Accurate predictions are crucial for addressing the challenges posed by climate change. Given South Korea’s location within the East Asian summer monsoon domain, characterized by high spatiotemporal variability, enhancing prediction accuracy for regions experiencing heavy rainfall during the summer monsoon is essential. This study aims to derive temporal weighting functions using hybrid surface rainfall radar-observation data as the target, with input from two forecast datasets: the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) and the KLAPS Forecast System. The results indicated that the variability in the optimized parameters closely mirrored the variability in the rainfall events, demonstrating a consistent pattern. Comparison with previous blending results, which employed event-type-based weighting functions, showed significant deviation in the average AUC (0.076) and the least deviation (0.029). The optimized temporal weighting function effectively mitigated the limitations associated with varying forecast lead times in individual datasets, with RMSE values of 0.884 for the 1 h lead time of KLFS and 2.295 for the 4–6 h lead time of MAPLE. This blending methodology, incorporating temporal weighting functions, considers the temporal patterns in various forecast datasets, markedly reducing computational cost while addressing the temporal challenges of existing forecast data.

Radar is useful for monitoring flash flood in urban areas because it provides rainfall data with high spatial and temporal resolution for a wide area. In 2014, the Korea Institute of Civil Engineering and Building Technology introduced the first X-band dual-polarization radar in Korea and has subsequently been researching on flash floods in the metropolitan area using the rainfall data. Considering the various advantages of X-band dual-polarization radar, the Water Hazard Information Platform in Korea introduced two X-band dual-polarization radars (KRU and YSU radars) into the metropolitan area, in 2017. This study described the characteristics of KRU and YSU radars, their observation strategies, and quality control techniques. Moreover, we also assessed the hydrological applications of the X-band dual-polarization radars in the metropolitan area by analyzing three years of rainfall data from 2017 to 2019.

This study evaluated the effects of watershed area, rain gauge density, rain gauge distribution, and rainfall movement direction on regional average rainfall estimates by comparing the spatial interpolation results. To this end, the interpolated watershed mean using radar grid data from the Ministry of Environment's TM rain gauge point was compared with the watershed mean of all radar grids within the watershed. Results showed that eight or more rain gauges are required for accurate area average rainfall estimates in small watersheds of less than 500 km 2. In addition, the inverse distance weighting method tends to underestimate area average rainfall, whereas the Thiessen method tends to overestimate it. Furthermore, as linearity of rain gauge distribution increased, the number of rainfall events with outliers increased. In particular, when the correlation coefficient exceeded 0.7, outliers appeared to rapidly increase, sharply increasing the possibility of overestimating or underestimating area average rainfall. This study clearly showed that there are limits to area-averaged rainfall estimates when using rain gauge rainfall data in small watersheds.

In order to more quantitatively compare the differences between radar reflectivity and snowfall intensity against ground-observed snow depth, snow depth ground observation data and weather radar observation data were analyzed. For radar observation data, cumulative reflectivity and precipitation intensity derived from reflectivity, differential reflectivity, and specific differential phase (Quantitative Precipitation Estimation) were compared. As a result of the analysis, it was found that the precipitation intensity was similar to the variation according to the snow depth and time compared to the radar reflectivity. However, although the initial accumulation tendency of snow fall was very well matched due to the characteristics of snow cover, which is sensitive to temperature and has accumulation and melting characteristics, the melting tendency from daytime showed a difference. Therefore, it is judged that more accurate snow depth can be estimated only when precipitation intensity estimation method according to temperature is derived and used in addition to methods such as accumulation of reflectivity. AcknowledgementThis research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

The primary aim of a rain radar is to observe ground-level rainfall accurately. Effective quality control of rain radar data is necessary to ensure accuracy. In the field of hydrology, various methods have been employed to improve the quality of radar rainfall by correcting or synthesizing it with gauge rainfall. However, it is more important to enhance the quality of radar polarimetric variables in order to improve the quality of radar rainfall. Rain radar systems generate various polarimetric variables through complex radar hardware systems and signal processing algorithms. Measurement errors may arise from equipment abnormalities, inappropriate operating settings, and obstructions in observation during signal processing algorithms and production process of polarimetric variables. Because rain radar is an observation device operating in real time, it entails more frequent and continuous quality control compared to flowmeters that intermittently measure streamflow. This study introduces a series of real-time quality control methods tailored for rain radar, including inspection of radar rainfall accuracy, quality analysis of polarimetric variables (ρ HV , Z DR , Φ DP), error estimation of polarimetric variables, and long-term variability of errors.

Quantifying changes in groundwater storage (GWS) remains a fundamental challenge in hydrology, given the sparsity of long-term in situ monitoring networks and the inherent difficulty of direct subsurface observation. Although GRACE and GRACE-FO satellite missions provide a means of tracking total terrestrial water storage at the continental scale, their coarse spatial resolution (~300 km) and monthly temporal sampling limit their direct applicability to regional groundwater studies. Here, we present a spatio-temporal disaggregation and data fusion framework for reconstructing daily GWS anomalies (GWSAs) across the Korean Peninsula, integrating GRACE/GRACE-FO Mascon solutions with the GLDAS Catchment Land Surface Model (CLSM). The approach leverages satellite-derived mass variations to constrain the model’s long-term anomaly structure while retaining the high-frequency temporal dynamics of land-surface modeling. The framework is evaluated against in situ bedrock monitoring well records from five sites: Seoul, Cheongyang, Uiseong, Imsil, and Wonju. Raw time-series correlations range from R = 0.14 to 0.70; upon removal of the monthly climatology to isolate non-seasonal variability, R improves to 0.49–0.72 across all sites, reaching 0.718 in Seoul and 0.707 in Cheongyang, with Cheongyang’s RMSE declining from 8.847 to 7.574 cm. These results indicate that the GRACE-CLSM fusion framework captures genuine sub-monthly groundwater dynamics beyond the dominant seasonal cycle. To our knowledge, this represents the first reconstruction of daily GWS changes for the Korean Peninsula with explicit preservation of spatial mass conservation, and the resulting dataset has direct utility for operational groundwater monitoring in a region subject to hydroclimatic variability.