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}{this https URL}.

通过提出一种两阶段降雨预测扩散模型 (TRDM)，我们改善了深度神经网络在长期降雨预报中的准确性，解决了模糊生成图像和空间位置错误的问题。第一阶段捕捉强大的时间信息并保留低分辨率条件下的空间信息，第二阶段对第一阶段生成的低分辨率图像进行高分辨率重建。我们展示了在 MRMS 和瑞典雷达数据集上最先进的结果。