Precipitation Forecasting

Precipitation forecasting aims to accurately predict rainfall amount and location, crucial for mitigating weather-related disasters and informing resource management. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), generative adversarial networks (GANs), and diffusion models, often incorporating data from multiple sources (e.g., satellites, radar, numerical weather prediction models) and employing techniques like post-processing and uncertainty quantification. These advancements improve forecast accuracy, particularly for extreme precipitation events and at finer spatial and temporal resolutions, leading to better decision-making in various sectors including agriculture, disaster preparedness, and water resource management.