Weather Radar Center, Korea Meteorological Adimistration
Received: 19 Dec 2016 – Accepted: 10 Jan 2017 – Published: 12 Jan 2017
Abstract. This study presents an empirical method for optimizing polarimetric variables in order to improve the accuracy of dual-polarization radar rainfall estimation using data derived from radars operated by different agencies. The empirical method was developed using the Yong-In Testbed (YIT) radar operated by the Korea Meteorological Administration (KMA). The method is based on the determination of relations between polarimetric variables. Relations for Z – ZDR and Z – KDP are derived from the measurements of a two-dimensional video disdrometer installed about 30 km away from the YIT radar. These relations were used to adjust the polarimetric variables of the dual-polarization constant altitude plan position indicator (CAPPI) at a height of 1.5 km. The CAPPI data with the adjusted polarimetric variables were used to estimate rainfalls using three different radar rainfall estimation algorithms. The first algorithm is based on Z, the second on Z and ZDR, and the third on Z, ZDR, and KDP. The accuracy of the radar-estimated rainfall was then assessed using raingauge observations. Three rainfall events with more than 40 mm of maximum hourly rainfall were shown to have the best estimation when the method using Z, ZDR, and KDP was used. However, stratiform precipitation events were better estimated by the algorithm using Z and ZDR. The method was also applied to the data of three radars that belong to KMA and the Ministry of Land, Infrastructure, and Transport. The evaluation was done for six months (May–October) in 2015. The results show an improvement in radar rainfall estimation accuracy for stratiform, frontal, and convective precipitation from approximately 50 % to 70 %.
Yoon, J., Park, J.-S., Kim, H.-L., Suk, M.-K., and Nam, K.-Y.: An empirical QPE method based on polarimetric variable adjustments, Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-409, in review, 2017.
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Latest update: 21 Jan 2017