Advancements in Weather Forecasting: A Review of Satellite Imagery and AI Integration

This review examines the integration of meteorological satellite imagery with artificial intelligence (AI), focusing on traditional machine learning and deep learning methods in weather forecasting. It begins by explaining the basic concepts and importance of satellite imagery in capturing highresolution atmospheric data. The study highlights how AI, especially deep learning, improves the analysis of these complex datasets, enhancing weather pattern identification and anomaly detection. We discuss the integration of satellite data with traditional forecasting methods, recent advancements in hybrid algorithms, and sophisticated data assimilation techniques. The review also covers deep learning models like CNNs, RNNs, and GANs, demonstrating their superior performance in predicting weather phenomena. Challenges such as high computational costs, resource requirements, and model generalizability are addressed, along with future directions in model optimization and hybrid approaches. This synthesis underscores the significant impact of combining satellite imagery with AI on weather forecasting accuracy and reliability.