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2019 Vol.52, Issue 5 Preview Page

Research Article

Selection framework of representative general circulation models using the selected best bias correction method

최적 편이보정 기법의 선택을 통한 대표 전지구모형의 선정

Young Hoon Songa
,
Eun-Sung Chungb*
,
Jang Hyun Sungc
송 영훈a
,
정 은성b*
,
성 장현c
aMS Student, Department of Civil Engineering, Seoul National University of Science and Technology
bAssociate Professor, Department of Civil Engineering, Seoul National University of Science and Technology
cResearcher, Han River Flood Control Office, Ministry of Environment
a서울과학기술대학교 건설시스템공학과 석사과정
b서울과학기술대학교 건설시스템공학과 부교수
c국토교통부 한강홍수통제소 연구사
* Corresponding Author
31 May 2019. pp. 337-347
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Abstract

This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM’s and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.

Keywords
General circulation model (GCM)
Quantile mapping
Random forest
Bias correction
TOPSIS (Technique for order of preference by similarity to ideal solution)

본 연구에서는 미래 기후예측을 위하여 활용되는 전지구모형(general circulation model, GCM) 중 우리나라에 적합한 대표 GCM을 선정하는 방법을 제시하였다. 이에 격자 기반 GCM 결과를 IDW (Inverse Distance Weighted) 방법을 사용하여 기상 관측소로 지점 규모로 상세화를 하여 관측강수와 비교하였다. GCM과 관측자료 사이의 편이를 보정하기 위하여 6가지 Quantile Mapping 방법과 Random Forest 기법을 사용하였고, 성능 지표를 비교하여 대표 편이보정방법을 선정하였다. 편이보정된 GCM 모의 결과에 대한 성능을 계산하고 다기준의사결정기법 중 하나인 TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) 방법을 이용하여 가장 우수한 GCM을 선정하였다. 그 결과 편이보정방법을 NPT (Non-Parametric Transformation) 방법 중 EQ (Empirical Quantile) 방법이 선정되었고, TOPSIS 성능 평가 결과, GISS-E2-R이 가장 우수하였다. 그 다음으로 우수한 GCM을 순서대로 제시하면 MIROC5, CSIRO-Mk3-6-0, CCSM4 이었다. 향후 더 많은 GCM 자료를 이용한다면 보다 보편적인 결과를 도출할 수 있을 것으로 기대된다.

키워드
전지구모형
Quantile mapping
Random forest
TOPSIS
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 52
  • No :5
  • Pages :337-347
  • Received Date : 2019-02-28
  • Revised Date : 2019-03-31
  • Accepted Date : 2019-03-31
  • DOI :https://doi.org/10.3741/JKWRA.2019.52.5.337
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