STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting

TL;DR

STLDM is a diffusion-based spatio-temporal model that improves precipitation nowcasting accuracy and efficiency by combining deterministic forecasting with latent diffusion enhancement.

Contribution

The paper introduces STLDM, a novel two-stage diffusion model that effectively captures complex precipitation patterns and outperforms existing methods.

Findings

Achieves superior accuracy on radar datasets

Improves inference efficiency over previous models

Effectively models stochastic and complex weather patterns

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.