Weather radar is a useful tool for monitoring and forecasting severe weather but has limited coverage due to beam blockage from mountainous terrain or other factors. To overcome this issue, an intelligent technology called “Echo Reconstruction UNet (ER-UNet)” is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather.

Accurate radar-based quantitative precipitation estimation (QPE) plays a critical role in disaster prevention and reduction. In mountainous terrain, blockage of low-elevation radar beams can lead to missing low-level precipitation observations, while errors in specific differential phase K_DP retrieval further limit QPE accuracy. To address these issues, this study uses data from two S-band dual-polarization weather radars located in Fangchenggang and Beihai, together with ground rain gauges, local raindrop size distribution (DSD) data, and digital elevation model (DEM) data. Hybrid-scan elevation construction and dual-radar mosaicking are adopted to improve the effective coverage of low-elevation observations, and a threshold-fusion strategy for K_DP, denoted as K_{DP_TF}, is proposed. The strategy selects the larger of the conventionally quality-controlled K_{DP_QC} and variationally reconstructed K_{DP_VR} at each valid gate. Based on the CSU-HIDRO algorithm, three QPE products, namely the quality-control QPE (QC_QPE), the variational-reconstruction QPE (VR_QPE), and the threshold-fusion QPE (TF_QPE), are generated. The precipitation events associated with Typhoon Yagi in 2024 and Typhoon Matmo in 2025 are selected for evaluation. The results indicate that TF_QPE most effectively mitigates the underestimation of 24 h accumulated precipitation. Compared with QC_QPE, the magnitude of the negative relative bias (RB) of TF_QPE is reduced, from −27.30% to −20.07% and from −16.56% to −9.78%, respectively, for the Yagi and Matmo events; the root-mean-square error (RMSE) decreases from 46.76 to 43.79 mm and from 43.27 to 43.13 mm, respectively. The hourly precipitation results further show that TF_QPE better captures the peak intensity of heavy precipitation and exhibits no evident temporal lag relative to rain gauge observations. For the heavy precipitation category, TF_QPE also yields the highest probability of detection (POD) and critical success index (CSI). These results suggest that the QPE framework developed in this study, which integrates hybrid-scan elevation construction, dual-radar mosaicking, and threshold-fusion-based K_DP optimization, can improve the effective low-elevation observational coverage in mountainous terrain and mitigate the underestimation of typhoon-induced heavy precipitation in radar QPE. These results provide technical references for typhoon-related heavy-rainfall monitoring and flood early-warning as well as radar-QPE applications over coastal Guangxi.