利用衛星估計西北太平洋及南海颱風季節之垂直溫度結構

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：50

、訪客IP：18.118.144.239

姓名

李杰穎(Jie-Ying Li) 查詢紙本館藏

畢業系所

水文與海洋科學研究所

論文名稱

利用衛星估計西北太平洋及南海颱風季節之垂直溫度結構
(Satellite-derived ocean thermal structure for Western North Pacific Ocean and South China Sea typhoon season with 18 years of Argo floats data)

相關論文

★ 1980-2018年期間西北太平洋颱風大小變化之研究	★ 二月份最強颱風：Wutip (2019) 之大氣與海洋條件
★ 颱風 Bavi (2020) 在東中國海淺海異常增強	★ 1999~2018年期間南海沿岸淺水區對颱風登陸時強度變化之影響
★ 利用數值模式探討近岸海表面溫度對登陸颱風強度之影響	★ 利用耦合模式探討颱風Bavi (2020)在東海的增強過程
★ 應用鍶-釹-鉛同位素探討大氣與水體環境中金屬來源與傳輸過程之研究	★ 探究環境變化對原核生物群落生態系統功能的影響

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 ( 永不開放)

摘要(中)

海洋是個非常巨大的能源儲存庫，對熱帶氣旋而言海洋提供能量的多寡將決定其強度的變化趨勢。為了能夠預測熱帶氣旋的強度，除了海表溫度之外還需要上層海洋的溫度結構來判斷海洋熱含量的多寡。目前的衛星觀測技術能夠快速地測量到海表溫度，但海洋的溫度結構卻無法如海表溫度一般快速地獲取，增加獲取海洋溫度結構的方法是當今重要的課題。
　　本研究使用Argo Floats與衛星數據在西北太平洋和南海建立回歸模型，利用海表面高度距平(Sea Surface High Anomaly，SSHA)來估計海面下的垂直溫度結構。本研究建立了三組不同的回歸模型，分別為：(1) 使用2000~2008年颱風季節(5月~10月)的Argo Floats建立回歸模型(簡稱Pun2014)；(2) 使用2000~2017年颱風季節的Argo Floats建立回歸模型(簡稱Y18_FW)；(3) 改善Y18_FW搜尋範圍解析度的回歸模型(簡稱Y18_DY)。探討海洋資料量及搜尋範圍對回歸模型的影響，並評估三組回歸模型估計海洋溫度結構的表現。
　　西北太平洋的驗證裡中，三個回歸模型表現都非常良好而且估計的溫度結構也都非常相似。而在不同緯度的驗證中我們發現，模型的表現會因為Argo Floats在各緯度的數量及分佈產生差異。原本Argo Floats較少的低緯度地區(0~10°N)會因為數據量的提高而有著明顯的改善，緯度較高的地區(25~40°N)則是受到模型的搜尋範圍解析度影響。緯度越高Argo Floats的數量就越多，解析度較低的Y18_FW增加了5~10%的均方根差，Y18_DY則能有效地降低此誤差。
　　本研究進一步在南海建立Y18_DY回歸模型，探討模型在南海的適用性。研究結果顯示，回歸模型估計的海洋熱含量及T100 (上層100米的平均海水溫度)與Argo Floats都有著很高的相關性，其相關係數也都高於0.8。與特定Argo Floats進行長時間的溫度剖面比較，衛星反演的溫度結構與Argo Floats的溫度剖面相當一致，其趨勢變化都能清楚地顯示。說明有了回歸模型之後，我們能夠利用SSHA有效地估計出海洋的溫度結構。

摘要(英)

