WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval

• URL: http://arxiv.org/abs/2511.17558v2
• Date: Thu, 27 Nov 2025 08:53:34 GMT
• Title: WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval
• Authors: Chunlei Shi, Han Xu, Yinghao Li, Yi-Lin Wei, Yongchao Feng, Yecheng Zhang, Dan Niu,
• Abstract summary: We propose WaveC2R, a novel wavelet-driven coarse-to-refined framework for radar retrieval.<n>We show that WaveC2R achieves state-of-the-art performance in satellite-based radar retrieval.
• Score: 15.574201840981035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Satellite-based radar retrieval methods are widely employed to fill coverage gaps in ground-based radar systems, especially in remote areas affected by terrain blockage and limited detection range. Existing methods predominantly rely on overly simplistic spatial-domain architectures constructed from a single data source, limiting their ability to accurately capture complex precipitation patterns and sharply defined meteorological boundaries. To address these limitations, we propose WaveC2R, a novel wavelet-driven coarse-to-refined framework for radar retrieval. WaveC2R integrates complementary multi-source data and leverages frequency-domain decomposition to separately model low-frequency components for capturing precipitation patterns and high-frequency components for delineating sharply defined meteorological boundaries. Specifically, WaveC2R consists of two stages (i)Intensity-Boundary Decoupled Learning, which leverages wavelet decomposition and frequency-specific loss functions to separately optimize low-frequency intensity and high-frequency boundaries; and (ii)Detail-Enhanced Diffusion Refinement, which employs frequency-aware conditional priors and multi-source data to progressively enhance fine-scale precipitation structures while preserving coarse-scale meteorological consistency. Experimental results on the publicly available SEVIR dataset demonstrate that WaveC2R achieves state-of-the-art performance in satellite-based radar retrieval, particularly excelling at preserving high-intensity precipitation features and sharply defined meteorological boundaries.

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