메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국기상학회 Asia-Pacific Journal of Atmospheric Sciences 한국기상학회지 제40권 제4호
발행연도
2004.8
수록면
409 - 418 (10page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

이 논문의 연구 히스토리 (3)

초록· 키워드

오류제보하기
Effectiveness of high and low resolution GCM information were analysed using probabilistic diagnostic method for Korean water resources managements. The formulation based on the significance probability of the Kolmogorov-Smirnov test for detecting differences between target (observation) and indicator variable(GCM). AMIP-Ⅱ (Atmospheric Model Intercomparison Project-Ⅱ) type GCM simulations done by ECMWF (European Centre for Medium-Range Weather Forecasts) were used for high resolution indicator variable and SMIP(Seasonal Prediction Model Intercomparison Project) type GCM simulations named Metri-AGCM(4°×5°) done by Korean Meteorological Agency (KMA) were used for low resolution indicator variable. The former has 2 and 2 degrees in longitude and latitude respectively and the latter has 4 and 5 degrees. Nodal surface precipitation and temperature values of both GCMs near 7 major river basins in Korea were used as indicator variables with analysis window concept. Observed mean areal precipitation and discharge values on each watershed were used for target variable. Monte Carlo simulations were used to establish the significant threshold of the estimator values. The results show that high resolution GCM is more significant to discriminate the extremes from target variables. It means that high resolution GCM can give more helpful information for water resources planning and managements. Considering this effectiveness, high resolution simulations are suggested for the future water resouces management application in spite of various limitations of the present GCM simulations.

목차

Abstract
1. 서론
2. 분석기법
3. 사례분석
4. 결과분석
5. 결론
감사의 글
Appendix
참고문헌

참고문헌 (8)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2009-453-016245170