Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

Abstract

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.

Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China

“…In previous studies [14,[53][54][55], Z DR and K DP are close to zero and exhibit high noise levels for light rainfall [56], and substantial observation error of Z DR with low signal-noise-ratios (SNRs) leads to large estimation biases [33]. Z h and Z DR suffer from great attenuation with heavy precipitation, which leads to large biases.…”

Observational Characteristics of Dual-Polarization Radar for Heavy Rainfall Events in Capital Area

“…교차상관계수가 0.95 이상이면 강수, 0.90~0.95는 우박, 0.9 이하는 이슬비를 나타낸다 (Bringi and Chandrasekar, 2001) (Ryzhkov et al, 2005)과 CSU-HIDRO (Cifelli et al, 2011)를 이용하여 레이더 강우를 추정하였다. 기존에는 국내 호우사상에 대한 두 알고리즘의 적용성을 분석한 연구가 수행되었다 (Noh et al, 2014;Kim et al, 2016). JPOLE 알고리즘은 간단히 Fig.…”

Quantitative Rainfall Estimation Using Weather Radar Based on the Improved Kalman Filter Method

“…The former uses the estimate of the variable at a previous time to obtain the current a priori estimate. On the other hand, the latter uses the current measurement value to correct the a priori estimate of the previous process and obtain the current posteriori estimate Liu et al, 2016;Noh et al, 2014). The specific recursive calculation process was:…”