 |
|
|
|
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：44 、訪客IP：3.144.19.44
姓名 閻雅婷(Ya-Ting Yen) 查詢紙本館藏 畢業系所 大氣物理研究所 論文名稱 FORMOSAT-3 GPS RO資料對東亞地區梅雨天氣系統診斷分析之影響評估 相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
- 本電子論文使用權限為同意立即開放。
- 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
- 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中) 為探討加入FORMOSAT-3 GPS RO資料對東亞地區梅雨天氣系統診斷分析與模擬的影響，本文針對2007年梅雨季6月6-9日的豪大雨個案，透過客觀分析方法與WRF 3DVAR資料同化方法，將FORMOSAT-3 GPS RO觀測資料融入CWB GFS氣象場分析。其中客觀分析使用Gandin在1963年提出的最佳內插法，將FORMOSAT-3 GPS RO反演探空資料融入氣象場後進行診斷分析。WRF 3DVAR方法則是將FORMOSAT-3 GPS RO資料同化至氣象場，求得最佳初始場後，再以WRF模式進行72hr的模擬，分析初始場的差異量及隨時間模擬的變化情形。
客觀分析的結果在陸地上並沒有明顯的差異，顯示FORMOSAT-3 GPS RO資料與無線電探空相當協調，但在海面上卻有相當明顯的差異，最顯著的影響是等壓面的高度場分析中，太平洋副熱帶高壓脊的強度明顯的增強。由於一般氣象中心全球模式之客觀分析，大都以氣候場作為初始猜測場，因此，在海面上造成明顯差異的主要原因，可能是初始猜測的氣候場與2007年太平洋副熱帶高壓脊西伸的強度不符所導致。
經由WRF 3DVAR方法將FORMOSAT-3 GPS RO資料同化至模式的結果，發現初始分析場的差異量與資料的所在位置明顯相關，修正量呈現近似同心圓的分布。而差異量經模式積分後有擴大現象，最大值大多落在梅雨鋒面的強對流位置及太平洋副熱帶高壓脊前緣。模擬結果也顯示加入FORMOSAT-3 GPS RO資料後，WRF模式模擬能夠突顯對流發展的特徵，如中高層對流雲頂溫度的降低及位渦的增強等現象。
透過兩種方法將FORMOSAT-3 GPS RO資料融入氣象場，對各種氣象參數上皆有不同程度的影響，這些差異有些是可以透過其他觀測資料(如衛星雲圖)推斷是正面的，但有些則尚難推斷。因此，以目前FORMOSAT-3 GPS RO資料的分布狀況，對梅雨天氣分析與預報模擬尚難有明確且有系統性的正面提升。目前世界各先進國家也陸續規劃佈署更好的GPS RO觀測衛星，國內也提出了春雷衛星(福衛三號後續)計畫，期望可大幅提高觀測資料的量與品質，對未來整體氣象的發展及天氣分析與預報技術的提升，定會有明顯的貢獻。摘要(英) In order to understand the impact of FORMOSAT-3 GPS RO measurements on the diagnosis and model simulation of Mei-Yu system in the East Asia region, this study investigates a heavy rain case occurring during 6-9 June 2007 by blending FORMOSAT-3 GPS RO data into CWB GFS analyses via objective analysis and WRF 3DVAR methods. The objective analysis method utilized is the optimum interpolation method. For the WRF 3DVAR method, FORMOSAT-3 GPS RO data are assimilated into the CWB GFS fields to get the best initial fields as the first step, then a 72hr simulation is conducted using the WRF model. Differences between analyses with and without FORMOSAT-3 GPS RO data are examined and compared.
Comparison results of objective analyses show no obviously difference over the land. This suggests that the FORMOSAT-3 GPS RO data are highly comparable with the radiosonde observations. However, obvious differences can be found over the ocean with particularly noticeable enhancement of Pacific subtropical high ridge line. In a global model, climatic analysis is usually used as the first guess field during the initial objective analysis procedure. Thus, the differences could be due to the discrepancy of the intensity of Pacific subtropical high ridge between the first guess field and the real atmospheric state of 2007.
