博碩士論文 107022003 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:13 、訪客IP:3.215.186.30
姓名 陳曉如(Hsiao-Ju Chen)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 台灣及南海地區雲的時空特徵: 向日葵8號於夏季觀測之前導研究
(Spatiotemporal Cloud Characteristics over Taiwan and South China Sea: A Pilot Study from Himawari-8 Observation in Summertime)
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摘要(中) 雲覆蓋全球表面約70%,其消長過程與微物理特性變化,對地球能量收支和水文循環有著重大影響。由於過往研究對雲的特性及其發展過程認知不足,迫使雲迄今為止皆為氣候變化預測模型中最大的不確定性因子。過往研究經常使用繞極軌道衛星作為觀測雲的儀器,然而其有限的時間解析度,無法提供連續性的觀測資料,並充分監測短時天氣系統。因此,本研究嘗試利用具有高時間解析度之地球同步衛星,作為分析工具。
本研究以台灣和南海為研究目標,並特別針對夏季時段,乃因該區域坐落於東印度洋暖池中心及亞洲季風之水氣通道,具有多樣性地貌及複雜的天氣氣候尺度。透過搭載於向日葵八號衛星之儀器-AHI於2017年至2019年夏季(6月至8月)白天(00UTC至08UTC)反演之雲產品,包含雲出現頻率(COF)、雲頂氣壓(CTP)、雲光學厚度(COF)和雲滴有效粒徑(RE),以及EAR5再分析資料提供的相對濕度(RH)、垂直速度(VV)、空氣溫度(T)及對流可用未能(CAPE)之大氣變量估計值,探討兩者之間的時空分布狀態。研究結果顯示,台灣及南海的低雲、高雲和深對流雲出現頻率,在03UTC至05UTC達最大值,且同時伴隨著COT的增長、相對濕度增加,以及上升氣流的增強。此外,由於台灣地貌複雜,不同雲型出現頻率也有明顯的區域差異,而南海則沒有明顯的空間變化。
摘要(英) Clouds account for 70% of global coverage, which life cycle and the changes of microphysical properties affect the energy budget and hydrological cycle on Earth. Due to insufficient understanding of the characteristics of clouds and their development process in previous studies, clouds have been the largest uncertainty factor in climate change forecast models so far. Previous studies often used polar-orbiting satellites as the instruments to observe cloud information, but the limitation of temporal resolution cannot provide continuous observation dataset and adequately monitor short-term weather systems. Therefore, this study attempts to use geostationary satellites with high temporal resolution as an analysis instrument. The study areas are Taiwan and South China Sea, which is located in the warm pool center of East Indian Ocean and water vapor path of Asia monsoon, with the various landform and complicated weather and climate scale, especially during summer period. By using the Himawari-8 satellite data and the atmospheric variable estimates provided by ERA5 reanalysis data, to analyze the relationship between cloud occurrence frequency, cloud top pressure, cloud optical thickness, cloud effective radius, and the environmental factors including relative humidity, vertical velocity, air temperature, and convective available potential energy during 2017 to 2019 summer season (June to August), daytime (00UTC to 08UTC). The results show that the occurrence frequency of low cloud, high cloud, and deep convective cloud reached the maximum value during 03 to 05UTC, with increasing COT, RH, and updraft. In addition, due to the complex topography of TW, there are obvious regional differences in frequency and cloud types, while there is no obvious spatial change over SCS.
關鍵字(中) ★ 向日葵8號
★ 雲微物理參數
★ 日變化
★ 海陸差異
關鍵字(英) ★ Himawari-8
★ Cloud Properties
★ Diurnal Variation
★ Land-Sea Difference
論文目次 Chapter 1: Introduction 1
1.1 The Role of the Cloud in the Earth System 1
1.2 The Limitations of Cloud Observation Instruments 2
1.3Motivation 4
1.4Objectives 7
Chapter 2: Data and Methodology 8
2.1 Himawari-8 Satellite Data 8
2.2 AHI Retrieved Cloud Properties 10
2.3 ERA5 Atmospheric Reanalysis Product 10
2.4 Study Area 11
2.5 Temporal Period 12
2.6 Methodology 13
2.6.1 The Method of Satellite and Reanalysis Data Processing 14
2.6.2 Classification Thresholds for Cloud Types 15
2.7 The Condition of Moisture Saturation 17
2.8 The Condition of Updraft 18
2.9 The Definition of Cloud Microphysical Parameters 21
2.9.1 Cloud Top Pressure (CTP) 21
2.9.2 Cloud Optical Thickness (COT) 22
2.9.3 Cloud Droplet Effective Radius (RE) 23
2.10 The Definition of Environmental Parameters 24
2.10.1 Relative Humidity (RH) 24
2.10.2 Temperature (T) 25
2.10.3 Vertical Velocity (VV) 26
2.10.4 Convective Available Potential Energy (CAPE) 27
Chapter 3: Results and Discussion 28
3.1 The Spatial Distribution of Cloud Products and Environmental Factors 28
3.1.1 Summer Mean of Cloud Product Retrieved from MODIS/Terra and Aqua 28
3.1.2 Summer Mean of Cloud Products Retrieved from AHI/Himawari-8 30
