Artificial intelligence (AI) has achieved a groundbreaking milestone in weather forecasting, surpassing traditional methods and revolutionizing the field. In a peer-reviewed study published in the journal Science, researchers at Google DeepMind presented the GraphCast AI model, which demonstrated superior accuracy in predicting weather conditions up to 10 days ahead compared to the European Centre for Medium-range Weather Forecasts (ECMWF) – the world’s leading conventional system.
The evaluation of GraphCast’s performance revealed its superiority over the ECMWF system in 90% of the 1380 metrics used, including temperature, pressure, wind speed, direction, and humidity at various atmospheric levels. The machine-learning co-ordinator at ECMWF, Matthew Chantry, acknowledged the impressive progress made by AI systems in meteorology, even exceeding expectations from just two years ago.
To develop GraphCast, a machine-learning architecture called graph neural network was employed, which assimilated more than 40 years of past ECMWF data to understand global weather system development and movement. The input for its forecasts consists of atmospheric conditions worldwide at the current time and six hours earlier, gathered by ECMWF from global weather observations. Remarkably, GraphCast generates a 10-day forecast within a minute using a single Google TPU v4 cloud computer.
In contrast to the data-derived approach of GraphCast, conventional weather prediction methods rely on numerical weather prediction models that use supercomputers to calculate complex equations based on scientific knowledge of atmospheric physics. These computations require significant energy consumption and take several hours to complete. The advent of GraphCast brings remarkable cost efficiencies, requiring only a fraction of the energy and time needed by traditional methods.
The efficacy of GraphCast was underlined by its accurate prediction of Hurricane Lee’s path in the North Atlantic, enabling Nova Scotia residents to prepare three days earlier than with traditional forecasting methods. However, in the case of Hurricane Otis off Mexico’s Pacific coast, AI performed no better than conventional models, failing to predict its sudden explosive intensification that heavily impacted Acapulco.
Moving forward, ECMWF plans to develop its own AI model and explore merging it with the numerical weather prediction system. This integration would enable the injection of physics-based understanding into the machine-learning systems, helping to overcome the perceived black box nature of AI-based forecasting.
Recognition of the importance of AI in weather forecasting extends beyond DeepMind’s GraphCast. The UK Met Office, the national weather service, recently announced a collaboration with the Alan Turing Institute to implement its graph neural network into their existing supercomputer infrastructure. Simon Vosper, the Met Office’s science director, emphasized the need to account for climate change in forecasting and the blending of AI and traditional models to provide robust and precise weather predictions in an era of profound change.
While AI has demonstrated remarkable advancements in weather forecasting, concerns surround its ability to accurately predict new extremes when trained solely on historical weather conditions. However, the combination of AI and physics-based models presents an opportunity for enhanced forecasts that can adapt to changing climatic patterns.
As AI continues to reshape the field of weather forecasting, GraphCast’s breakthrough achievement highlights its potential in providing more accurate predictions, longer lead times, and increased preparedness for severe weather events. The incorporation of AI into existing systems showcases a harmonious blend of technology and scientific expertise, paving the way for improved weather forecasting capabilities in an era of evolving climate dynamics.
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