A Survey on Global Weather Forecasting Models, Techniques, and Emerging Trends

The forecasting of global weather patterns assists in predicting qualitative and quantitative atmospheric variables such as temperature, humidity, atmospheric pressure, precipitation, wind speed, and clouds movement. Accurate weather forecasting plays a crucial role in agriculture, transportation, disaster management, and environmental monitoring. Recent advances in numerical weather prediction, satellite observations, artificial intelligence, and high-performance computing have significantly improved forecasting capabilities. This survey reviews recent developments in global weather forecasting, emphasizing machine learning, deep learning, probabilistic forecasting, ensemble forecasting, graph neural networks, Fourier neural operators, and federated learning approaches. The study highlights major achievements, limitations, and future research directions toward scalable, privacy-preserving, and highly accurate weather forecasting systems.