A new artificial intelligence-based weather forecasting model developed by researchers at Stanford University has demonstrated superior accuracy compared to conventional physics-based models in global weather prediction tests. The system, named ClimaNet, uses a deep learning architecture trained on decades of historical atmospheric data to predict temperature, precipitation, and extreme weather events up to 10 days in advance. In head-to-head comparisons over a six-month period, ClimaNet reduced average temperature prediction errors by 15% and improved rainfall forecasts by 22% in tropical regions. The model operates significantly faster than traditional supercomputer-based systems, generating a 10-day global forecast in under two minutes on a single high-performance server. Researchers emphasize that the AI model complements rather than replaces existing methods, offering a valuable tool for meteorologists. The team plans to open-source the model’s core architecture later this year to encourage broader scientific collaboration. Read the full article at: https://technologyreview.com/2024/05/15/climanet-ai-weather-forecasting/