A new artificial intelligence weather forecasting model developed by Google DeepMind has demonstrated superior accuracy compared to conventional physics-based systems in global weather predictions. The model, named GraphCast, uses machine learning to analyze decades of historical weather data and identify complex patterns that traditional models might miss. In head-to-head comparisons with the European Centre for Medium-Range Weather Forecasts’ high-resolution forecast (HRES) system, GraphCast provided more accurate predictions for over 90% of 1,380 test variables, including critical measurements like atmospheric pressure, temperature, and wind speed. The AI system can generate a 10-day forecast in under one minute on a single Google TPU v4 machine, a significant reduction in computational cost compared to traditional supercomputer-run models. Researchers emphasize that AI models like GraphCast are designed to complement, not replace, existing numerical weather prediction systems, offering a faster, more efficient tool for meteorologists. The technology represents a major shift toward data-driven approaches in atmospheric science. Read the full article at https://technologyreview.com/2023/11/14/1084089/ai-weather-forecasting-model-graphcast-outperforms-traditional-systems/