  Diffusion models are widely used in image generation because they can
generate high-quality and realistic samples. This is in contrast to generative
adversarial networks (GANs) and variational autoencoders (VAEs), which have
some limitations in terms of image quality.We introduce the diffusion model to
the precipitation forecasting task and propose a short-term precipitation
nowcasting with condition diffusion model based on historical observational
data, which is referred to as SRNDiff. By incorporating an additional
conditional decoder module in the denoising process, SRNDiff achieves
end-to-end conditional rainfall prediction. SRNDiff is composed of two
networks: a denoising network and a conditional Encoder network. The
conditional network is composed of multiple independent UNet networks. These
networks extract conditional feature maps at different resolutions, providing
accurate conditional information that guides the diffusion model for
conditional generation.SRNDiff surpasses GANs in terms of prediction accuracy,
although it requires more computational resources.The SRNDiff model exhibits
higher stability and efficiency during training than GANs-based approaches, and
generates high-quality precipitation distribution samples that better reflect
future actual precipitation conditions. This fully validates the advantages and
potential of diffusion models in precipitation forecasting, providing new
insights for enhancing rainfall prediction.
