A new artificial intelligence weather forecasting model developed by Google DeepMind has demonstrated superior performance compared to the world’s leading traditional system in a comprehensive global evaluation. The model, named GraphCast, uses machine learning to analyze decades of historical weather data and current atmospheric conditions to predict future states. In a head-to-head comparison with the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Forecast (HRES) system, 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 generates a 10-day forecast in under a minute on a single Google TPU v4 machine, a process that takes conventional numerical weather prediction models hours to compute on vast supercomputers. Researchers emphasize that the AI model is designed to complement, not replace, existing physics-based systems, offering a faster, less computationally expensive tool for meteorologists. The findings, published in the journal Science, highlight the rapidly advancing role of machine learning in environmental prediction. Read the full article at: https://sciencedaily.com/releases/2023/11/231114155020.htm