Accurate estimation of precipitation is essential to predict and prevent natural disasters (e.g. flood, landslide, and heavy snowfall). Recently, weather radars have become a popular tool for quantitative precipitation estimation (QPE) with high spatio-temporal resolution. Especially, in the last decade, QPE performance has been improved by introduction of polarimetric technology to observe multiple hydrometeorological variables at various scales. By being able to measure variables such as the differential reflectivity, specific differential phase, and cross-correlation coefficient, the reliability of estimation has significantly improved as compared to the reflectivity-based method (Ryzhkov et al., 2005a). However, QPEs using dual polarization radar data are still subject to uncertainties resulting from rainfall conversion relationships, combination methods of different parameters, and sampling errors. In this study, we assessed the uncertainty and applicability of conventional QPE algorithms, such as JPOLE (Ryzhkov et al., 2005a) and CSU (Cifelli et al., 2011) algorithms, using the dual polarization radar at Mt. Biseul in the south-eastern part of the Korean Peninsula. Analysis results illustrated that the JPOLE algorithm outperformed the CSU algorithm slightly for daily and hourly rainfall analysis using various metrics. The higher accuracy was found in the stations located within less than 60 km from radar and 100 m in the elevation.
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