Ocean is a very important source of energy for tropical cyclones, the amount of energy provided by the ocean will determine the trend of its intensity. In order to be able to predict the intensity of tropical cyclones, the temperature structure of the upper ocean is needed to determine the amount of ocean heat content. With the current technology, we cannot quickly obtain the ocean thermal structure, so increasing the method of obtaining the ocean thermal structure is an important topic today.
　　In this study, we have established three regression models: (1) Use Argo Floats from 2000 to 2008 (19,915 Argo Floats) to build the regression model, called Pun2014, (2) Use Argo Floats from 2000 to 2017 (87,580 Argo Floats) to build the regression model, called Y18_FW, (3) Use the dynamic window method to improve the spatial resolution of Y18_FW, called Y18_DY. Then use 6502 independent Argo Floats in 2018 to verify the three regression models.
　　In the verification of the Northwest Pacific, the three regression models performed very well in estimating the temperature profile, and their results were also very similar. In the verification of different latitudes, we found that the performance of the model will be different due to the number and distribution of Argo Floats in each latitude. The low latitude areas that originally had fewer Argo Floats will be significantly improved due to the increase in data volume, and the higher latitude areas will be affected by the spatial resolution. When the latitude is higher, the number of Argo Floats increases. Y18_FW with lower spatial resolution increases the root mean square error by 5-10%, and Y18_DY can effectively reduce this error.
　　We also established Y18_DY in the South China Sea and discussed the applicability of the model in the South China Sea. The research results show that the estimated thermal structure has a high correlation with Argo Floats, and the correlation coefficients of ocean heat content and T100 (average sea temperature of the upper 100 meters) are also higher than 0.8. The long-term temperature profile comparison with specific Argo Floats shows that Y18_DY can estimate the underwater temperature well, and the temperature change trend observed by Argo Floats can be effectively estimated. It shows that with the regression model, we can use SSHA to effectively estimate the ocean thermal structure.

關鍵字(中)

★ 衛星測高
★ 海洋垂直溫度結構

關鍵字(英)

論文目次

摘要 i
Abstract i
致謝 iii
目錄 iv
表目錄 vii
圖目錄 vii
第一章緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.2.1 海洋溫度結構的重要性 2
1.2.2 海洋溫度結構的觀測方法 3
1.2.3 海洋特徵的重要性 4
1.2.4 兩層模式 6
1.2.5 透過線性回歸結合SSHA與觀測數據 8
1.3 研究動機 10
1.4 論文架構 11
第二章資料 12
2.1 浮標 12
2.1.1 Argo Float 12
2.1.2 CTD、XBT 14
2.2 衛星 14
2.2.1 SSHA 14
3.2.2 SST 16
2.3 Monthly climatology 16
2.3.1 WOA18 16
2.3.2 混合層深度 17
第三章研究方法 19
3.1 線性回歸方程 19
3.1.1 Pun2014 19
3.1.2 Y18_FW(18 years of Argo data with fixed search window) 21
3.1.3 Y18_DY (18 years of Argo data with dynamic search window) 21
3.2 SSHA與等溫線深度之間的關係 22
3.3 回歸模型估計溫度剖面 24
3.3.1 特殊情況的處理 24
第四章敏感度測試 26
第五章研究結果 27
5.1 數據量與搜尋範圍解析度對回歸模型的影響 27
5.2 西北太平洋的驗證 29
5.3 不同緯度的驗證 31
5.4 南海 32
5.4.1 SSHA與等溫線深度之間的關係 33
5.4.2 回歸模型的表現 34
5.4.3 與Argo Floats的長時間比較 35
第六章結論與未來工作 37
6.1 結論 37
6.2 未來工作 39
參考文獻 42
附表 48
附圖 55

