A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, and NWP Data Using Machine Learning over South Korea

Authors

Hyoju Park, Pukyong National University
Hiroyuki Miyazaki, Global Data Lancers (GLODAL) Incorporation
Menas Kafatos, Chapman UniversityFollow
Seung Hee Kim, Chapman UniversityFollow
Yangwon Lee, Pukyong National UniversityFollow

Document Type

Article

Publication Date

1-29-2026

Abstract

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day−1 in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment.

Comments

This article was originally published in Water, volume 18, issue 3, in 2026. https://doi.org/10.3390/w18030353

Recommended Citation

Park, H.; Miyazaki, H.; Kafatos, M.; Kim, S.H.; Lee, Y. A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, and NWP Data Using Machine Learning over South Korea. Water 2026, 18, 353. https://doi.org/10.3390/w18030353

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This work is licensed under a Creative Commons Attribution 4.0 License.