Cloud Imagery Generation by Physics-Constrained Diffusion Networks

Satellite cloud imagery prediction holds significant application value in meteorology, aviation safety, and renewable energy management. However, due to the complex and nonlinear dynamics of cloud systems, existing deep learning methods often produce blurry, distorted, or flickering results with temporal inconsistencies when generating long-term, high-fidelity predictions. To address these challenges, this paper proposes a novel multi-stage training conditional diffusion prediction model. Our framework leverages the powerful generative modeling capabilities of diffusion models to produce cloud image frames with rich texture details, while incorporating a pre-trained RAFT optical flow estimator to introduce motion consistency loss that explicitly constrains the dynamic evolution between consecutive frames, thereby effectively enhancing temporal coherence in sequences. Furthermore, to overcome training instability in complex systems, we design an innovative three-stage progressive training strategy that gradually guides the model through fundamental content generation, dynamic consistency modeling, and realism optimization via adversarial training. Extensive experiments on real satellite cloud imagery datasets demonstrate that our method significantly outperforms existing models across multiple quantitative metrics and qualitative visual assessments. The generated prediction sequences not only exhibit superior visual clarity and realism but also show remarkable temporal consistency, validating the effectiveness and superiority of our framework for complex spatiotemporal prediction tasks. This work provides a robust and efficient novel approach for integrating generative models with physical constraints to achieve high-quality spatiotemporal forecasting.