Precipitation nowcasting is a critical spatio-temporal prediction task for society to prevent severe damage owing to extreme weather events. Despite the advances in this field, the complex and stochastic nature of this task still poses challenges to existing approaches. Specifically, deterministic models tend to produce blurry predictions while generative models often struggle with poor accuracy. In this paper, we present a simple yet effective model architecture termed STLDM, a diffusion-based model that learns the latent representation from end to end alongside both the Variational Autoencoder and the conditioning network. STLDM decomposes this task into two stages: a deterministic forecasting stage handled by the conditioning network, and an enhancement stage performed by the latent diffusion model. Experimental results on multiple radar datasets demonstrate that STLDM achieves superior performance compared to the state of the art, while also improving inference efficiency. The code is available in https://github.com/sqfoo/stldm_official.

翻译：临近降水预报是一项关键的时空预测任务，对社会预防极端天气事件造成的严重损害至关重要。尽管该领域已取得进展，但此任务的复杂性和随机性仍对现有方法构成挑战。具体而言，确定性模型倾向于产生模糊的预测，而生成模型则常受精度不足的困扰。本文提出一种简单而有效的模型架构，称为STLDM，这是一种基于扩散的模型，它同时结合变分自编码器和条件网络，端到端地学习潜在表示。STLDM将此任务分解为两个阶段：由条件网络处理的确定性预报阶段，以及由潜在扩散模型执行的增强阶段。在多个雷达数据集上的实验结果表明，STLDM相比现有最优方法实现了更优越的性能，同时提高了推理效率。代码可在 https://github.com/sqfoo/stldm_official 获取。