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.

With the expansion of urbanization in China, ecological environments are becoming more and more prominent. Uncovering driving factors and ways of regulating ecosystem health has become a hot topic for regional sustainable development. This paper adopted the improved vigor–organization–resilience service (VORS) model to diagnose the regional ecosystem health status in Guangxi from 2000 to 2020 and verify the main factors affecting ecosystem health. Considering the influencing factors (including vegetation, terrain, climate and human activities), the mechanism of driving factors associated with regional ecosystem health was analyzed by using a geographic detector (GD), a multiscale geographically weighted regression model (MGWR), and the XGBOOTS-SHAP model. The results show that the spatial distribution of ecosystem health is characterized by low values in the central region and high values in the northern and eastern regions with higher elevations from 2000 to 2020. The spatial agglomeration evolution changes from agglomeration to dispersion, and the regional urbanization distribution and evolution are consistent. The interaction of driving factors for ecosystem health and vegetation is enhanced significantly, while the interaction of climate factors is relatively weak. And most of the impacts of human activities on the ecological environment are negative. The vegetation factor has a dominant positive effect on ecosystem health, while human activity elements have a weak negative effect on ecosystem health. Meanwhile, climate factors are complex and changeable, and their impacts on ecosystem health are changeable, leading to corresponding changes in other factors. This study provides scientific reference for the harmonious and sustainable development of humans and nature in southern China.

With the expansion of urbanization in China, ecological environment are becoming more and more prominent. To explore the driving factors and regulation ways of ecosystem health has become a hot issue in realizing regional sustainable development. This paper adopted the improved vigor-organization-resilience-service (VORS) model to diagnose the regional ecosystem health status from 2000 to 2020 in Guangxi and find out the main factors affecting the ecosystem health. Considering the influence factors (including vegetation, terrain, climate and human activities), the mechanism of driving factors on regional ecosystem health was analyzed by using geographic detector (GD), multiscale geographically weighted regression model (MGWR), XGBOOTS-SHAP model. The results found that the spatial distribution of ecosystem health was characterized by low values in the central region, and high values in the northern and eastern regions with higher elevations from 2000 to 2020. The spatial agglomeration evolution changed from agglomeration to dispersion, and the regional urbanization distribution and evolution were consistent. The interaction of driving factors on ecosystem health and vegetation was enhanced significantly, while the interaction of climate factors was relatively weak. And most of the impacts of human activities on ecological environment are negative. Vegetation factor has a dominant positive effect on ecosystem health, while human activity elements have a weak negative effect on ecosystem health. Meanwhile, climate factors are complex and changeable, and their impacts on ecosystem health are changeable, which leads to corresponding changes in other factors. This study provides scientific reference for the harmonious and sustainable development of man and nature in southern China.

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.

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.