Enhancing Spaceborne Precipitation Radar Detail: Diffusion-Based Super-Resolution With Weak-Echo Recovery

Haotian Tan, Xiang Pan, Hao Huang, Kun Zhao, L Chen, Kai Li, Yuanyuan Xu, Yinghui Lu, Haiqin Chen, Haonan Chen

IEEE Transactions on Geoscience and Remote Sensing · 01 January 2026 · Colorado State University (incl. CIRA)

In one sentence. Latent diffusion model super-resolves GPM spaceborne precipitation radar to 1-km scale, recovering weak echoes below sensitivity and sharpening convective structures in real cases.

Abstract

Spaceborne precipitation radars provide vertically resolved observations of precipitating systems with near-global coverage, but their coarse horizontal footprint limits the depiction of finescale convective structures and weak echoes. This study develops a deep-learning super-resolution framework for the GPM Dual-frequency Precipitation Radar (DPR) based on a latent diffusion model (LDM). A physically consistent training dataset is constructed by coupling convection-permitting WRF simulations with multi-frequency radar forward modeling, where 1-km S-band reflectivity is used as the high-resolution target, and Ku/Ka reflectivity aggregated to a DPR-like ~5-km footprint at multiple heights is used as the conditional input. The proposed LDM performs conditional diffusion in a compact latent space learned by a VQ-VAE and incorporates multi-branch fusion of dual-frequency and multi-height features to enhance the effective horizontal resolution while producing S-band-consistent reflectivity factor and partially recovering weak-echo regions below DPR sensitivity. On the synthetic test set, the LDM achieves substantially improved perceptual and distributional similarity (e.g., markedly lower FID/LPIPS) compared with representative deterministic and generative baselines, while maintaining competitive structure-focused skill for intense echoes. Evaluations on multiple real cases—including landfalling tropical cyclones and midlatitude/inland convective systems—demonstrate improved echo continuity and sharper convective/rainband morphology that is broadly consistent with independent ground-based S-band radar observations, indicating promising transferability beyond the simulation domain. The proposed framework offers a practical pathway to enhance the utility of current and future spaceborne precipitation radars for finescale precipitation analysis, while providing an effective bridge between spaceborne radar observations and ground-based radar applications through S-band-consistent high-resolution reflectivity fields.

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