請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95191完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林博雄 | zh_TW |
| dc.contributor.advisor | Po-Hsiung Lin | en |
| dc.contributor.author | 鐘晨瑋 | zh_TW |
| dc.contributor.author | Chen-Wei Chung | en |
| dc.date.accessioned | 2024-08-29T16:31:51Z | - |
| dc.date.available | 2024-08-30 | - |
| dc.date.copyright | 2024-08-29 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-09 | - |
| dc.identifier.citation | CAA. (2014). The Advanced Operational Aviation Weather System(AOAWS) Meteorological Products Manual.
Chan, P. W. (2010). LIDAR-based turbulence intensity calculation using glide-path scans of the Doppler LIght Detection And Ranging (LIDAR) systems at the Hong Kong International Airport and comparison with flight data and a turbulence alerting system. Meteorologische Zeitschrift, 19(6), 549-563. doi:10.1127/0941-2948/2010/0471 Cornman, L. B., Morse, C. S., & Cunning, G. (1995). Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements. Journal of Aircraft, 32(1), 171-177. doi:10.2514/3.46697 FAA. (2024). Aeronautical Information Manual. Retrieved from https://www.faa.gov/air_traffic/publications/atpubs/aim_html/ Huang, R., Sun, H., Wu, C., Wang, C., & Lu, B. (2019). Estimating Eddy Dissipation Rate with QAR Flight Big Data. Applied Sciences, 9(23). doi:10.3390/app9235192 ICAO. (2007). ICAO Annex 10: Aeronautical Telecommunications Volume III - Communication Systems (2nd Edition, 2007 ed. Vol. ICAO Annex 10 Volume III). ICAO. (2017). AVIATION OCCURRENCE CATEGORIES - DEFINITIONS AND USAGE NOTES. International Civil Aviation Organization ICAO. (2018). Meteorological service for international air navigation: Annex 3 to the Convention on International Civil Aviation (Twentieth edition, 2018 ed.): International Civil Aviation Organization. Kim, S. H., Chun, H. Y., Kim, J. H., Sharman, R. D., & Strahan, M. (2020). Retrieval of eddy dissipation rate from derived equivalent vertical gust included in Aircraft Meteorological Data Relay (AMDAR). Atmos. Meas. Tech., 13(3), 1373-1385. doi:10.5194/amt-13-1373-2020 Ko, H. C., Chun, H. Y., Wilson, R., & Geller, M. A. (2019). Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States. Journal of Geophysical Research: Atmospheres, 124(14), 7553-7579. doi:10.1029/2019jd030287 Kolmogorov, A. N. (1991). The Local Structure of Turbulence in Incompressible Viscous Fluid for Very Large Reynolds Numbers. Proceedings: Mathematical and Physical Sciences, 434(1890), 9-13. Retrieved from http://www.jstor.org/stable/51980 Kopeć, J. M., Kwiatkowski, K., de Haan, S., & Malinowski, S. P. (2016). Retrieving atmospheric turbulence information from regular commercial aircraft using Mode-S and ADS-B. Atmospheric Measurement Techniques, 9(5), 2253-2265. doi:10.5194/amt-9-2253-2016 Lenschow, D. H. (1972). The measurement of air velocity and temperature using the NCAR Buffalo Aircraft Measuring System (No. NCAR/TN-74+EDD). University Corporation for Atmospheric Research. doi:10.5065/D6C8277W Meymaris, G., Sharman, R., Cornman, L. B., & Deierling, W. (2019). The NCAR In Situ Turbulence Detection Algorithm(No. NCAR/TN-560+EDD ed.). doi:10.5065/g24q-ea27 NTSB. (2021). Preventing Turbulence-Related Injuries in Air Carrier Operations Conducted Under Title 14 Code of Federal Regulations Part 121. Safety Research Report, NTSB/SS-21/01. Retrieved from https://www.ntsb.gov/safety/safety-studies/Documents/SS2101.pdf Schwartz, B. (1996). The Quantitative Use of PIREPs in Developing Aviation Weather Guidance Products. Weather and Forecasting, 11(3), 372-384. doi:https://doi.org/10.1175/1520-0434(1996)011<0372:TQUOPI>2.0.CO;2 Sharman, R. D., Cornman, L. B., Meymaris, G., Pearson, J., & Farrar, T. (2014). Description and Derived Climatologies of Automated In Situ Eddy-Dissipation-Rate Reports of Atmospheric Turbulence. Journal of Applied Meteorology and Climatology, 53(6), 1416-1432. doi:10.1175/jamc-d-13-0329.1 Staufenbiel, R. W., & Schlichting, U. J. (1988). Stability of airplanes in ground effect. Journal of Aircraft, 25(4), 289-294. doi:10.2514/3.45562 Sun, J. (2021). The 1090 Megahertz Riddle: A Guide to Decoding Mode S and ADS-B Signals(2 ed.). doi:10.34641/mg.11 Thorpe, S. A. (1997). Turbulence and mixing in a Scottish Loch. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 286(1334), 125-181. doi:10.1098/rsta.1977.0112 WMO. (2003). Aircraft Meteorological Data Relay (AMDAR) Reference Manual. Retrieved from https://library.wmo.int/viewer/32136?medianame=wmo_958_en_ 國家運輸安全調查委員會. (2019). 台灣飛安統計2003-2012. Retrieved from https://www.ttsb.gov.tw/media/1134/statistics03-12.pdf 國家運輸安全調查委員會. (2023). 台灣飛安統計2013-2022. Retrieved from https://www.ttsb.gov.tw/media/7412/台灣飛安統計報告2013-2022.pdf | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95191 | - |
