Precipitation Associated

Research on precipitation forecasting is intensely focused on improving accuracy and efficiency, particularly for high-resolution, short-term predictions crucial for disaster management and resource allocation. Current efforts leverage machine learning, employing deep learning architectures like U-Nets and diffusion models to process diverse data sources, including satellite imagery and radar data, often incorporating techniques like federated learning for handling distributed datasets. These advancements aim to overcome limitations of traditional numerical weather prediction models by generating more realistic and reliable precipitation forecasts at finer spatial and temporal scales, ultimately enhancing preparedness for extreme weather events and optimizing water resource management.