## 4 Results
### Evaluation of Calibration Bias  
In this section, the daily mean bias was calculated between adjacent radars to evaluate \(\varepsilon_{\text{SC}}\). Figure 10 shows the scatter plots of \(Z_{\text{H}}\) in the overlapping volumes from the matched radar pairs before applying the calibration bias for a rain case on 15 May 2020. For each radar pair, the slope of the \(Z_{\text{H}}\) was parallel to the 1:1 line, so the difference between the two radars was caused by the calibration bias. The comparison was valid because the number of pairs of \(Z_{\text{H}}\) was at least 3,392. The GNG was well-calibrated during the entire period as shown in Figure 7. The calibration bias of MYN can be estimated using the mean bias for the MYN-GNG pair. The mean bias for the MYN-GNG pair was \(-\)0.01 dB. Therefore, we can expect that the calibration bias of MYN was close to 0.0 dB (The \(\varepsilon_{\text{SC}}\) of MYN was 0.63 dB on this event). The \(Z_{\text{H}}\) of KSN was less than \(-\)2.01 dB compared to MYN and higher than 4.26 dB compared to JNI. That means the \(Z_{\text{H}}\) of JNI was less than \(-\)6.26 dB compared to GNG. Furthermore, the \(Z_{\text{H}}\) of JNI was similar to that of GSN (mean bias of GSN-JNI pair was \(-\)0.52 dB). The calibration bias of JNI and GSN was close to \(-\)6.0 dB. The \(\varepsilon_{\text{SC}}\) of JNI and GSN radar for 15 May 2020 in Figure 7 were \(-\)6.25 dB and \(-\)6.14 dB, respectively. To sum up, the mean bias before calibration bias allowed us to determine the mis-calibrated radar if there is a well-calibrated radar over the radar network. Figure 10: Scatter plots of radar reflectivity between two radars during the 15 May 2020 before calibration bias correction. The dashed line represents the mean bias of the reflectivity. The numbers from (1) to (9) correspond to the order of matched radar pairs in Table 2. The black numbers indicate the mean bias within \(\pm 1\) dB, while the red numbers indicate the mean bias out of \(\pm 1\) dB. Figure 11 shows the mean bias for each radar pair in Figure 10 after applying \(\varepsilon_{\text{SC}}\). If \(\varepsilon_{\text{SC}}\) was not calculated during precipitation events, the \(\varepsilon_{\text{SC}}\) calculated on the former or latter event was applied. The mean biases of JNI-KSN and SSP-JNI pairs were reduced to \(-0.59\) and \(-0.09\) dB, and these were further reduced to values closer to \(0.0\) dB after correcting the \(\varepsilon_{\text{SC}}\). This result proved that \(\varepsilon_{\text{SC}}\) was well calculated. For all radar pairs, the mean bias ranged from \(-6.83\) dB to \(4.26\) dB before applying \(\varepsilon_{\text{SC}}\). On the other hand, the mean bias ranged from \(-1.07\) to \(1.75\) dB after applying \(\varepsilon_{\text{SC}}\). Figure 12 represents the mean bias before (triangles) and after (dots) applying \(\varepsilon_{\text{SC}}\) to four rainfall events. These four events were widespread precipitation events passing through the Korean Peninsula. The red line indicates the mean bias within \(\pm 1.0\) dB. The mean bias before correcting \(\varepsilon_{\text{SC}}\) shows a large inhomogeneity of \(Z_{\text{H}}\) over the radar network. For CASE I, the mean biases of all radar pairs except for the GDK-KWK pair were greater than \(\pm 1.0\) dB. For the MYN-GNG, MYN-KSN, and PSN-MYN pairs, the mean biases were \(-2.85\), \(-4.02\), and \(4.18\) dB, respectively, due to the large calibration bias of MYN (\(3.64\) dB). The mean biases were reduced from \(-1.08\) dB to \(0.83\) dB after correcting \(\varepsilon_{\text{SC}}\). For the JNI-KSN, GSN-JNI, and SSP-JNI pairs, the calibration of JNI (\(-6.73\) dB) caused large mean biases (\(4.06\) dB, \(-4.89\) dB, and \(-6.94\) dB). These mean biases were reduced to \(-2.09\) dB, \(0.6\) dB, and \(0.69\) dB after correcting \(\varepsilon_{\text{SC}}\). For CASE III and CASE IV, the mean bias between  
Figure 11: Same as Figure 10 after calibration bias correction. the two radars was noticeably reduced after correcting the \(\epsilon_{\text{SC}}\).