## 5 Summary and Conclusions  
In this study, a technique for calculating and monitoring the calibration bias of S-band radar in a nationwide radar network was developed by combining GC monitoring, self-consistency (GC), and intercomparison. Each method has an advantage in that there is no need for large tasks for comparative analysis with other equipment. The combined method can calculate, monitor, and evaluate the calibration bias in real time regardless of weather conditions using radar data only. The GC reflectivity (Z\({}_{\text{C}}\)) was statistically analyzed to estimate the calibration bias during the non-precipitation periods. The relative calibration adjustment (RCA) defined as the change in Z\({}_{\text{C}}\) relative to baseline, was calculated from the ground clutter map (GCM). The baseline for calculating RCA was determined from the Z\({}_{\text{C}}\) used to construct the GCM. We suggest two types of baselines (95 th and 50 th percentiles) to identify the change in antenna pointing (e.g., azimuth and elevation angle offset) and to calculate the RCA from the statistics of the Z\({}_{\text{C}}\). The 50 th percentile was used as an indicator (dMMedian) to determine the azimuth and/or elevation slip change, while the 95 th percentile was used to calculate the RCA. The calibration bias is estimated from the absolute calibration bias calculated based on SC of previous rain events and changes in RCA. The SC method between the differential phase and Z\({}_{\text{H}}\) was used to calculate the absolute calibration bias. Several criteria were considered to accurately estimate absolute calibration bias. The mean bias between  
Figure 12: Mean bias of reflectivity between two radars for 4 rainfall events: (**a**) 9 May 2020, (**b**) 15 May 2020, (**c**) 24 June 2020 and (**d**) 29 June 2020. The R1 represents the reference radar, while the R2 represents the matched radar. The triangle and dot indicate the mean bias before (triangle) and after (dot) calibration bias correction, respectively. two adjacent radars was calculated to verify the homogeneity of the radar network by applying the absolute calibration bias from SC method. For this purpose, the overlapping volume between two adjacent radars was determined based on radar beam geometry. The RCA and absolute calibration bias in ZH for each site in the KMA network were analyzed. The RCA reflected changes in the radar status, such as changes in antenna pointing and calibration bias after routine inspection. The elevation and/or azimuth angle issues were revealed by significant change in dMedian and RCA. In addition, the change in RCA can be used to identify the change in calibration bias. Specifically, the calibration bias estimated from RCA is similar to that from SC. With the GC monitoring method, it is possible to estimate the calibration bias even when the calibration bias changes during the non-precipitation period. Moreover, the mean bias between adjacent radars was reduced to a value closer to 0.0 dB after calibration bias correction. This means that the homogeneity of ZH over a nationwide radar network can be improved through radar calibration. In other words, the calibration is a requirement for the quantitative use of ZH. To summarize, we recommend how to make use of combined method according to weather condition and surrounding geography. SC and intercomparison require rain event, while GC monitoring method does not require rain event. If the GC exist near radar, it is possible to continuously know the calibration bias in time and monitor the radar status from the RCA with an interval of an hour during the non-precipitation period. During or after rain event, the absolute calibration bias of each radar and the mean bias between radars can be calculated. The absolute calibration bias and the mean bias let us know which radar has to be re-calibrated and how spatially uniform data can be used over the radar network. We conclude that the combined method contributes to monitoring and estimating the calibration bias in ZH in real time.