Intercomparison of Phased-Array Weather Radar and GPM-DPR Toward Calibration of Ground-Based Weather Radar Networks

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Authors: Tsubasa Goto, Hiroshi Kikuchi, Yoshitaka Nakamura, Tomoo USHIO
Cited by: 0
References: 29

Abstract

Because individual meteorological radar units have limited observation coverage, networks of multiple meteorological radars typically must be deployed. However, because of the complex structure of each radar, the optimum method to achieve precise observation accuracy is unknown. Our objective was to investigate the feasibility of using the satellite-borne global precipitation measurement/dual-frequency precipitation radar (GPM/DPR) as a calibration reference. We compared the radar reflectivity factor of two phased-array weather radars (PAWRs), located in Osaka and Kobe, with GPM/DPR data to assess the observation accuracy of each. To compare data obtained from instruments with two different spatiotemporal resolutions and observation frequencies, we proposed a comparison method that takes into account frequency conversion, coordinate transformation, volume matching, and observation beam patterns. The results of the comparison between GPM/DPR and PAWR showed that evaluation parameters, such as bias error, were consistent (even for PAWRs installed at different locations) and that GPM/DPR could function as a calibrator. Furthermore, the evaluation of temporal effects demonstrated that the correlation coefficient of radar reflectivity factor remained above 0.8 with a time difference of less than 4 min, followed by a progressive decrease as the time difference widened. These results using PAWR revealed the impact of comparisons using high-density data and time differences in comparisons, which was not possible with previously studied results using parabolic radars.

Fields of Study & Subjects

Precipitation Measurement and Analysis Calibration and Measurement Techniques Radio Wave Propagation Studies

Publication details

Journal / source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume
19
Pages
9470–9480
Publication date
1 January 2026
Language
EN