Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Assessment and merging technique for GPM satellite precipitation product using ground based measurement

GPM 위성 강우자료의 검증과 지상관측 자료를 통한 강우 보정 기법

  • Baik, Jongjin (Center for Built Environment, Sungkyunkwan University) ;
  • Park, Jongmin (Department of Civil and Environmental Enginnering, University of Maryland) ;
  • Kim, Kiyoung (Hydrological Survey Department, Korea Institute of Hydrological Survey) ;
  • Choi, Minha (Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University)
  • 백종진 (성균관대학교 건설환경연구소) ;
  • 박종민 (메릴랜드 건설환경공학과) ;
  • 김기영 (한국수자원조사기술원 하천조사실) ;
  • 최민하 (성균관대학교 수자원전문대학원)
  • Received : 2017.10.30
  • Accepted : 2017.11.27
  • Published : 2018.02.28
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Abstract

Precipitation is a key variable to enhance the understanding of water cycle system and secure and manage the water resources efficiently. In this study, we evaluated the feasibility of GPM precipitation datasets through comparison with the 92 ASOS sites in South Korea during 2015. Additionally, three merging techniques (i.e., Geographical Differential Analysis, Geographical Ratio Analysis, Conditional Merging) were applied to improve accuracy of precipitation by fusing the advantages from point and satellite-based datasets. The results of this study are as follows. 1) GPM dataset indicated slightly overestimation with compared ASOS dataset, especially high uncertainties in summer season. 2) Validation of three merging techniques through jackniffe cross-validation showed that uncertainty were decreased as the spatial resolution increased. Especially, conditional merging showed the best performance among three methods.

강우는 물순환 시스템을 이해를 증가 시킬 뿐만 아니라, 효율적인 수자원 확보 및 관리에 있어서 가장 핵심적인 인자이다. 본 연구는 2015년을 대상으로 한반도에서의 92개의 ASOS 지점자료와 최근에 발사된 GPM 위성강우 자료의 비교를 통하여 활용가능성을 평가하였다. 또한 지점 자료의 장점과 인공위성 자료의 장점을 융합함으로써 보다 개선된 강우자료를 산출하기 위해 3가지의 상세화 방법(Geographical Differential Analysis, Geographical Ratio Analysis, Conditional Merging)들을 적용하였다. 이 연구에서 도출된 결과는 다음과 같다. 1) ASOS 자료와의 검증을 통해 GPM 강우자료가 약간 과대산정되는 편향을 가지고 있는 것을 확인하였으며, 특히 여름 기간에 오차가 높게 발생하는 것으로 나타났다. 2) Jackknife 방법을 통하여 각 합성방법에 대해서 검증하였을 때, 공간해상도가 높아짐에 따라서 오차가 줄어드는 것을 확인하였으며, 상세화 방법 중 conditional merging 방법이 가장 좋은 성능을 나타내었다.

Keywords

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