A context-conditioned Denoising Diffusion Probabilistic Model (DDPM) for generating and analyzing satellite cloud imagery. Trained on a NASA dataset containing high-resolution images with associated timestamps, wind-speed annotations, and learned embeddings. Supports conditional generation (e.g., given wind speed or timestamp), robustness experiments, and rapid prototyping on small 16×16 images with straightforward scaling to larger resolutions.