  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. The LDM produces more accurate precipitation predictions, while the
comparisons are more mixed when predicting whether the precipitation exceeds
predefined thresholds. The clearest advantage of the LDM is that it generates
more diverse predictions than DGMR or PySTEPS. Rank distribution tests indicate
that the distribution of samples from the LDM accurately reflects the
uncertainty of the predictions. Thus, LDMs are promising for any applications
where uncertainty quantification is important, such as weather and climate.
