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.

通过引入扩散模型于降水预测任务，提出了一种基于历史观测数据的短时降水预报条件扩散模型（SRNDiff）。SRNDiff 通过在去噪过程中加入额外的条件解码器模块，实现了端到端的降水预测。该模型在条件生成方面超越了 GANs，虽然需要更多计算资源，但在训练过程中表现出更高的稳定性和效率，并生成更能反映未来降水情况的高质量降水分布样本。这充分验证了扩散模型在降水预测中的优势和潜力，并为提升降水预测提供了新的见解。