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.

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation.

In the summer of 2023, North China was hit by an exceptionally intense precipitation storm caused by Typhoons Doksuri and Khanun, resulting in significant secondary disasters and underscoring the critical need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products, such as Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission, show great merits for enhancing forecasts. This study uses a dense rain gauge network as a benchmark to evaluate the performance of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products during the 2023 summer extreme precipitation event in North China. The satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER), IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), as well as two gauge-corrected products IMERG final run (IMERG_FR) and GSMaP gauge-adjusted (GSMaP_Gauge). The results show that the satellite-based QPE products, particularly IMERG_LR and GSMaP_MVK, show good performance in capturing the spatial distribution and overall rainfall amounts during the extreme precipitation event. However, they have significant under-detect high-intensity precipitation events in this region. The IMERG products generally outperform the GSMaP products, especially in terms of temporal rainfall measurement, but all products tend to underestimate rainfall. At high rainfall rates, while the detection ability is high, the false alarm ratios are also significantly elevated for all satellite-based QPE products. These findings highlight the need for further improvement of satellite-based QPE products for more accurate and reliable rainfall estimation.

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.

Precipitation datasets derived from reanalysis products play a critical role in weather forecasting and hydrological applications. This study aims to assess the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The assessment is conducted with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations as reference, and the continuous and categorical statistical indicators as assessment metrics. The findings of this study are as follows: 1) CRA40 outperforms ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 shows better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). 2) Both CRA40 and ERA5 generally exhibit an overestimation of precipitation over mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., Xizang-Qinghai plateau, Xinjiang province), while wet regions (e.g., the middle and lower reaches of Changjiang River, and South China) demonstrate relatively less overestimation. 3) ERA5 shows better performance in the detection of daily precipitation than CRA40. Neither CRA40 nor ERA5 can well capture heavy precipitation events. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.