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EdgeGeoDiff: A Novel Two-Stage Diffusion Approach for Precipitation Downscaling with Edge Details and Geographical
Shiji Zhang1, Chenghong Zhang2, Tao Wu1
1School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.
Sensors (Basel, Switzerland)
|March 28, 2026
Summary
EdgeGeoDiff, a novel diffusion model, enhances precipitation downscaling by integrating edge information and geographical data. This method overcomes limitations of traditional super-resolution techniques, improving precipitation extreme capture and fine-scale variability reconstruction.
Area of Science:
- Hydrology and Remote Sensing
- Deep Learning Applications
- Geospatial Data Analysis
Background:
- Precipitation downscaling enhances coarse-resolution climate data, crucial for hydrological and meteorological studies.
- Deep learning-based super-resolution (SR) methods are increasingly used but struggle with precipitation data's weak high-frequency signals and skewed distributions.
- Existing single-image super-resolution (SISR) approaches often produce overly smooth results, miss extremes, and lack fine-scale variability.
Purpose of the Study:
- To develop an advanced precipitation downscaling model addressing limitations of current SISR methods.
- To leverage edge information and geographical priors to improve the reconstruction of fine-scale precipitation structures.
- To enhance the capture of precipitation extremes and preserve fine-scale variability in downscaled data.
Main Methods:
- Proposed EdgeGeoDiff, a two-stage diffusion model for precipitation downscaling.
- Stage 1: A residual network for initial high-resolution precipitation field reconstruction.
- Stage 2: A diffusion model guided by Laplacian-extracted edge features and geographical priors (e.g., elevation) to generate enhancement residuals.
Main Results:
- EdgeGeoDiff effectively reconstructs fine-scale precipitation details while preserving large-scale patterns.
- The model outperforms conventional SISR methods in RMSE, PSNR, SSIM, and CSI.
- Demonstrated superior performance in capturing high-frequency precipitation signals compared to existing methods.
Conclusions:
- EdgeGeoDiff offers a significant advancement in precipitation downscaling, particularly for capturing extreme events and fine-scale variability.
- The integration of edge information and geographical priors within a diffusion framework is effective for precipitation data.
- The proposed method provides more accurate and detailed high-resolution precipitation estimates for various applications.
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