用扩散模型提升降水短时预报精度，兼顾清晰度与效率
STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting
- 分两阶段：先条件网络确定性预测，再用潜空间扩散模型增强细节
- 在多个雷达数据集上超越现有最优方法，推理速度更快
- 适合气象预警、智能交通等需要高精度实时预报的场景
降水短时预报是应对极端天气、减少灾害损失的关键时空预测任务。尽管该领域已有进展，但其复杂的随机特性仍给现有方法带来挑战：确定性模型易生成模糊结果，生成模型常缺乏准确性。本文提出一种简单高效的架构——STLDM，一种基于扩散的模型，可端到端学习变分自编码器与条件网络的潜在表示。STLDM将任务分解为两个阶段：由条件网络完成确定性预报，再由潜空间扩散模型进行细节增强。在多个雷达数据集上的实验表明，STLDM性能优于当前最优方法，同时提升推理效率。代码已开源：https://github.com/sqfoo/stldm_official。
原文摘要 · Abstract (English)
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.
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