Remi Lam, Staff Research Scientist at Google DeepMind, added: "We are deeply humbled and incredibly proud to receive the MacRobert Award. Our passion for engineering has always been fuelled by the desire to build AI that helps solve some of the hardest scientific and engineering challenges of our time, and, ultimately, improve billions of people’s lives. This recognition reaffirms the crucial role engineering plays in that mission, and brings a spotlight to the nascent field of data-driven weather forecasting. We are particularly excited that GraphCast will enable a new generation of researchers to build on this technology, and we look forward to the breakthroughs they will achieve. Thank you to the Royal Academy of Engineering for sharing our vision."
