Clouds play a vital role in the earth-atmosphere system.Accurate cloud classification is essential for improving regional weather forecasts and understanding the global energy budget.However,precise and objective identification of ground-based cloud images remain challenging,primarily due to the limited availability of standardized cloud image datasets.This constraint hampers the further development of deep learning models for cloud classification.To address this issue,we propose a methodological hypothesis:can pre-training deep learning models on large scale,non-meteorological datasets enhance the accuracy of cloud classification,followed by fine-tuning with domain-specific cloud imagery? To test this hypothesis,we implement three deep learning architectures—two convolutional neural networks (ResNet50 and MobileNet-V2) and a self-attention-based Vision Transformer (ViT)—to perform ground-based cloud classification.We conduct a comparative analysis of models trained solely on cloud image datasets and those pre-trained on the ImageNet dataset before being fine-tuning with cloud data.Our results highlight the impact of pre-training strategies across different architectures.Even without pre-training,ResNet50 and MobileNet-V2 achieve strong baseline performance,with average F₁ scores of 0.85 and 0.87,respectively.Notably,the ViT model shows significant improvement with pre-training:the F₁ score increases from 0.79 to 0.96—a 21.5％ enhancement—demonstrating the importance of large-scale pre-training for architectures reliant on spatial feature extraction.Analysis of misclassified cases reveals that deep learning models primarily rely on spatial characteristics to distinguish cloud types.This suggests that incorporating auxiliary meteorological parameters—such as cloud-base height and thickness—as embedded features may further enhance model interpretability and performance.The performance gains from pre-training are largely attributed to improved edge detection and morphological pattern recognition,which are especially beneficial for complex architectures like ViT.In addition to these theoretical contributions,this study achieves practical implementation by deploying a stable cloud classification model on a mobile platform (available at http://43.142.162.19:5174/).This application supports real-time cloud-type identification via photo uploads and provides educational content,thereby promoting public engagement with atmospheric science and demonstrating the real-world applicability of deep learning for ground-based cloud observation.
Future research will focus on integrating cloud physical properties—such as thermodynamic parameters and radiative characteristics—into deep learning models.Fusing physical constraints with visual features may enhance classification robustness,reduce data requirements,and improve interpretability,paving the way for explainable AI systems in atmospheric sciences.In conclusion,this study establishes deep learning as an effective approach for automated cloud classification and underscores the critical role of pre-training,especially for advanced architectures.The mobile deployment further bridges meteorological research and public outreach,demonstrating the dual scientific and educational value of AI-powered cloud classification systems.