| dc.description.abstract | 亂流一直都是影響航空安全的重點因素之一。過去我們仰賴飛行員報告(PIREPs)來記錄航行過程中所遭遇的亂流事件時間、位置與強度。隨著時間的演進,這樣的觀測方法對於目前的數值模式預報來說,在時空的精確度與強度的定量上略顯不足。因此一個描述大氣亂流的定量指標——渦流消散速率(Eddy Dissipation Rate,EDR)被國際民用航空組織(International Civil Aviation Organization,ICAO)提出。這個指標具有和機型無關的特性,因此適合做為一個定量的標準數值來將大氣中的亂流進行量化。不過部分現有之EDR演算法所需要的變數,必須透過航空公司從飛行電腦當中下載,因此對於資料的取得不易。
本研究旨在開發一套低成本且易攜帶的現地機載亂流觀測盒(In-Situ Airborne Turbulence Box,ISATB),透過重新組裝無人機模組,並結合公開的ADS-B/Mode-S資料,本研究實現了以易取得的方法計算航路上之EDR數值。該觀測儀器已被我們攜帶搭乘不同航班,包括國內低高度航班與國際越洋航線,並成功通過安檢,且觀測儀器自帶記憶卡,僅需額外接上行動電源即可實時紀錄航空器擾動的定量資料。 經過20多次航班個案,本研究成功利用自製觀測儀器對亂流事件進行量測,並與觀測人員的感受紀錄進行比對,證實了觀測儀器的準確性和可靠性。觀測結果顯示,觀測儀器對不同強度的亂流事件有著良好的反應,能夠準確記錄亂流事件發生的時間和地點,與人員觀測結果高度一致。此外,本文針對三種不同天氣型態,共五個個案討論的綜合比對,將觀測儀器與臺灣民用航空局臺北航空氣象中心現有之美國國家大氣研究中心(National Center for Atmospheric Research,NCAR)亂流數值預報產品進行比對,ISATB對不同強度的亂流事件有著一定的分辨能力,能夠區分不同強度的亂流,進一步豐富了航空亂流觀測的資料庫。 | zh_TW |
| dc.description.abstract | Turbulence has been a critical factor affecting aviation safety. Traditionally, Pilot Reports (PIREPs) have been used to record the time, location, and intensity of turbulence encounters during flights. However, as numerical forecasting models have advanced, this method has shown limitations in spatial and temporal precision and in accurately quantifying turbulence intensity. To address these shortcomings, the Eddy Dissipation Rate (EDR) has been introduced as a quantitative indicator for describing atmospheric turbulence by International Civil Aviation Organization (ICAO). EDR is independent of aircraft type, making it suitable as a standardized metric for quantifying turbulence in the atmosphere. Nonetheless, some existing EDR algorithms require variables that can only be obtained through data downloads from aircraft flight computers, which restricts data accessibility.
This study aims to develop a low-cost and portable in-situ airborne turbulence box (ISATB) by reassembling drone modules and integrating public ADS-B/Mode-S data. By utilizing this method, the study successfully calculated EDR values along flight routes using readily accessible means. The observational instrument has been carried on various domestic and international flights. It has successfully passed security checks and is equipped with a memory card, requiring only an external power bank to aircraft turbulence data in real time. Through more than 20 flight cases, the study effectively measured turbulence events using the self-designed observational instrument. Comparisons with observers' subjective records verified the instrument's accuracy and reliability. The results indicate that the instrument responds well to turbulence events of varying intensities, accurately recording the time and location of such events, which were highly consistent with human observations. Furthermore, based on the comprehensive comparison of three different weather patterns and a total of five cases, the observation instruments were compared with the existing turbulence numerical forecast products from the National Center for Atmospheric Research (NCAR) provided by the Taipei Aeronautical Meteorological Center of the Civil Aviation Administration of Taiwan. This capability enhances the existing turbulence observation database and improves our understanding of turbulence prediction. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-29T16:31:51Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-29T16:31:51Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv 目 次 vi 圖 次 vii 表 次 ix 第一章 緒論 1 第二章 文獻回顧 4 2.1 EDR演算法 4 2.2 EDR量測方法 7 2.3 亂流強度定義 8 第三章 研究方法 10 3.1 研究資料來源 10 3.2 資料處理流程 16 3.3 EDR演算法 18 3.4 測試實驗 19 第四章 個案討論 21 4.1 鋒面系統 21 4.2 東部外海對流系統 25 4.3 臺灣海峽東北季風 32 第五章 結論 38 參考文獻 40 附錄一 自製觀測儀器使用晶片規格 42 附錄二 觀測航班資料 44 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 航空氣象 | zh_TW |
| dc.subject | 亂流觀測 | zh_TW |
| dc.subject | 渦流消散速率 | zh_TW |
| dc.subject | NCAR | zh_TW |
| dc.subject | 無人機 | zh_TW |
| dc.subject | NCAR | en |
| dc.subject | Aviation Meteorology | en |
| dc.subject | Turbulence Observation | en |
| dc.subject | UAV | en |
| dc.subject | EDR | en |
| dc.title | 臺北飛航情報區現地空載亂流觀測 | zh_TW |
| dc.title | In Situ Airborne Turbulence Observation in Taipei FIR | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蘇世顥;官文霖 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Hao Su;Wen-Lin Guan | en |
| dc.subject.keyword | 亂流觀測,航空氣象,NCAR,渦流消散速率,無人機, | zh_TW |
| dc.subject.keyword | Turbulence Observation,Aviation Meteorology,NCAR,EDR,UAV, | en |
| dc.relation.page | 58 | - |
| dc.identifier.doi | 10.6342/NTU202401186 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-07-09 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 大氣科學系 | - |
| 顯示於系所單位: | 大氣科學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 8.57 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
