RRBF-KMA: high-resolution radar–gauge merged precipitation dataset for South Korea (2016–2024)
- 1BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote sensing (CARE), Kyungpook National University, Daegu, 41566, Republic of Korea
- 2Department of Statistical Science, Baylor University, Waco, TX 76798, USA
- 3School of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
Abstract
This study presents a long-term, high-resolution precipitation dataset over South Korea generated by merging nationwide composite radar reflectivity with dense rain gauge observations from the Korea Meteorological Administration (KMA). The dataset provides precipitation at 10 min temporal resolution and 0.5 km spatial resolution on a regular grid of 2305 pixels × 2881 pixels, covering the period from 2016 to 2024.
Rain gauge observations, originally recorded at 1 min intervals, are quality-controlled and aggregated to 10 min accumulations prior to merging. Radar inputs are obtained from the nationwide composite reflectivity product provided by KMA. To ensure temporal consistency in the merged precipitation fields, a radar–gauge bias correction scheme is applied in which scaling factors are updated at 10 min intervals during precipitation events and constrained relative to preceding time steps. This approach reduces spurious temporal variability while maintaining sensitivity to evolving precipitation systems.
Residual differences between bias-corrected radar estimates and gauge observations are interpolated using a Residual Radial Basis Function (RRBF) method. The method models spatially structured residuals to preserve fine-scale radar-derived variability while incorporating gauge-based corrections. The entire merging procedure is computationally efficient, requiring approximately one minute per 10 min analysis, which supports near-real-time implementation.
The resulting product provides temporally continuous and spatially consistent precipitation fields suitable for hydrological applications, extreme rainfall analysis, and urban flood forecasting. The dataset, together with detailed documentation of the generation and validation procedures, is publicly available for research and operational use. The dataset is publicly available at https://doi.org/10.5281/zenodo.19491708 (Ryu et al., 2026a) and https://doi.org/10.5281/zenodo.19562910 (Ryu et al., 2026b).
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BibTeX
@article{ryu2026rrbfkma,
title = {{RRBF-KMA: high-resolution radar–gauge merged precipitation dataset for South Korea (2016–2024)}},
author = {S. Ryu and J. J. Song and K. S. Lim and G. Lee},
journal = {Earth System Science Data},
year = {2026},
month = oct,
doi = {10.5194/essd-18-7679-2026},
url = {https://doi.org/10.5194/essd-18-7679-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - RRBF-KMA: high-resolution radar–gauge merged precipitation dataset for South Korea (2016–2024)
AU - S. Ryu
AU - J. J. Song
AU - K. S. Lim
AU - G. Lee
PY - 2026
DA - 2026/10/26
JO - Earth System Science Data
DO - 10.5194/essd-18-7679-2026
UR - https://doi.org/10.5194/essd-18-7679-2026
AB - This study presents a long-term, high-resolution precipitation dataset over South Korea generated by merging nationwide composite radar reflectivity with dense rain gauge observations from the Korea Meteorological Administration (KMA). The dataset provides precipitation at 10 min temporal resolution and 0.5 km spatial resolution on a regular grid of 2305 pixels × 2881 pixels, covering the period from 2016 to 2024. Rain gauge observations, originally recorded at 1 min intervals, are quality-controlled and aggregated to 10 min accumulations prior to merging. Radar inputs are obtained from the nationwide composite reflectivity product provided by KMA. To ensure temporal consistency in the merged precipitation fields, a radar–gauge bias correction scheme is applied in which scaling factors are updated at 10 min intervals during precipitation events and constrained relative to preceding time steps. This approach reduces spurious temporal variability while maintaining sensitivity to evolving precipitation systems. Residual differences between bias-corrected radar estimates and gauge observations are interpolated using a Residual Radial Basis Function (RRBF) method. The method models spatially structured residuals to preserve fine-scale radar-derived variability while incorporating gauge-based corrections. The entire merging procedure is computationally efficient, requiring approximately one minute per 10 min analysis, which supports near-real-time implementation. The resulting product provides temporally continuous and spatially consistent precipitation fields suitable for hydrological applications, extreme rainfall analysis, and urban flood forecasting. The dataset, together with detailed documentation of the generation and validation procedures, is publicly available for research and operational use. The dataset is publicly available at https://doi.org/10.5281/zenodo.19491708 (Ryu et al., 2026a) and https://doi.org/10.5281/zenodo.19562910 (Ryu et al., 2026b).
SN - 1866-3508
SN - 1866-3516
ER -