博碩士論文 107621603 詳細資訊




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:41 、訪客IP:18.190.219.238
姓名 阮氏青(Nguyen Thi Chinh)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 NAN
(Intercomparison between Supertyphoons Mangkhut and Yutu (2018): Rapid Intensification and Track Evolution as Explored by the Ocean-Coupled HWRF)
相關論文
★ 雲微物理參數化法應用於颱風模式中之研究★ 1998年臺灣梅雨個案模擬及其應用 -蘭陽平原之擴散研究
★ 地形對颱風路徑的影響之數值探討★ 中尺度MM5數值模式與大氣擴散模式之整合應用研究
★ 侵台颱風之GPS折射率3DVAR資料同化及數值模擬★ 地形及渦旋初始化對類似納莉颱風路徑及環流變化之影響
★ 類似桃芝颱風路徑之模擬★ WRF模式在颱風路徑預報應用與EOF分析誤差因素
★ 利用WRF3DVAR同化GPS折射率資料探討 對於颱風預報的影響★ 衛星資料結合變分分析對數值預報之影響
★ 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★ 利用MM5 4DVAR同化虛擬渦旋探討其對WRF模式預報颱風之影響
★ GPS掩星觀測資料同化及對區域天氣預報模擬之影響★ 西北向侵台颱風登陸前中心路徑打轉之模擬研究
★ 衛星資料與虛擬渦旋四維變分同化對颱風數值模擬的影響★ 資料同化對台灣地區颱風和梅雨模擬之影響
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 (2026-2-1以後開放)
摘要(中) 2018年超級颱風玉兔(Yutu)及山竹(Mangkhut) 生成於西北太平洋且經歷快速增強,並接連登陸於菲律賓北部。本研究使用HWRF模式進行海洋耦合(CTL)及非耦合(UN)之兩實驗,探討海洋對颱風之影響,進一步了解兩個颱風個別的快速增強過程及路徑變化。
模式結果顯示,UN會明顯高估颱風強度,於CTL改善過強的強度,尤其於颱風玉兔模擬中更加明顯。當颱風玉兔增強時,受到眼牆區域之強垂直平均角動量平流及垂直渦流角動量平流影響,增強颱風底層入流及高層外流,將強角動量向內傳遞,導致颱風玉兔快速增強過程強於颱風山竹,此強不對稱徑向入流是透過眼牆與附近較高溫的海水交互作用而產生,且特別常發生於典型的強颱風。研究亦進行海溫敏感度測試,當颱風所經過區域海溫降低1oC,颱風山竹將不會快速增強,而颱風玉兔受其影響較小,颱風強度仍會快速增強。颱風玉兔環境場中較弱的深層垂直風切導致移動速度較慢,相較於移動速度較快的颱風山竹,此現象有利於颱風玉兔更快發展快速增強。模式模擬中,相較於初始海洋溫度的調整,快速增強後的發展主要受到物理參數化方法影響。
路徑方面,在模擬時間的前面兩天(山竹)或前面三天(玉兔),海洋耦合、非耦合、移除地形及調整海洋溫度的實驗皆獲得相當好的路徑模擬,但經過這段時間後,兩個颱風皆往北偏。相較於CTL,UN在實驗中顯現出較好的路徑模擬,特別是在颱風山竹。雖然菲律賓及呂宋島對山竹的路徑向北偏折没有產生明顯的影響,但會减弱玉兔的向北偏折。實驗中也有將初始海溫降低1oC,會使兩個颱風向北偏折的情形減弱,若將海溫升高1oC,則會使向北偏折增強。這兩個颱風在靠近北菲律賓時的路徑偏折主要受到物理參數化方法和初始時間影響,而物理參數化方法對於颱風玉兔的路徑模擬有較大的影響,對於颱風山竹的強度影響較小。於位渦趨勢收支中顯示山竹在北菲律賓的向北偏折主要受到水平位渦平流導致的向西北移動趨勢及絕熱加熱導致的向西但微向北移動趨勢所影響,而颱風玉兔在靠近登陸時的向北偏折主要受到水平位渦平流導致的向西北方移動趨勢影響。
摘要(英) Consecutive Supertyphoons Mangkhut and Yutu in 2018 underwent rapid intensifications (RI) over the western North Pacific (WNP) and made landfall at the northern Philippines. Hurricane Weather Research and Forecasting System (HWRF) was used to investigate the different processes in RI over the WNP and track evolution near the northern Philippines between both typhoons.
