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姓名	蔡沛蓉(Pei-Jung Tsai)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	(Extreme Heavy Rainfall Event on 01-02 June 2017 over Northern Taiwan Area: Analysis of Radar Observation and Ensemble Simulations)
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摘要(中)	本研究主要探討2017年6月1日至02日台灣北部梅雨鋒面個案。此鋒面於清晨滯留於台灣北部並在8小時內降下超過300毫米之累積雨量，本研究透過觀測資訊和數值模擬結果進而探討此極端降雨事件的主要肇始原因，依雷達資料和紅外線色調強化衛星圖，可將鋒面系統按生命期分為三個階段，分別為南移階段、MCS合併階段與MCS重建階段，由多都卜勒風場合成系統(Wind Synthesis System using Doppler Measurements, WISSDOM) 反演得水平解析為1 km及高度解析為0.5 km之三維風場，除作為鋒面移動動力及結構探討的工具外，後續也作為與模式模擬比較時的動力參考及真實場。為了了解鋒面強降雨事件形成之條件及特徵，本研究利用美國國家海洋和大氣管理局之全球預報模式 (National Centers for Environmental Prediction, NCEP) 提供之全球最終再分析場資料 (Final operational global analysis, FNL) 進而擾動、積分取得 128 支系集模擬成員，並透過 K-means 群集分析設8小時內累積降水100 毫米為閾值，最終可將128 支系集分為五群，分布於台灣北部外海、北部沿海以及內陸地區，藉由反演風場及群集分析結果探討此個案中影響鋒面滯留、至災主要之中尺度-α和中尺度-β關鍵動力因素，結果顯示地形噴流對於極端降水為重要的前導因素，近一步利用模式環境場檢視發現槽與雨帶間存在著一定的相關性，隨著北方短波槽東移接近台灣，台灣西側氣壓梯度力增加，進而影響西南季風之增強，而在風場與地形交互作用下形成更強之噴流，並控制著鋒面強度及雨帶位置，使得短波槽移出之時間間接影響強降雨位置。總結來說，利用觀測及反演場除了能分析梅雨動力結構，更可進一步檢視模式模擬的表現，而在系集綜觀環境場的輔助下，有助於了解鋒面表現及其不同尺度之動力特徵。
摘要(英)	During 01-02 June 2017, a Mei-Yu front with accumulated rainfall over 550 mm in 8 hours occurred and stagnated at northern Taiwan in the early morning. The study examined the main features of extreme rainfall event through both observational data and numerical model simulations. Based on the observations of radar data and infrared satellite images, the lifetime of the frontal system is divided into three stages when investigating the characteristic of this event, and they are southward moving stage, MCS merging stage, and back-building stage. Three-dimensional wind fields are retrieved at 1-km horizontal resolution by the Wind Synthesis System using Doppler Measurements (WISSDOM) through two radar sites, and the synthetic winds are used as reference when comparing to model simulations. To fully comprehend the characteristics caused this event, the 128-member ensemble simulations, which were obtained and perturbed from National Centers for Environmental Prediction (NCEP) Final operational global analysis (FNL), are generated. The K-means clustering analysis is applied to classify the 128-ensemble into five groups by a threshold of 100 mm/8-hr. These five clusters illustrated different locations of extreme rainfall: some were over the ocean, and some were inland near west coast or northeast of Taiwan. Through retrieved wind field and cluster analysis, the dynamic features in both meso-α and meso-β scales are discussed to identify the key factors that can make the front stagnate and produce such heavy rainfall in northern Taiwan. The result shows that the barrier jet stands as a significant lead in the extreme rainfall process. Further inspection of large-scale simulations reveals a connection between the trough and the spatial distribution of rainbands. When the short-wave trough at north gets close to Taiwan, the intensification of pressure gradient force at west would strengthen the southwesterly flow and result in a stronger jet which could control the strength and position of the frontal system. The movement of the trough would affect the rainband position indirectly. In conclusion, the observations not only can help us analyze the dynamic structure of Mei-Yu front but also to inspect the performance of model simulations. Further, the overview of the Mei-Yu process in different scales is revealed through the clustered ensemble simulations.
關鍵字(中)	★ 極端強降水 ★ K-means分群 ★ 系集模擬 ★ 雷達觀測	關鍵字(英)	★ extreme heavy rainfall ★ K-means clustering ★ ensemble simulation ★ radar observations
論文目次	摘要 i Abstract ii Acknowledgment iv Chapter 1 Introduction 1 Chapter 2 Data and Methodology 7 2.1 Data 7 2.2 Wind Synthesis System using Doppler Measurements (WISSDOM) 9 2.3 Model configuration 13 2.4 Cluster analysis 15 Chapter 3 Case overview 18 3.1 2017/06/01-02 Mei-Yu case 18 Chapter 4 Result: Part I -WISSDOM 24 4.1 Retrieval of 2017 Mei-Yu case 24 Chapter 5 Result: Part II – Cluster analysis 39 5.1 Performance of ensemble simulations 39 5.2 Dynamic characteristic of clustering result 59 Chapter 6 Summary and future works 75 6.1 Summary 75 6.2 Future works 78 References 82
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指導教授	鍾高陞 廖宇慶(Kao-Shen Chung Yu-Chieng Liou)	審核日期	2022-8-12
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