Cloud Classification

Cloud classification aims to automatically categorize cloud types from various data sources, primarily satellite imagery and radar data, to improve weather forecasting, climate modeling, and environmental monitoring. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), tensorized neural networks (TNNs), and other architectures like autoencoders, often incorporating attention mechanisms and multi-task learning to enhance accuracy and efficiency. These advancements enable more precise cloud detection and segmentation, leading to improved quantitative precipitation estimates and a deeper understanding of cloud-climate interactions, ultimately benefiting both scientific research and practical applications like disaster response.