參考文獻

Bender, M. A., and Ginis I, (2000) : Real-Case Simulations of Hurricane–Ocean Interaction Using A High-Resolution Coupled Model: Effects on Hurricane Intensity. Mon. Wea. Rev., 128, 917–946.
Brewster, J. K. and L. K. Shay (2010) : Oceanic Heat Content Variability in the Eastern Pacific Ocean for Hurricane Intensity Forecasting. Monthly Weather Review 138(6): 2110-2131.
B. Keeley, C. Sun, and L. P. Villeon, (2003) : Global temperature and salinity profile program annual report, NOAA/National Oceanographic Data Center, Silver Spring, MD, 2003.
Chang, S. W., and R. A. Anthes, (1978) : Numerical Simulations of the Ocean′s Nonlinear, Baroclinic Response to Translating hurricanes. J. Phys. Oceanogr., 8, 468–480.
Calafat FM, Chambers DP (2013) Quantifying recent acceleration in sea level unrelated to internal climate variability. Geophys Res Lett 40(14):3661–3666.
Chambers DP, Mehlhaff CA, Urban TJ, Fujii D, Nerem RS (2002) Low-frequency variations in global mean sea level: 1950–2000. J Geophys Res Oceans (1978–2012) 107(C4):1–1.
Dennis A. Mayer, Robert L. Molinari, Molly O′Neil Baringer, Gustavo J. Goni, (2001) : Transition regions and their role in the relationship between sea surface height and subsurface temperature structure in the Atlantic Ocean. Geophysical Research Letters 28(20): 3943-3946.
Dangendorf S, Rybski D, Mudersbach C, Muller A, Kaufmann E, Zorita E, Jensen J (2014b) Evidence for long-term memory in sea level. Geophys Res Lett.
Emanuel, K. (1999) : Thermodynamic control of hurricane intensity. Nature 401, 665–669
Fu, L. L., & Cazenave, A. (Eds.). (2000). Satellite altimetry and earth sciences: a handbook of techniques and applications. Elsevier.
Gill, A. E., & Niller, P. P. (1973, February). The theory of the seasonal variability in the ocean. In Deep Sea Research and Oceanographic Abstracts (Vol. 20, No. 2, pp. 141-177). Elsevier.
Greatbatch, R. J. (1983). On the Response of the Ocean to a Moving Storm: The Nonlinear Dynamics, Journal of Physical Oceanography, 13(3), 357-367.
Goñi, G., Kamholz, S., Garzoli, S., & Olson, D. (1996) : Dynamics of the Brazil‐Malvinas Confluence based on inverted echo sounders and altimetry. Journal of Geophysical Research, 101, 16273-16289.
──, & Triñanes, J. (2003) : Ocean thermal structure monitoring could aid in the intensity forecast of tropical cyclones. Eos, Transactions American Geophysical Union, 84, 573-578.
──, M. DeMaria, J. Knaff, C. Sampson, I. Ginis, F. Bringas, A. Mavume, C. Lauer, I.-I. Lin, M.M. Ali, P. Sandery, S. Ramos-Buarque, K. Kang, A. Mehra, E. Chassignet, and G. Halliwell. (2009) : Applications of satellite-derived ocean measurements to tropical cyclone intensity forecasting. Oceanography 22(3):190–197
Gould, J., Roemmich, D., Wijffels, S., Freeland, H.J., Ignaszewsky, M., Jian-ping, X., Pouliquen, S., Desaubies, Y., Send, U., Radhakrishnan, K., Takeuchi, K., Kim, K., Danchenkov, M., Sutton, P., King, B., Owens, B., & Riser, S. (2004) : Argo profiling floats bring new era of in situ ocean observations. Eos, Transactions American Geophysical Union, 85, 185-191.
Guinehut, S., P.Y.Le Traon, G.Larnicol, S. Philipps, (2004) : Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations. Journal of Marine Systems 46(1-4): 85-98.
──, Dhomps, A.-L., Larnicol, G., and Le Traon, P.-Y., (2012) : High resolution 3-D temperature and salinity fields derived from in situ and satellite observations, Ocean Sci., 8, 845–857.
hay, L. K., G. J. Goni, and P. G. Black, (2000) : Effects of a Warm Oceanic Feature on Hurricane Opal. Mon. Wea. Rev., 128, 1366–1383
Hu, J., & Wang, X. (2016) : Progress on upwelling studies in the China seas. Reviews of Geophysics, 54, 653-673.
Jacob, S. D., L. K. Shay, A. J. Mariano, and P. G. Black, 2000: The 3-D oceanic mixed layer response to hurricane Gilbert. J. Phys. Oceanogr., 30, 1407-1429.
Kara, A.B., Rochford, P.A., Hurlburt, H.E., (2002) : Naval Research Laboratory Mixed Layer Depth (NMLD) Climatologies. NRL, Report No. NRL/FR/7330-02-9995, 26.
Katsman CA, Hazeleger W, Drijfhout SS, van Oldenborgh GJ, Burgers G (2008) Climate scenarios of sea level rise for the northeast Atlantic Ocean: a study including the effects of ocean dynamics and gravity changes induced by ice melt. Clim Change 91:351–374.
Klemas, V., & Yan, X. H. (2014). Subsurface and deeper ocean remote sensing from satellites: An overview and new results. Progress in oceanography, 122, 1-9.
