博碩士論文 106022003 詳細資訊




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姓名 侯勝勛(Sheng-Syun Hou)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 應用多時期向日葵8號衛星影像進行雲像素的偵測與追蹤
(Detection and Track Movement of Cloud Pixels Using Himawari-8 Multiple Temporal Images)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-7-31以後開放)
摘要(中) 雲在大氣環流中扮演著非常重要的角色,不僅表現出天氣變化的特徵,也有助於人們作為天氣預報的指標。雲的類型、相位、雲頂的高度、壓力和溫度等參數可以估計雲的發展與預測降水。為了要分析雲的特性,我們必須觀察雲的外觀隨著時間推移的變化,將雲視為粒子群並追蹤雲的移動方向,從雲的生成至消散進行觀測。
在本篇論文的研究中,我們所關注的重點是位在熱帶地區的中尺度對流系統(Mesoscale Convective Systems, MCSs),此系統是由積雲所組成的對流天氣系統,常見於較弱的氣壓梯度與微風的環境當中,由各個獨立的圓形或橢圓形雷雨胞組成多個群體,而造成豪大雨與強對流的天氣現象,通常易造成惡劣天氣,特別是深對流系統更是造成天氣災害的主要原因。由於中尺度對流系統的生命週期非常短,甚至只維持數十分鐘至數小時間的時間尺度,並且經常發生在海面上,因此,利用衛星觀測彌補地面觀測站無法提供完整空間分布資訊的缺陷,根據衛星進行大面積和連續時間觀測的特性,粒子群演算法(PSO)可觀測雲的生成、成熟和消散,並透過粒子群演算法在時間尺度的範圍內多次觀測,我們可以識別出中尺度對流胞整個生命周期的演變過程,進一步分析隨時間變化的對流雲物理特性。本研究使用向日葵8號(Himawari-8)地球同步觀測衛星的多光譜及高時空解析度數據做實驗,並以2017年6月台灣地區周圍的對流案例做分析。
對於此案例的對流雲生命週期統計參數顯示,將亮度溫度閾值定義在205 K的雲系統中,能清楚得知雲的冷心邊界。當對流雲成熟時,雲所移動方向的前端溫度將迅速下降,可利用此特性的參數變化增加對災害天氣的預報,再利用粒子群演算法的分割與優化追蹤雲粒子的移動,提供做為天氣預報參數的一部份。
摘要(英) Clouds play a very important role in atmospheric circulation. It not only presents the feature of weather changed, but also assisting support weather prediction. Cloud type, cloud phase, cloud-top height, pressure and temperature can provide information to estimate cloud development and precipitation prediction. To analyze the changes of cloud properties, we must track the movement, split and merge of clouds in pixel level. In this study, we focus on Mesoscale Convective Systems (MCSs) in tropical regions, a convective weather system consisting of cumulus convection. It is commonly appear in weak pressure gradients and low wind speed environments as circular or elliptical cluster of thunderstorms combined into multiple thunderstorm monomers. And often causes severe weather such as heavy rain and strong wind on the ground. Especially the deep convection system is the main cause of weather disaster. The life cycle of MCSs are very short and often occurs above the sea. Therefore, by the characteristics of large-area and contiguous observations from satellites, cloud tracking algorithm can be used to determine the initiation, maturity, and dissipation of MCSs. By applying multiple temporal detection with a spread of convective seeds in the spatiotemporal domain, we can identify and characterize the consistency of MCSs and their life cycles. Then, it is possible to analyze convective cloud microphysical properties over time. We utilize multiple channel and high spatiotemporal resolution data from the geostationary satellite of Himawari-8 and focus on the convection of individual cases in Taiwan region in June 2017. Statistical analysis of cloud parameters in their life cycle shows that the cold shield by threshold 205 K in the spatiotemporal domain can detect MCSs. When the convective cloud matures, the brightness temperature will drop rapidly and we can monitor the parameter changes to enhance the disastrous weather capability. Particle Swarm Optimization (PSO) is adopted to track the clouds. Finally, the change of cloud top properties can be further analyzed as part of weather prediction coefficients.
關鍵字(中) ★ 向日葵8號衛星
★ 中尺度對流系統
★ 粒子群演算法
★ 亮度溫度
關鍵字(英) ★ Himawari-8
★ Mesoscale Convective Systems (MCSs)
★ Particle Swarm Optimization (PSO)
★ Brightness Temperature
論文目次 摘要 i
Abstract iii
Contents v
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Framework 5
Chapter 2 Methodology 6
2.1 Literature review 6
2.1.1 Using satellite thermal infrared tracking clouds 6
2.1.2 Bayesian multiple-hypothesis tracking of targets 10
2.1.3 Image segmentation 12
2.2 Method and flow chart 14
2.1.1 Mesoscale convective systems 16
2.2.2 Atmospheric temperature inversion 19
2.2.3 Particle swarm optimization 22
Chapter 3 Research Material 26
3.1 Study area 26
3.2 Data 28
3.3 Research process 33
Chapter 4 Experimental Result 35
4.1 Cloud detection 42
4.1.1 Cloud mask 42
4.1.2 Cloud boundary 45
4.1.3 Binary image 47
4.2 Cloud tracking 51
4.2.1 PSO segmentation 53
4.2.2 Multi-temporal images of MCSs tracking 55
4.3 Verification 64
4.3.1 Global numerical forecast system 64
4.3.2 Ground stations 68
Chapter 5 Conclusion and Future Work 70
5.1 Conclusion 70
5.2 Future works 71
References 72
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CWB Observation Data Inquire System:
[https://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp]

NCEP Global Data Systems (GDAS):
[https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00379]
指導教授 任玄 劉千義(Hsuan Ren Chian-Yi Liu) 審核日期 2020-7-28
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