FreCast: Refining Radar Echo Intensity via Phase-Preserving Amplitude Residual Diffusion for Precipitation Nowcasting

2026-08-08

AI Summary

This study addresses the issue of local intensity bias in radar echo predictions within deep learning–based precipitation nowcasting. To this end, the authors propose a two-stage structure-guided intensity refinement framework: the first stage generates an initial echo prediction, while the second stage corrects local intensity distortions under spatial structural constraints through an amplitude residual diffusion mechanism that preserves phase information. This work is the first to introduce phase-preserving amplitude residual diffusion into nowcasting, effectively mitigating intensity distortion. The method consistently achieves performance gains across three benchmark datasets and significantly improves the prediction quality of intense precipitation structures and rainband continuity, particularly at longer lead times.

Abstract

Precipitation nowcasting predicts the spatiotemporal evolution of future radar echoes from historical radar echo sequences, thereby estimating the occurrence, development, and movement of precipitation over the near term. In recent years, deep learning has become an important approach to precipitation nowcasting. Although state-of-the-art models can generally capture the overall spatial distribution of future precipitation, their predictions still exhibit substantial biases in radar echo intensity at individual locations. This observation motivates a more targeted strategy for reducing forecast errors. Instead of regenerating an entire radar echo sequence without spatial constraints, the predicted precipitation structure can be used to guide the refinement of echo intensities at individual locations. This structure-guided refinement directly targets echo intensity biases. Accordingly, we propose FreCast, a two-stage framework for radar echo prediction. The first stage generates an initial forecast of future radar echoes. The second stage uses the spatial structure of the initial forecast as a constraint to further correct intensity biases at individual locations in the first-stage prediction. Experiments on three datasets demonstrate that FreCast achieves consistent improvements across forecast skill metrics. Qualitative results further show that FreCast better preserves rainband continuity and intense precipitation structures at longer lead times.

Problem

• precipitation nowcasting
• radar echo intensity
• forecast bias
• spatiotemporal prediction
• intensity refinement

Innovation

• phase-preserving diffusion
• amplitude residual
• structure-guided refinement
• precipitation nowcasting
• radar echo intensity correction