After assimilating the FORMOSAT-3 GPS RO data into the model initial fields by the WRF 3DVAR method, results show that the differences of initial fields are highly correlated with the positions of the GPS RO data and the corrections exhibit like concentric circles. The differences will gradually expand during the model integration with the maximum corrections often found in areas of active convection along the Mei-Yu front and the front peripheries of Pacific subtropical high ridge. The results also show that model simulations with FORMOSAT-3 GPS RO data will enhance the characteristics of convective activities such as cloud top cooling and intrusion of high potential-vorticity air in the middle and upper troposphere.
By blending of FORMOSAT-3 GPS RO data into the analysis fields in these two ways, results show different degrees of impact on different meteorological parameters. Some of the impacts are positive and can be verified from other measurements such as satellite images. However, some of them are hard to give precise conclusions. Therefore, with the current data amount and distribution of FORMOSAT-3 GPS RO data, a systematic improvement in the diagnosis and forecast of the Mei-Yu system seems unfeasible at present. Now, many countries are planning on launching more and advanced GPS RO satellites, and in Taiwan we also have a FORMOSAT-3 follow-up mission too. We hope that, in the future, even better quality and denser data will contribute to advanced evolution of meteorology and improvement of weather analysis and forecast.關鍵字(中) ★ WRF模式三維資料同化方法
★ 最佳內插法
★ FORMOSAT-3 GPS RO資料關鍵字(英) ★ WRF 3DVAR
★ Optimum interpolation method
★ FORMOSAT-3 GPS RO data論文目次 中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖說 vii
第一章、前言 1
第二章、資料來源與分析方法 6
2.1資料來源 6
2.1.1中央氣象局全球預報模式(CWB GFS)初始分析場資料 6
2.1.2全球電信系統(GTS)觀測資料 6
2.1.3 FORMOSAT-3 GPS RO資料 6
2.2分析方法 8
2.2.1客觀分析方法 8
2.2.2 WRF 3DVAR方法 9
2.2.3 WRF模式介紹 10
第三章、2007年台灣地區梅雨季豪大雨個案介紹及東亞地區FORMOSAT-3 GPS RO資料校驗 12
3.1 個案描述與實驗設計 12
3.2 東亞地區FORMOSAT-3 GPS RO資料的校驗統計分析 14
第四章、客觀分析與WRF模式模擬結果 16
4.1 客觀分析結果 16
4.1.1高度場分析 16
4.1.2溫度場分析 17
4.1.3比濕場分析 18
4.1.4水氣通量輻散分析 19
4.1.5 K-index分析 19
4.1.6可降水量分析 20
4.1.7相當位溫垂直剖面分析 21
4.1.8位渦垂直剖面分析 22
4.2 WRF模式模擬結果 23
4.2.1高度場模擬結果 23
4.2.2溫度場模擬結果 24
4.2.3比濕場模擬結果 25
4.2.4水氣通量輻散模擬結果 26
4.2.5 K-index模擬結果 27
4.2.6可降水量模擬結果 27
4.2.7鋒生函數模擬結果 28
4.2.8相當位溫垂直剖面模擬結果 29
4.2.9位渦垂直剖面模擬結果 29
4.2.10降水量模擬結果 30
第五章、結論與未來展望 32
參考文獻 35
附圖 37參考文獻 林松錦與陳雯美，1987：Barnes客觀分析法中影響半徑的選擇。大氣科學，15，59-68。
林松錦，2007：GPS RO觀測資料對東亞地區梅雨研究的影響評估與分析應用。國家實驗研究院國家太空中心專題研究報告，96-NSPO(B)-SP-FA07-02(E)，60pp。
黃清勇與朱延祥，2004：FORMOSAT-3/COSMIC科學研究簡介。大氣科學，32，293-328。
陳泰然，2000：台灣豪雨研究回顧與展望。科學發展月刊，28，103-106。
蔡清彥與陳正改，1980：影響台灣北部地區之梅雨系統。大氣科學，7，49-58。
Anthes, R., C. Rocken, Y.-H. Kuo, 2000: Applications of COSMIC to meteorology and climate. TAO, 11, 115-156.
______, P. A. Bernhardt, Y. Chen, L. Cucurull, K. F. Dymond, D. Ector, S. Healy, S.-P. Ho, D. C. Hunt, Y.-H. Kuo, H. Liu, K. Manning, C. McCormick, T. K. Meehan, W. J. Randel, C. Rocken, W. Schreiner, S. V. Sokolovskiy, S. Syndergaard, D. C. Thompson, K. E. Trenberth, T.-K. Wee, N. L. Yen, and Z. Zeng, 2008: The COSMIC/FORMOSAT-3 Mission: Early Results. Bull. Amer. Meteor. Soc., 89, 313-333.
