博碩士論文 946201009 詳細資訊




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姓名 麥翠珊(Choi-San Mak)  查詢紙本館藏   畢業系所 大氣物理研究所
論文名稱 利用MM5 4DVAR同化虛擬渦旋探討其對WRF模式預報颱風之影響
(Impacts of Assimilation of a Virtual Vortex Obtained from MM5 4DVAR on WRF Typhoon Predictions)
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摘要(中) 模式初始分析場資料與實際情況的差異,為造成模式預報誤差的原因之一。目前已經有很多觀測資料可供模式同化,例如衛星資料等,使模式初始場更接近真實大氣,但改善的程度不及加入虛擬渦旋的作用大。虛擬渦旋可以由經四維變分資料同化(4DVAR)來調整初始場,使此初始場包含受到模式動力條件限制的颱風渦旋,即颱風渦旋的虛擬資料同化(BDA)。本研究提出一個新的BDA方法,使用MM5 4DVAR經過BDA調整後的初始場,從中取得模式產生的渦旋的變數資訊,經三維變分資料同化(3DVAR)到WRF 模式裡,探討對侵台颱風路徑及強度預報的影響。這也使WRF能導入一個較傳統簡單的渦旋更為理想的平衡渦旋以進行預報作業及改善研究。
本研究目的主要評估此新的同化方法對颱風模擬的影響,針對珊珊颱風(2006)及聖帕颱風(2007),進行72小時模擬。結果顯示同化4DVAR BDA渦旋的三維風場對颱風模擬大體上有最明顯的改善。此外,3DVAR風場似乎有往質量場調整的趨勢,因此3DAVR同化不合理的BDA溫度場,更容易產生一個不合理的渦旋的結構。本研究模擬顯示此資料同化使颱風的移動速度加快。這可能與經同化後較強的初始渦旋有關。對颱風強度的模擬,此同化顯示出明顯的改善。敏感度測試顯示,若只同化海平面氣壓,對颱風強度模擬僅有較為微弱的改善。加入其他變數場(風場、溫度場及濕度場),對模擬並未產生一致性的改善。
摘要(英) The discrepancies between model initial analysis and the true state may contribute to the factors for model prediction errors. To adjust the initial fields for reducing the discrepancies, many data, such as satellite observations, are available for assimilation, however still showing less impacts as compared to those from insertion of a bogus vortex. Using a four dimensional variational method (4DVAR), the bogus vortex can be effectively assimilated into the model to adjusts the initial field under constraints of model dynamics, which is the so-called bogus data assimilation (BDA). In this study, we address a new BDA method which adopts the better balanced vortex from MM5 4DVAR than the traditional simple Rankine vortex and then applies the three dimensional variational method (3DVAR) to assimilate this vortex data into WRF to investigate the impacts on track and intensity predictions of typhoons impinging Taiwan.
The target of this research is to evaluate the impacts of the new method on typhoon prediction. Two typhoons, Shanshan (2006) and typhoon Sepat (2007), were selected in this study, and they were simulated for 72 h. The results show that assimilation of the 3D wind of the virtual model vortex from 4DVAR in general have the largest improvement on typhoon simulation. Besides, the wind field tends to adjust to the mass field in 3DVAR and hence an unreasonable vortex structure may be produced when an unrealistic temperature from BDA has been assimilated as well by 3DVAR. The results also indicate that this assimilation tends to faster the typhoon movement, possibly due to the intensified 3DVAR vortex after the assimilation of the virtual vortex. For simulation of typhoon intensity, this approach may give significant improvement. Sensitive tests show this improvement, however, becomes much less when sea surface pressure was assimilated instead. Assimilation with the combined data (wind, temperature and moisture) in general does not lead to more consolidated improvement.
