STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting

Shi Quan Foo, Chi-Ho Wong, Zhihan Gao, Dit-Yan Yeung, Ka-Hing Wong, Wai-Kin Wong

Abstract: 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/sqfoo/stldm_official

Assigned Action Editor: ~Andreas_Lehrmann1

Submission Number: 5706