Non Storm Cloud

Research on "non-storm clouds" focuses on improving the detection, classification, and three-dimensional reconstruction of various cloud types from satellite and ground-based imagery, often employing machine learning techniques. Current efforts utilize convolutional neural networks (CNNs), vision transformers (ViTs), and generative adversarial networks (GANs) to address challenges like cloud shadow removal, cloud gap imputation, and accurate cloud type classification in diverse atmospheric conditions. These advancements enhance our understanding of cloud properties, improve the accuracy of climate models, and support applications in agriculture, renewable energy forecasting, and space weather prediction.