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.

翻译：扩散模型因其能够生成高质量且逼真的样本而被广泛应用于图像生成领域，这与生成对抗网络（GANs）和变分自编码器（VAEs）在图像质量方面存在一定局限性形成对比。我们将扩散模型引入降水预报任务，基于历史观测数据提出了一种带条件扩散模型的短时降水临近预报方法，称为SRNDiff。通过在去噪过程中引入额外的条件解码器模块，SRNDiff实现了端到端的条件降雨预测。SRNDiff由两个网络组成：一个去噪网络和一个条件编码器网络。条件网络由多个独立的UNet网络构成，这些网络在不同分辨率下提取条件特征图，为扩散模型提供精准的条件信息以指导条件生成。尽管需要更多计算资源，SRNDiff在预测精度上超越了GANs。与基于GANs的方法相比，SRNDiff模型在训练过程中展现出更高的稳定性和效率，并能生成更准确反映未来实际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报中的优势与潜力，为提升降雨预测提供了新思路。