3.1.3 Comparison between AHI and MODIS 33
3.1.4 The Spatial Distribution of Environmental Factors 35
3.2 Joint Histogram of CTP with COT and CTP with RE 38
3.3 The Ratio of Different Cloud Phases in the Pure Cloud Pixels 39
3.4 The Characteristics of Ice Cloud and Water Cloud 40
3.5 The Daytime Variation of Cloud Characteristics 43
3.6 The Daytime Variation of Cloud Information 47
3.7 The Daytime Variation of Environment Factors 49
3.8 The Relationship between Daytime Variation of Clouds and Environmental Factors 53
3.8.1 The Relationship between Daytime Variation of Cloud Occurrence Frequency and Environmental Parameters over SCS 53
3.8.2 The Relationship between Daytime Variation of Cloud Occurrence Frequency and Environmental Factors over TW 56
3.8 The Time Maps of Different Cloud Types Occurrence Frequency 60
Chapter 4: Conclusion and Future Work 62
Reference 66

Figure 1. 1 | FAR values for AHI’s and MODIS’s cloud masks 5
Figure 1. 2 | Spatial distribution of the four typical study areas: study area. 6
Figure 2. 1 | The true color composite image of AHI observation 9
Figure 2. 2 | Study area 12
Figure 2. 3 | Flow Chart 13
Figure 2. 4 | The method of daily data processing 14
Figure 2. 5 | The method of Monthly data processing 15
Figure 2. 6 | ISCCP cloud classifications. 16
Figure 2. 7 | The dew point temperature of cloud. 17
Figure 2. 8 | The cooling effect of temperature with height change. 18
Figure 2. 9 | Surface heating 19
Figure 2. 10 | Topography effect 19
Figure 2. 11 | Front 20
Figure 2. 12 | Convergence 20
Figure 2. 13 | Sample plot of cloud top pressure at 04 UTC on July 1, 2018 21
Figure 2. 14 | Sample plot of cloud optical thickness at 04 UTC on July 1, 2018 22
Figure 2. 15 | Sample plot of cloud effective radius at 04 UTC on July 1, 2018 23
Figure 2. 16 | Sample plot of 850hPa relative humidity at 04 UTC on July 1, 2018 24
Figure 2. 17 | Sample plot of 850hPa air temperature at 04 UTC on July 1, 2018 25
Figure 2. 18 | Sample plot of 850hPa Vertical velocity at 04 UTC on July 1, 2018 26
Figure 2. 19 | Sample plot of convective available potential energy at 04 UTC on July 1, 2018 27
Figure 3. 1 | Seasonal mean daytime cloud top pressure from Terra and Aqua 29
Figure 3. 2 | Mean values of cloud priducts. 32
Figure 3. 3 | Comparison of Himawari-8 and MODIS/Aqua retrieved cloud products 34
Figure 3. 4 | The mean values of environmental factors 37
Figure 3. 5 | Occurrence Frequency of COT and RE in different CTP over TW 39
Figure 3. 6 | The ratio of cloud phases during summer season over TW and SCS 40
Figure 3. 7 | CTP, COT, and RE occurrence frequency of ice cloud and water cloud. 42
Figure 3. 8 | The daytime variation of four cloud type occurrence frequency 46
Figure 3. 9 | The daytime variation of CTP, COT, and RE of four cloud types 49
Figure 3. 10 | The value of the daytime variation of environmental factors. 52
Figure 3. 11 | Daytime variation of normalized occurrence frequency of four cloud types and environmental parameters in different layers over SCS. 56
Figure 3. 12 | Daytime variations of normalized occurrence frequency of four cloud types and environmental factors over TW. 58
Figure 3. 13 | The time series of maximum values of cloud occurrence frequency. 61
Figure 3. 14 | The map of terrain over East Asia 61

Table 2. 1 | The specifications of Himawari-8 / AHI 9
Table 2. 2 | The information of EAR5 11
Table 3. 1 | The value of cloud characteristeice of ice and water cloud 41
Table 3. 2 | Correlation coefficient between the daytime variation of RH, T and VV and cloud over SCS 53
Table 3. 3 | Correlation coefficient between the daytime variation of CAPE and cloud over SCS 53
Table 3. 4 | Correlation coefficient between the daytime variation of of RH, T and VV and cloud over TW 56
Table 3. 5 | Correlation coefficient between the daytime variation of CAPE and cloud over TW 56
Table 3. 6 | The relationship between clouds and environmental factors 59
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指導教授 劉千義(Chian-Yi Liu) 審核日期 2021-7-28
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