關鍵字(中) ★ 虛擬渦旋 關鍵字(英) ★ Bogus vortex
論文目次 中文摘要………………………………………………………...................................i
英文摘要......................................................................................................................ii
誌謝.............................................................................................................................iii
目錄.............................................................................................................................iv
表目錄…………………………………………………………….............................vi
圖目錄………………………………………………………....................................vii
第一章 緒論……………………………………………….....................................1
1-1 前言…………………………………………………...............................1
1-2 文獻回顧…………………………………………...................................1
1-3 研究動機……………………………………………...............................3
第二章 研究方法與實驗設計………………………………….............................5
2-1 資料來源………………………………………….................................5
2-2 模式簡介……………………………………….....................................5
2-3 研究方法……………………………………………..............................6
2-4  實驗設計………………………………………......................................9
第三章 個案介紹………………………………………………............................10
3-1 珊珊颱風……………………………………..........................................10
3-2 聖帕颱風……………………………………...…...................................10
第四章 結果分析與討論……………………………............................................11
4-1 珊珊颱風個案分析…………………………..........................................11
a. 4DVAR BDA後的MM5初始場............................................................11
b. 3DVAR同化不同參數後之WRF初始場…….....................................12
c. MM5及WRF模擬的結果……………………….................................14
4-2 聖帕颱風個案分析…………………………………..............................15
4-2-1 初始時間為14日00UCT…………................................................15
a. MM5 4DVAR初始場場及3DVAR WRF初始場….............................15
b. MM5及WRF模擬的結果 …………………….………...................16
4-2-2 初始時間為16日00UCT……………………………...................16
a.4DVAR BDA後的MM5初始場…………………………...………....16
b.3DVAR同化不同參數後之WRF初始場…...................................…..17
c.MM5及WRF模擬的結果……………….................………………....17
4-3 比較各模擬結果的結構差異…………….......................................……18
a.珊珊颱風spin up結構分析………………............................................18
b.聖帕颱風spin up結構分析……………...........................................….20
第五章 敏感度測試…………………………………….............................……21
第六章 結論與展望……………………………………….…................………23
參考文獻………………………………………………..................................……..27
表…………………………………………………….................…………………...30
圖……………………………………………………….................………….……..34
參考文獻 吳俊澤,2007:利用MM5 4DVAR模式同化掩星折射率資料及虛擬渦旋探討颱風數值模擬之影響。國立中央大學碩士論文。70頁。
黃葳芃,2006:投落送資料對颱風路徑模擬評估研究-康森米雷颱風個案分析。國立台灣大學博士論文。168頁。
Fujita, T., 1952: Pressure distribution within a typhoon. Geophys. Mag., 23, 437-451.
Barker, D. M., W. Huang, Y.-R. Guo, and A. J. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note. NCAR/TN-453 + STR, 68 pp. [Available from UCAR Communications, P.O. Box 3000, Boulder, CO, 80307, USA.]
——, W. Huang, Y.-R. Guo, and 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.
Chou, K.-H., and C.-C., Wu, 2008: Typhoon Initialization in a Mesoscale-Combination of the Bogus Vortex and the Dropwindsonde Data in DOTSTAR. Mon. Wea. Rev., 136, 865-879.
Grell, G. A., J. Dudhia and D.R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model(MM5). NCAR Technical Note, NCAR/TN-398+STR, 117pp.
Guo, Y.-R., Y.-H., Kuo, J. Dudhia, D. Parsons, and C., Rocken, 2000:Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case. Mon. Wea. Rev., 128, 619-643.
Kurihara, Y., N. A. Bender, and R. J. Ross, 1993: An initialization scheme of huuicane models by vortex specification. Mon. Wea. Rev., 121, 2030-2045.
Low-Nam, S., and C. Davis, 2001: Development of a tropical cyclone bogussing scheme for the MM5 system. 11th PSU/NCAR Mesoscale Model Users’ Workshop, Boulder, CO, NCAR, 130-134.
Neumann, C. J., 1993: Global overview. Chapter 1, Global Guide to Tropical Cyclone Forecasting. WMO, 1.1-1.56.
Park, K, and X. Zou, 2004: Toward developing an objective 4DVAR BDA scheme for hurricane initialization based on TPC observed parameters.Mon Wea Rev., 132, 2054–2069.
Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s Spectral Statistical Interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
Pu, Z.-X., and S. A. Braum, 2001:Evaluation of bogus vortex techniques with four-dimensional variational data assimilation. Mon. Wea. Rev., 120, 1747-1763.
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.
Wang, D., X. Liang, Y. Zhao, and B. Wang, 2008: A compansion of two tropical cyclone bogussing schemes. Wea. and Foresting, 23, 194-204.
Wang Y, 1998: On the bogusing of tropical cyclones in numerical models: the influence of vertical structure. Meteorol. Atmos. Phys., 65: 153–170.
Wu, C.-C., K.-H. Chou, Y. Wang, and Y.-H. Kuo, 2006: Tropical Cyclone Initialization and Prediction Based on Four-Dimensional Variational Data Assimilation. J. Atmos. Sci., 63,2383-2394.
Xiao, Q., X. Zou, and B. Wang, 2000: Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme. Mon. Wea .Rev., 128, 2252-2269.
Zhao, Y., B. Wang, and Y. Wang, 2007: Initialization and simulation of a landfalling Typhoon using a variational bogus mapped data assimilation (BMDA). Meteor. Atmos. Phys., 98, 269-282.
Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836–860.
——, F. Vandenberghe, M. Pondeca, and Y.-H. Kuo, 1997: Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Tech. Note NCAR/TN-435-STR, 110pp. [Available from UCAR Communications, P.O. Box 3000, Boulder, CO 80307.]
——, W. Huang, and Q. Xiao, 1998: A user's guide to the MM5 adjoint modeling system. NCAR Tech. Note NCAR/TN-437+IA, 92pp. [Available from UCAR Communications, P.O. Box 3000, Boulder, CO 80307.]
指導教授 黃清勇(Ching-Yuang Huang) 審核日期 2008-7-2
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