Cloud observation serves as the fundamental bedrock for acquiring comprehensive cloud-related information. The categorization of distinct ground-based clouds holds profound implications within the meteorological domain, boasting significant applications. Deep learning has substantially improved ground-based cloud classification, with automated feature extraction being simpler and far more accurate than using traditional methods. A reengineering of the DenseNet architecture has given rise to an innovative cloud classification method denoted as CloudDenseNet. A novel CloudDense Block has been meticulously crafted to amplify channel attention and elevate the salient features pertinent to cloud classification endeavors. The lightweight CloudDenseNet structure is designed meticulously according to the distinctive characteristics of ground-based clouds and the intricacies of large-scale diverse datasets, which amplifies the generalization ability and elevates the recognition accuracy of the network. The optimal parameter is obtained by combining transfer learning with designed numerous experiments, which significantly enhances the network training efficiency and expedites the process. The methodology achieves an impressive 93.43% accuracy on the large-scale diverse dataset, surpassing numerous published methods. This attests to the substantial potential of the CloudDenseNet architecture for integration into ground-based cloud classification tasks.

Ground-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. Initially, a Cascaded Causal LSTM (CCLSTM) with a dual-memory group decoupling structure is designed to enhance the representation of short-term cloud changes. Next, self-attention memory units are incorporated to capture the long-term dependencies and emphasize the non-stationary characteristics of cloud movements. These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. Finally, by embedding gradient highway units and adding skip connection, CloudPredRNN++ is constructed into a hierarchical recursive structure, mitigating the gradient vanishing and enhancing the uniform modeling of temporal–spatial features. Experiments on the sequence ground-based cloud dataset demonstrate that CloudPredRNN++ can predict the future cloud state more accurately and quickly. Compared with other spatiotemporal sequence prediction models, CloudPredRNN++ shows significant improvements in evaluation metrics, improving the accuracy of cloud dynamics forecasting and alleviating long-term dependency decay, thus confirming the effectiveness in ground-based cloud prediction tasks.