• 한국수자원학회논문집
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• 제47권10호
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• pp.867-878
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• 2014

기상레이더의 관측 특성상, 지형클러터 등의 관측영역 한계로 인한 관측공백 지역이 발생한다. 이러한 레이더 빔의 차폐는 강우량의 과소추정 원인이 된다. 이를 해결하기 위해 본 연구에서는 Hybrid Scan Reflectivity (HSR) 기법을 개발하고 기존 방법 결과와 비교하였다. 결과에 의하면, 기존 레이더 관측방법으로 지형에 의해 반사도 정보를 얻지 못하는 영역에 대하여 HSR 기법이 레이더 강우량을 추정할 수 있음을 확인하였다. 반사도 스캔기법과 빔차폐/비 빔차폐영역에서 모두 HSR 기법을 적용한 결과가 정확성이 가장 뛰어났다. 다음으로 각 방법별 레이더 추정 강우량을 HEC-HMS에 적용하여 홍수 유출량 추정 정확성을 평가하였다. HSR 기법에 의한 유출량은 RAR 산출 시스템과 M-P 관계식 대비 상관계수는 평균 7%와 10%, Nash-Sutcliffe Efficiency는 평균 18%와 34% 향상되었다. 따라서 정확한 홍수량 추정을 위해 수문분야에 HSR 기법에 의해 추정된 강우량을 활용할 필요성이 있는 것으로 사료된다.

• 대기
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• 제24권3호
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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.