Barker, D., W. Huang, Y.-R. Guo, and Al. Bourgeois, 2003: A Three-Dimensional Variational (3DVAR) Data Assimilation System for Use with MM5. NCAR Technical Note (NCAR/TN-453+STR), 68 pp.
______, W. Huang, Y.-R. Guo, A.J. Bourgeois, and Q.N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897–914.
Bluestein, H. B., 1993: Synoptic-Dynamic Meteorology in Midlatitudes. Vol. II, Oxford University Press, 594pp.
Fjeldbo, G. and V. R. Eshelman, 1968: The atmosphere of Mars analyzed by integrated inversion of the Mariner IV occultation data. Planet. Space Sci., 16, 1035–1059.
Gandin, L.S., 1963: Objective Analysis of Meteorological Fields, Gidrometeoizdat, Leningrd, 242 pp.
Huang, C.-Y., Y.-H. Kuo, and S.-H. Chen, 2005: Improvements on typhoon forecast with assimilated GPS occultation refractivity. Wea. Forecasting, 20, 931–953.
______, C.-Y., Y.-H. Kuo, S.-Y. Chen, A. S. K. A. V. P. Rao, and C.-J. Wang, 2007: The assimilation of radio occultation data and its impact on rainfall prediction along the west coast of india during monsoon 2002. Pure Appl. Geophys., 164, 1577-1591.
Kliore, A.J., T.W. Hamilton, and D.L. Cain, 1964: Determination of some physical properties of the atmosphere of Mars from changes in the Doppler signal of a spacecraft on an earth occultation trajectory, JPL, Technical Report 32-674.
Kuo, Y.-H., X. Zou, S. J. Chen, W. Huang, Y.-R. Guo, R. A. Anthes, M. Exner, D. Hunt, C. Rocken, and S. Sokolovskiy, 1997: A GPS/MET sounding through an intense upper-level front, Bull. Amer. Meteor. Soc., 79, 617-626.
___, Y.-H., T.-K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. A. Anthes, 2004: Inversion and error estimation of GPS radio occultation data. J. Meteor. Soc. Japan, 82 , 507-531.
Kursinki, E. R., G. A. Hajj, K. R. Hardy, L. J. Romans, and J. T. Schofield, 1995: Observing tropospheric water vapor by radio occultation using the global positioning system. Geophys. Res. Letter, 22, 2365-2368.
Leroy, S. S., 1997: Measurement of geopotential heights by GPS radio occultation. J. Geophys. Res., 102, 6971–6986.
Phinney, R. A. and D. L. Anderson, 1968: On the radio occultation method for studying planetary atmospheres. J. Geophys. Res., 73, 1819-1827.
Parrish, D. F., and J. C., Derber, 1992: the National Meteorological Centre’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
Rocken, C., R. Anthes, M. Exner, D. Hunt, S. Sokolovskiy, R. Ware, M. Gorbunov, W. Schreiner, D. Feng, B. Herman, Y. Kuo, and X. Zou, 1997: Analysis and validation of GPS/MET data in the neutral atmosphere. J. Geophys. Res., 102, 29849–29866.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the advanced research WRF Version 2. NCAR Tech Notes-468+STR.
Thayer, D., 1974: An improved equation for the radio refractive index of air. Radio Sci., 9, 803-807.
Ware, R., M. Exner, D. Feng, M. Gorbunov, K. Hardy, B. Herman, Y.-H. Kuo, T. Meehan, W. Melbourne, C. Rocken, W. Schreiner, S. Sokolovskiy, F. Solheim, X. Zou, R. A. Anthes, S. Businger, and K. Trenberth, 1996: GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull. Amer. Meteor. Soc., 77, 19–40.
Wu, C.-C., H.-C. Kuo, H.-H. Hsu, and B. J.-D. Jou, 2000: Weather and climate research in Taiwan: Potential application of GPS/MET data. TAO, 11, 211-234.
Yunck, T. P., C.-H. Liu, and R. Ware, 2000: A history of GPS sounding. TAO, 11, 1-20.
Zou, X., Y.-H. Kuo, and Y.-R. Guo, 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon. Wea. Rev., 123, 2229-2249.指導教授 林松錦(Song-Chin Lin) 審核日期 2008-7-9 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit