Precipitation Prediction

Accurate precipitation prediction is crucial for various sectors, from agriculture to disaster management, and remains a significant challenge due to the inherent complexity of atmospheric processes. Current research focuses on improving prediction accuracy and reliability through advanced machine learning techniques, including convolutional neural networks (CNNs), U-Nets, transformers, and diffusion models, often applied as post-processing methods for numerical weather prediction (NWP) outputs or trained on high-resolution satellite data. These models are being enhanced by incorporating physical understanding of atmospheric dynamics and addressing uncertainties through probabilistic forecasting and uncertainty quantification methods. Improvements in precipitation forecasting translate directly to better preparedness for extreme weather events and more informed decision-making across numerous societal applications.