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dc.contributor.advisor최용상-
dc.contributor.author김혜실-
dc.creator김혜실-
dc.date.accessioned2020-02-03T16:31:52Z-
dc.date.available2020-02-03T16:31:52Z-
dc.date.issued2020-
dc.identifier.otherOAK-000000163114-
dc.identifier.urihttp://dcollection.ewha.ac.kr/common/orgView/000000163114en_US
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/252907-
dc.description.abstractSatellite-based operational cloud height retrievals generally assume a plane-parallel homogeneous cloud exists in each field of regard, or pixel. However, this assumption ignores vertical inhomogeneity, which is of particular importance for optically thin, but geometrically thick, ice clouds. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature/heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is firstly tested by a radiative transfer model, Streamer. We generate diverse ice clouds with varying cloud heights, effective radius, and cloud optical thickness. By proxy data, we describe the relationship between spectral cloud emissivity and brightness temperatures for three channels at 11, 12, and 13.3 µm. Then we show that the uncertainties in our resulting cloud temperature using inferring cloud emissivity ranges quite correspond to cloud thickness. Then we apply our methodology to the current passive satellite, MODIS/Aqua over the western North Pacific Ocean during August 2015. We estimate minimum/maximum heights for three cloud regimes, i.e., single-layer thin and thick ice clouds, and multi-layered clouds. Our results are assessed through comparison with CALIOP Version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layer optically thin clouds show good agreement with those from CALIOP; bias for maximum (minimum) heights versus the cloud top (base) heights of CALIOP are 0.13 km (–1.01 km). For optically thick and multi-layered clouds, the biases of the estimated cloud heights from the cloud top/base become larger. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multi-channel infrared imager. The vertically resolved heights for ice clouds can contribute new information for studies involving weather prediction and cloud radiative effects. ;위성의 적외 탐지로 구름의 온도를 산출하는 기법은 대게 구름이 ‘균질한 입자로 이루어진 하나의 층’이라는 가정을 사용한다. 이 가정은 다양한 얼음상 입자로 이루어진 권운의 온도 산출에 문제가 된다. 본 논문에서는 구름 입자의 특성에 따라 가질 수 있는 파장별 구름 방출률 범위가 다르다는 점을 활용하여 실제 구름이 가질 수 있는 온도 범위(최대/최소 구름온도값)를 추정하는 기법을 개발하였다. 파장별 구름 방출률 범위는 11, 12, 13.3 µm 영역에서의 밝기온도차로 추정한다. Streamer 복사전달모델을 통해 알고리즘의 물리적 배경을 설명하였으며, Aqua 위성에 탑재된 MODIS C6 자료를 통해 알고리즘을 개발 및 검증하였다. 알고리즘 성능 시험은 2015년 8월 한 달 동안 북태평양 지역의 얼음상 구름에 대해 이루어 졌다. 알고리즘 검증자료는 CALIOP V4 구름 자료를 사용하였고, 검증 대상 구름 화소는 총 11873개였다. 광학적으로 두께가 얇은 단층 얼음상 구름의 경우, 본 알고리즘에서 추정된 최대/최소 구름온도값은 실제 구름의 꼭대기/바닥면과 근소한 차이(0.13/–1.01 km)를 보였다. 그러나 광학적으로 두꺼운 단층 또는 다층 얼음상 구름의 경우는 구름 꼭대기/바닥면과의 차이(0.30/-1.71 km 또는 1.47/–4.67 km)가 상대적으로 크게 나타났다. 본 알고리즘은 얼음상 구름온도의 적외 채널 위성 산출물의 정확도를 높일 뿐만 아니라, 실제 구름의 두께를 고려한 구름 온도의 불확실성을 함께 제공한다는 장점이 있다. 또한 본 알고리즘은 2018년 12월에 발사된 한국의 두 번째 기상위성인 GK2A을 포함하여 최신의 국제기상위성들에 활용이 가능하다.-
dc.description.tableofcontents1.Introduction 1 1.1. Background 1 1.2. Goals and objectives 6 2. Data/models used in this study 8 2.1.AQUA/MODIS 8 2.2.CALIOP/CALIPSO 9 2.3.Numerical weather model product 10 2.4.Radiative transfer model 11 3. Methodology for inferring cloud heights 12 3.1.The observed IR radiances for the plane parallel cloud 12 3.2.Spectral ice cloud emissivity and its effects to cloud heights 15 3.3.A new method to infer ice cloud height 20 4. Generation of Look-Up Tables (LUTs) 24 4.1. Physical basis of LUTs 24 4.2. LUTs by the radiative transfer model (RTM) 26 4.2.1. Relationship between Spectral cloud emissivity and three indices, BT-
dc.description.tableofcontents11, BTD-
dc.description.tableofcontents11,13, and BTD-
dc.description.tableofcontents11,12 26 4.2.2. Tests for ec-
dc.description.tableofcontents11, ∆ec-
dc.description.tableofcontents11,12, and Tc by RTM 34 4.2.3. Sensitive tests for ec-
dc.description.tableofcontents11,12, and Tc by RTM 44 4.3. LUTs by satellite observations 54 5. Results 61 5.1. Study domain 61 5.2. Clear-sky maps generated from MODIS 62 5.3. Comparison of min/max(Tc) with CALIOP for three granules 65 5.3.1. A scene for single-layer optically thin ice cloud 65 5.3.2. A scene for single-layer optically thick ice cloud 70 5.3.3. A scene for multi-layered cloud 72 5.4. Comparison of min/max(Tc) with CALIOP for August 2015 74 6.Conclusions 78 6.1. Discussion of results 78 6.2. Summary 83 References 87 Abstract (in Korean) 95-
dc.formatapplication/pdf-
dc.format.extent7469489 bytes-
dc.languageeng-
dc.publisher이화여자대학교 대학원-
dc.subject.ddc628-
dc.titleRetrieval of ice cloud temperatures using spectral cloud emissivity and its uncertainty-
dc.typeDoctoral Thesis-
dc.format.pagexiv, 97 p.-
dc.identifier.thesisdegreeDoctor-
dc.identifier.major대학원 대기과학공학과-
dc.date.awarded2020. 2-
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