Precipitation Nowcasting

Precipitation nowcasting aims to predict short-term (typically up to a few hours) rainfall patterns using recent radar or satellite observations, primarily to improve early warning systems for extreme weather events. Current research heavily utilizes deep learning, employing various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, particularly LSTMs), transformers, and diffusion models, often incorporating techniques like spatial-temporal correlation decoupling and physics-informed constraints to enhance accuracy and handle uncertainty. These advancements have significant implications for disaster management, resource allocation (e.g., agriculture, transportation), and improving the overall accuracy and timeliness of weather forecasts.