A new weather forecasting system called Pangu-Weather uses deep learning to predict global weather faster and more accurately than traditional methods. By analyzing 43 years of weather data and training neural networks with millions of parameters, Pangu-Weather outperforms current numerical weather prediction methods in accuracy for various weather factors and time ranges. The system's 3D Earth Specific Transformer architecture and hierarchical temporal aggregation algorithm help improve prediction accuracy, especially for short to medium-range forecasts. Pangu-Weather can forecast extreme weather events like tropical cyclones and create large-member ensemble forecasts in real-time. This system not only proves that AI-based methods can surpass traditional weather prediction methods but also suggests new ways to enhance deep learning weather forecasting systems.