A new artificial intelligence weather forecasting model developed by Google DeepMind has demonstrated superior accuracy to traditional physics-based systems in global medium-range forecasts. The model, named GraphCast, uses machine learning to analyze decades of historical weather data and identify complex atmospheric patterns. In head-to-head comparisons with the European Centre for Medium-Range Weather Forecasts’ high-resolution forecasting system (HRES), GraphCast provided more accurate predictions for over 90% of 1,380 test variables, including critical metrics like temperature, pressure, wind speed, and humidity at multiple atmospheric levels. The AI system can generate a 10-day forecast in under one minute on a single Google TPU, significantly faster than conventional methods which require hours of computation on supercomputers. Researchers note that while AI models like GraphCast excel at pattern recognition from vast datasets, they are not a complete replacement for traditional models, which are better at incorporating real-time observational data. The development points toward a future where AI and physics-based models work in tandem to improve forecast precision and efficiency. Read the full article at: https://technologyreview.com/2023/11/14/1084089/ai-weather-model-graphcast-outperforms-traditional-forecasts/