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.

翻译：扩散模型在图像生成领域已被广泛采用，其生成的样本在质量和多样性上均优于生成对抗网络（GANs）。我们引入了一种用于降水临近期预报（基于最新观测数据的短期预报）的潜扩散模型（LDM）。与GANs相比，LDM训练更加稳定且计算需求更低，尽管其生成过程计算代价更高。我们将该模型与基于GAN的深度降雨生成模型（DGMR）以及统计模型PySTEPS进行了基准测试。LDM能生成更准确的降水预测，但在预测降水是否超过预设阈值时，比较结果则更为复杂。LDM最显著的优势在于，其生成的预测结果比DGMR或PySTEPS更多样化。秩分布检验表明，LDM生成的样本分布能准确反映预测的不确定性。因此，在不确定性量化至关重要的应用领域（如天气与气候），潜扩散模型具有广阔前景。