Radar–Rain Gauge Merging for High-Spatiotemporal-Resolution Rainfall Estimation Using Radial Basis Function Interpolation DOI Creative Commons

Soorok Ryu,

Joon Jin Song, GyuWon Lee

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 530 - 530

Published: Feb. 4, 2025

This study introduces methods for generating fused precipitation data by applying radial basis function (RBF) interpolation, which integrates radar reflectivity-based with ground-based gauge measurements. Rain gauges provide direct point rainfall measurements near the ground, while radars capture spatial variability of precipitation. However, radar-based estimates, particularly extreme events, often lack accuracy due to their indirect derivation from reflectivity. The aims produce high-resolution gridded ground merging estimates precise rain were sourced automated synoptic observing systems (ASOSs) and automatic weather (AWSs), data, based on hybrid surface (HSR) composites, all provided Korea Meteorological Administration (KMA). Although RBF interpolation is a well-established technique, its application unprecedented. To validate proposed method, it was compared traditional approaches, including mean field bias (MFB) adjustment kriging-based such as regression kriging (RK) external drift (KED). Leave-one-out cross-validation (LOOCV) performed assess errors analyzing overall error statistics, errors, in intensity data. results showed that RBF-based method outperformed others terms accuracy.

Language: Английский

Radar–Rain Gauge Merging for High-Spatiotemporal-Resolution Rainfall Estimation Using Radial Basis Function Interpolation DOI Creative Commons

Soorok Ryu,

Joon Jin Song, GyuWon Lee

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 530 - 530

Published: Feb. 4, 2025

This study introduces methods for generating fused precipitation data by applying radial basis function (RBF) interpolation, which integrates radar reflectivity-based with ground-based gauge measurements. Rain gauges provide direct point rainfall measurements near the ground, while radars capture spatial variability of precipitation. However, radar-based estimates, particularly extreme events, often lack accuracy due to their indirect derivation from reflectivity. The aims produce high-resolution gridded ground merging estimates precise rain were sourced automated synoptic observing systems (ASOSs) and automatic weather (AWSs), data, based on hybrid surface (HSR) composites, all provided Korea Meteorological Administration (KMA). Although RBF interpolation is a well-established technique, its application unprecedented. To validate proposed method, it was compared traditional approaches, including mean field bias (MFB) adjustment kriging-based such as regression kriging (RK) external drift (KED). Leave-one-out cross-validation (LOOCV) performed assess errors analyzing overall error statistics, errors, in intensity data. results showed that RBF-based method outperformed others terms accuracy.

Language: Английский

Citations

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