RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion
This addresses the challenge of scalable and accurate precipitation nowcasting for meteorology and disaster management, representing a novel method for a known bottleneck.
The paper tackled the problem of precipitation nowcasting by proposing a token-wise attention diffusion model that integrates attention into both the U-Net and encoder, eliminating the need for separate latent modules. The result is a method that significantly outperforms state-of-the-art approaches, improving local fidelity, generalization, and robustness in forecasting.
Precipitation nowcasting, predicting future radar echo sequences from current observations, is a critical yet challenging task due to the inherently chaotic and tightly coupled spatio-temporal dynamics of the atmosphere. While recent advances in diffusion-based models attempt to capture both large-scale motion and fine-grained stochastic variability, they often suffer from scalability issues: latent-space approaches require a separately trained autoencoder, adding complexity and limiting generalization, while pixel-space approaches are computationally intensive and often omit attention mechanisms, reducing their ability to model long-range spatio-temporal dependencies. To address these limitations, we propose a Token-wise Attention integrated into not only the U-Net diffusion model but also the spatio-temporal encoder that dynamically captures multi-scale spatial interactions and temporal evolution. Unlike prior approaches, our method natively integrates attention into the architecture without incurring the high resource cost typical of pixel-space diffusion, thereby eliminating the need for separate latent modules. Our extensive experiments and visual evaluations across diverse datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches, yielding superior local fidelity, generalization, and robustness in complex precipitation forecasting scenarios.