Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Understand

Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs).

• We introduce a latent diffusion model (LDM) for precipitation nowcasting - short-term forecasting based on the latest observational data.
• The LDM is more stable and requires less computation to train than GANs, albeit with more computationally expensive generation.
• We benchmark it against the GAN-based Deep Generative Models of Rainfall (DGMR) and a statistical model, PySTEPS.