Two-stage Rainfall-Forecasting Diffusion Model
XuDong Ling ChaoRong Li FengQing Qin LiHong Zhu Yuanyuan Huang
Deep neural networks have made great achievements in rainfall
prediction.However, the current forecasting methods have certain limitations,
such as with blurry generated images and incorrect spatial positions. To
overcome these challenges, we propose a Two-stage Rainfall-Forecasting
Diffusion Model (TRDM) aimed at improving the accuracy of long-term rainfall
forecasts and addressing the imbalance in performance between temporal and
spatial modeling. TRDM is a two-stage method for rainfall prediction tasks. The
task of the first stage is to capture robust temporal information while
preserving spatial information under low-resolution conditions. The task of the
second stage is to reconstruct the low-resolution images generated in the first
stage into high-resolution images. We demonstrate state-of-the-art results on
the MRMS and Swedish radar datasets. Our project is open source and available
on GitHub at:
\href{https://github.com/clearlyzerolxd/TRDM}{https://github.com/clearlyzerolxd/TRDM}.