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    확률대응법을 이용한 레이더 강우 추정관계식의 매개변수 결정 = Parameter estimation of radar rain rate equation using probability matching method

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    https://www.riss.kr/link?id=T13384481

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Radar is widely used to observe the rain rate field and to prevent the flood damage. Since the Mt. Gwan-ak radar was introduced in 1969, the Korea Meteorological Administration has expanded its radar network covering the entire Korean peninsula.
    As a remote sensing tool, radar collects the reflectivity intensity from the rain drops in the air. The reflectivity data should be converted in the rain rate data to be used for various applications. The so-called Z-R relation is developed for this purpose. Parameters of the Z-R relation are generally estimated by the Least Square Method (LSM) or recently by the Probability Matching Method (PMM). This study evaluated the PMM for the parameter estimation of the Z-R relation.
    As a first step, the sensitivity analysis was done to decide the threshold number of data pairs and the data interval for the development of a histogram. As a result, it was found that at least 1,000 number of data pairs are required to apply the PMM for the parameter estimation. This amount of data is similar to that collected for 2 hours. Also, the data interval for the histogram was found to be at least 100(Reflectivity: 0.6 dBZ space for 0~60 dBZ, Rain rate: 0.26 dBR space for 0~25 dBR). Additionally, it was found that the matching the first moment is better than the zeroth moment, and that the data pairs comprising 30 to 100% are better for the PMM application.
    Finally above findings were applied to the rainfall event occurred on July 9, 2011. The radar data used were those from the Mt. Bisl radar, and the rain gauge data were the TM data within the radar umbrella. The optimal parameters were decided to be A=90, and b=1.5, which were also found to be similar to previous studies. Also, the radar rain rate derived by applying these parameters was found to be better matched to the rain gauge rain rate, than the conventional Z-R relation.
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    Radar is widely used to observe the rain rate field and to prevent the flood damage. Since the Mt. Gwan-ak radar was introduced in 1969, the Korea Meteorological Administration has expanded its radar network covering the entire Korean peninsula. As a...

    Radar is widely used to observe the rain rate field and to prevent the flood damage. Since the Mt. Gwan-ak radar was introduced in 1969, the Korea Meteorological Administration has expanded its radar network covering the entire Korean peninsula.
    As a remote sensing tool, radar collects the reflectivity intensity from the rain drops in the air. The reflectivity data should be converted in the rain rate data to be used for various applications. The so-called Z-R relation is developed for this purpose. Parameters of the Z-R relation are generally estimated by the Least Square Method (LSM) or recently by the Probability Matching Method (PMM). This study evaluated the PMM for the parameter estimation of the Z-R relation.
    As a first step, the sensitivity analysis was done to decide the threshold number of data pairs and the data interval for the development of a histogram. As a result, it was found that at least 1,000 number of data pairs are required to apply the PMM for the parameter estimation. This amount of data is similar to that collected for 2 hours. Also, the data interval for the histogram was found to be at least 100(Reflectivity: 0.6 dBZ space for 0~60 dBZ, Rain rate: 0.26 dBR space for 0~25 dBR). Additionally, it was found that the matching the first moment is better than the zeroth moment, and that the data pairs comprising 30 to 100% are better for the PMM application.
    Finally above findings were applied to the rainfall event occurred on July 9, 2011. The radar data used were those from the Mt. Bisl radar, and the rain gauge data were the TM data within the radar umbrella. The optimal parameters were decided to be A=90, and b=1.5, which were also found to be similar to previous studies. Also, the radar rain rate derived by applying these parameters was found to be better matched to the rain gauge rain rate, than the conventional Z-R relation.

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    목차 (Table of Contents)

    • 제 1 장 서론 1
    • 1.1 레이더의 도입 1
    • 1.2 국내 레이더의 도입 2
    • 1.3 반사도와 강우강도의 관계 추정 2
    • 1.4 연구목적 및 내용 3
    • 제 1 장 서론 1
    • 1.1 레이더의 도입 1
    • 1.2 국내 레이더의 도입 2
    • 1.3 반사도와 강우강도의 관계 추정 2
    • 1.4 연구목적 및 내용 3
    • 제 2 장 이론적 배경 5
    • 2.1 단일편파레이더의 강우 관측원리 7
    • 2.1.1 레이더 방정식 7
    • 2.1.2 단일편파레이더의 강우 관측원리 9
    • 2.2 단일편파레이더의 하드웨어 종류 및 기능 10
    • 2.3 단일편파레이더의 편파변수 14
    • 2.3.1 수평 반사도 14
    • 2.3.2 시선속도 14
    • 2.3.3 스펙트럼 폭 15
    • 2.4 단일편파레이더의 강우 추정관계식 16
    • 2.4.1 Z-R 관계식 16
    • 2.4.2 Z-R 관계식의 매개변수를 결정하기 위한 기존의 방법 18
    • 2.4.3 Z-R 관계식의 선행연구 20
    • 제 3 장 확률대응법을 이용한 매개변수 결정 23
    • 3.1 확률도시공식 23
    • 3.2 확률대응법 24
    • 3.2.1 {Z
    • Z>Zτ}와 {R
    • R>Rτ} 범위의 확률대응 26
    • 3.2.2 {Z
    • Z0<Z<Zτ}와 {R
    • R0<R<Rτ} 범위의 확률대응 27
    • 3.2.3 본 연구에 적용한 확률대응법 30
    • 제 4 장 확률대응법의 민감도 분석 34
    • 4.1 모의자료의 생성 34
    • 4.2 자료 수에 따른 민감도 분석 37
    • 4.3 히스토그램 구간 간격에 따른 민감도 분석 54
    • 제 5 장 강우 추정관계식의 매개변수 결정 66
    • 5.1 대상자료 66
    • 5.1.1 레이더 자료 및 우량계 자료 66
    • 5.1.2 대상 호우사상 69
    • 5.2 강우 추정관계식의 매개변수 결정 71
    • 5.3 레이더 강우와 지상 강우의 비교 평가 75
    • 제 6 장 결론 78
    • 제 7 장 참고문헌 80
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