## 3 Application of Uncertainty Quantification Methods and Results
### Results of Quantitative Radar-Based Precipitation Estimation  
As described in Section 3.1, eight ensemble radar-based precipitation estimates were calculated using two quality control algorithms, two precipitation estimation methods, and two post-processing precipitation bias correction methods for the entire Korean peninsula. Following the quality control methods (ORPG and fuzzy algorithms) and the post-processing precipitation bias correction methods (G/R ratio and LGC methods), the accuracy of the radar-based precipitation estimates was compared with the observed precipitation reported by the AWSs (see Table 2). It can be seen from Table 2a that the values of root mean square error (RMSE) and correlation coefficient (CC) relating to the ORPG method were 8.56 mm/h and 0.79, respectively, while those relating to the fuzzy method were 8.32 mm/h and 0.81, respectively. Thus, the accuracy of radar-based precipitation estimates produced using the fuzzy algorithm was superior to that produced using the ORPG method. Table 2b shows the results of the precipitation estimates obtained by applying the G/R ratio and LGC methods to all 18 precipitation events (18 precipitation events \(\times\) 2 quality control methods). The RMSE and CC values of the G/R ratio method were 7.29 mm/h and 0.93, respectively, while those of the LGC method were 6.06 mm/h and 0.94, respectively. Thus, the accuracy of radar-based precipitation estimates produced using the LGC method, which corrected the bias of the precipitation estimates at each radar pixel, was superior to that produced using the G/R ratio method. Figure 3 shows Q-Q (quantile-quantile) plots of the RMSEs of the radar-based precipitation estimates in each quality control algorithm and bias correction method for the 18 precipitation cases. It can be seen that the RMSEs of the fuzzy algorithm and the LGC method were superior for each precipitation case. The results of applying the quality control (fuzzy and ORPG algorithms) and post-processing methods (G/R ratio and LGC methods) are shown in Figure 4. Illustrated in Figure 4a-c are the AWS-observed precipitation and radar-based precipitation estimates produced by applying the fuzzy and ORPG algorithms for case 11, which was an intense precipitation event that occurred in the Incheon \begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy** & **ORPG** & **Fuzzy** \\ \hline RMSE (mm/h) & 8.56 & 8.32 \\ \hline Correlation Coefficient & 0.79 & 0.81 \\ \hline
**(b)** Accuracy of bias correction methods (gauge-to-radar: G/R ratio; local gauge correction: LGC methods). \\ \hline
**Accuracy** & **G/R ratio** & **LGC** \\ \hline RMSE (mm/h) & 7.29 & 6.06 \\ \hline Correlation coefficient & 0.93 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the accuracy of radar-based precipitation estimates in each quality control algorithm and bias correction method. Figure 3: Q-Q (quantile-quantile) plots of RMSEs of radar-based precipitation estimates in each (a) quality control algorithm and (b) bias correction method. and west sea regions at 10:20 local standard time (LST) on 28 August 2012. Although the three panels have slightly different color intensity of the index, this is considered in the comparison of the results. In Figure 4a, the precipitation intensity in regions 1 and 2 was 16-20 and 8-12 mm/h, respectively. Precipitation estimates in the same precipitation zones are shown in Figure 4b,c. In region 1, Figure 4c shows very intense precipitation estimates (>40 mm/h in some regions), whereas the precipitation estimates shown in Figure 4b are intense but closer to the observed values. In region 2, the precipitation estimates shown in Figure 4c are more intense than those shown in Figure 4b, which shows precipitation estimates closer to the observed values. However, in region 3 of Figure 4b, the results produced by applying the fuzzy algorithm show wave echoes that have not been removed completely, indicating that the approach requires further improvement.