A new artificial intelligence-based weather forecasting model developed by researchers at Stanford University has demonstrated superior accuracy in global weather predictions compared to conventional physics-based models. The AI system, named ClimaNet, was trained on decades of historical global weather data and can generate high-resolution 10-day forecasts in under two minutes on standard computing hardware, a task that typically requires hours on supercomputers using traditional methods. In benchmark tests against the European Centre for Medium-Range Weather Forecasts (ECMWF) system, ClimaNet showed a 15% improvement in predicting key atmospheric variables like geopotential height and temperature at pressure levels. The researchers emphasize that the model is designed to complement, not replace, existing forecasting infrastructure, offering a faster, more efficient tool for initial analysis. While promising, the team notes that further testing is needed for extreme weather events and long-term climate projections. Read the full article at: https://technologyreview.com/2024/05/15/ai-weather-model-outperforms-traditional-systems