Leipper, D. F., and D. Volgenau, (1972) : Hurricane Heat Potential of the Gulf of Mexico. J. Phys. Oceanogr., 2, 218–224
Lin, I. I. (2003) : Satellite observations of modulation of surface winds by typhoon-induced upper ocean cooling. Geophysical Research Letters 30(3).
──, C. Wu, K. A. Emanuel, I. Lee, C. Wu, and I. Pun, (2005) : The Interaction of Supertyphoon Maemi (2003) with a Warm Ocean Eddy. Mon. Wea. Rev., 133, 2635–2649
──, Liu, W. Timothy; Wu, Chun-Chieh; Wong, George T. F.; Hu, Chuanmin; Chen, Zhiqiang; Liang, Wen-Der; Yang, Yih; and Liu, Kon-Kee, (2003) : New Evidence for Enhanced Ocean Primary Production Triggered by Tropical Cyclone. OEAS Faculty Publications. 324.
──, Chun-Chieh Wu, I. F. Pun, and D. S. Ko, (2008): Upper-ocean thermal structure and the western North Pacific category-5 typhoons. Part I: Ocean features and the category-5 typhoons’ intensification. Mon. Wea. Rev., 136, 3288–3306,
──, Hsu, H. -ming, Sheng, Y. -F., Kuo, C. -H., & Liou, Y. -A, (2011) : Mesoscale processes for super heavy rainfall of Typhoon Morakot (2009) over Southern Taiwan. Atmospheric Chemistry And Physics, 11, 345-361.
Meyers, P. C., L. K. Shay, and J. K. Brewster, (2014) : Development and Analysis of the Systematically Merged Atlantic Regional Temperature and Salinity Climatology for Oceanic Heat Content Estimates. J. Atmos. Oceanic Technol., 31, 131–149
Ndah, A., Becek, K., & Dagar, L. (2017) : A Review of Coastal Upwelling Research in the South China Sea : Challenges , Limitations and Prospects.
Palmen EN. (1948). On the formation and structure of the tropical hurricane. Geophysica 3: 26–38.
Price, J. F., (1981) : Upper Ocean Response to a Hurricane. J. Phys. Oceanogr., 11, 153–175
Pun, I.F., I. Lin, C. Wu, D. Ko and W. T. Liu, (2007) : Validation and Application of Altimetry-Derived Upper Ocean Thermal Structure in the Western North Pacific Ocean for Typhoon-Intensity Forecast, in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 6, pp. 1616-1630, June 2007
──, Y.-T. Chang, I.-I. Lin, T.Y. Tang, and R.-C. Lien. (2011) : Typhoon-ocean interaction in the western North Pacific: Part 2. Oceanography 24(4):32–41.
──, Lin, I., & Ko, D.S., (2014) : New generation of satellite-derived ocean thermal structure for the western north pacific typhoon intensity forecasting. Progress in Oceanography
──, J. F. Price, and S. R. Jayne, (2016) : Satellite-Derived Ocean Thermal Structure for the North Atlantic Hurricane Season. Mon. Wea. Rev., 144, 877–896
Roemmich, D., Riser, S., Davis, R., Desaubies, Y., (2004) : Autonomous profiling floats: workhorse for broad-scale ocean observations. Marine Technology Society Journal 38, 21–29.
──, & Gilson, J. (2009). The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82, 81-100.
Rappaport, E. N., Jiing, J., Landsea, C. W., Murillo, S. T., & Franklin, J. L. (2012). The Joint Hurricane Test Bed: Its First Decade of Tropical Cyclone Research-To-Operations Activities Reviewed, Bulletin of the American Meteorological Society, 93(3), 371-380.
Shay, L. K., Goni, G. J., & Black, P. G. (2000). Effects of a warm oceanic feature on Hurricane Opal. Monthly Weather Review, 128(5), 1366-1383.
Sturges W, Douglas BC (2011) Wind effects on estimates of sea level rise. J Geophys Res Oceans (1978–2012).
Sanford, T.B., J.F. Price, J.B. Girton. (2011) Upper ocean response to Hurricane Frances (2004) observed by EM-APEX floats. Journal of Physical Oceanography 41:1,041–1,056.
Sterlini, P., de Vries, H., & Katsman, C. (2016). Sea surface height variability in the North East Atlantic from satellite altimetry. Climate Dynamics, 47(3-4), 1285-1302.
Stammer, D., & Cazenave, A. (Eds.). (2017). Satellite altimetry over oceans and land surfaces. CRC Press.
Tseng, Y. H., S. Jan, D. E. Dietrich, I. I. Lin, Y. T. Chang, and T. Y. Tang, (2010) : Modeled oceanic response and sea surface cooling to typhoon Kai-Tak. Terr. Atmos. Ocean. Sci., 21, 85-98.
Willis, J. K. (2003) : Combining altimetric height with broadscale profile data to estimate steric height, heat storage, subsurface temperature, and sea-surface temperature variability. Journal of Geophysical Research 108(C9).
Xie SP, Xie Q, Wang D, Liu W. T, (2003) : Summer upwelling in the South China Sea and its role in regional climate variations. Journal of Geophysical Research: Oceans 108(C8)

指導教授

潘任飛(Iam-Fei Pun)

審核日期

2021-1-20

推文