• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.867-878
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• 2014

Due to the nature of weather radar, blank areas occur due to impediments to observation, such as the ground clutter. Radar beam blockages have resulted in the underestimation rainfall amounts. To overcome these limitations, this study developed the Hybrid Scan Reflectivity (HSR) technique and compared the HSR results with existing methods. As a result, the HSR technique was able to estimate rainfalls in areas from which no reflectivity information was observable using existing methods. In case of estimating rainfalls depending on reflectivity scan techniques and beam-blockage/non beam-blockage, the HSR accuracy is superior. Furthermore, rainfall amounts derived from each method was inputted to the HEC-HMS to examine the accuracy of the flood simulations. The accuracy of the results using the HSR technique in contrast to the RAR calculation system and M-P relation was improved by 7% and 10%(based on correlation coefficients), and 18% and 34%(based on Nash-Sutcliffe Efficiency), on average, respectively. Therefore, it is advised that the HSR technique be utilized in the hydrology field to estimate flood discharge more accurately.

• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.