Both ocean-coupled (CTL) and uncoupled (UN) experiments were conducted for comparison. The model results show that CTL improves the over-predicted typhoon intensity of UN as a result of typhoon-ocean interactions, particularly for Yutu. Stronger angular momentum (AM) is transported inward by the intensifying radial inflow flow at lower levels and outflow at upper levels for Yutu, which then leads to a stronger RI than Mangkhut, mainly contributed by the larger positive vertical mean AM advection in the troposphere and vertical eddy AM advection at low levels near the eyewall. The simulated RI will not occur if the initial ocean temperature along the Mangkhut track is decreased by 1oC, while Yutu’s RI is much less affected by the same temperature decrease or increase. Environmental weaker deep-layer vertical wind shear associated with the slower-moving Yutu is favorable for the faster development of RI as compared to the faster-moving Mangkhut. The later development after RI onset for both typhoons is more dominated by physics schemes applied in the simulations than the initial ocean temperature change; however, their induced RI onsets and rates are only slightly changed.
For the typhoon approaching the northern Philippines, all coupled and uncoupled, no-terrain, and ocean temperature change experiments obtain good simulated tracks in the first two days (for Mangkhut) or first three days (for Yutu) and then followed by a northward deflection for both typhoons. UN gives a better-simulated track than CTL, particularly for Mangkhut. The Philippines and Luzon terrains do not affect significantly the northward deflection for Mangkhut, but they ease the northward track deflection for Yutu. The northward track deflection for both typhoons decreases when their initial ocean temperature is reduced by 1oC, while it increases when their initial ocean temperature is increased by 1oC. The track deflection near the northern Philippines of both typhoons is much more sensitive to physics schemes and initial time. However, the physics schemes have a more effective control on the track of Yutu, but with a less influence in determining Mangkhut intensity. Diagnostics of potential vorticity (PV) tendency budget shows that the northward deflection for Mangkhut near the northern Philippines can be explained by the northwestward tendency induced by horizontal PV advection and the westward but with slightly northward tendency induced by diabatic heating, while the northward deflection for Yutu near landfall is mainly dominated by the northwestward tendency induced by horizontal PV advection.
關鍵字(中) ★ NAN 關鍵字(英) ★ Ocean-Coupled HWRF
★ Supertyphoon
★ Rapid intensification
★ Track evolution
論文目次 摘要 i
Abstract iii
Acknowledgement v
Table of Contents vi
List of Tables viii
List of Figures ix
Notation Illustration xvii
Chapter 1. Introduction 1
Chapter 2. Model Aspects and Overview of Two Supertyphoons 5
2.1. The Coupled Model 5
2.2. Data for Simulations 6
2.3. Overview of Two Supertyphoons 6
2.4. AAM Budget 8
2.5. PV Tendency Budget 8
Chapter 3. Rapid Intensification over the Western North Pacific 10
3.1. Numerical Experiments 10
3.2. Simulation Results 10
3.2.1. Typhoon Track 10
3.2.2. Typhoon Intensity 11
3.2.3. Influences of Ocean Conditions to Typhoon Intensity 12
3.2.4. Typhoon Circulation 14
3.2.5. AAM Budget Analysis 16
3.3. Discussions on Factors Affecting RI 19
3.3.1. Sensitivity Experiments for Ocean Temperature 19
3.3.2. Vertical Wind Shear 21
3.3.3. Typhoon Translation 22
3.3.4. Sensitivity to Physics Schemes 22
Chapter 4. Track Evolution of Both Typhoons Approaching the Northern Philippines 24
4.1. Numerical Experiments 24
4.2. Sensitivity to Air-sea Coupling 25
4.2.1. Typhoon Track and Intensity 25
4.2.2. Ocean Conditions and Horizontal Wind Speed 26
4.3. Track Forecast with and without Terrain 27
4.3.1. Simulated Track 27
4.3.2. Typhoon Circulation 28
4.4. Simulated Precipitation 30
4.5. Sensitivity to Initial Ocean Temperature 31
4.6. PV Tendency Budget 32
4.7. Sensitivity to Physics Schemes 35
4.8. Sensitivity to Initial Time 38
Chapter 5. Conclusions 40
References 43
Tables 51
Figures 54
參考文獻 Bender, M. A., I. Ginis, and Y. Kurihara, 1993: Numerical simulations of tropical cyclone-ocean interaction with a high‐resolution coupled model. J. Geophys. Res., 98(D12), 23, 245–23, 263, https://doi.org/10.1029/93JD02370.
 , and I. Ginis, 2000: Real‐case simulations of hurricane–ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon. Wea. Rev., 128, 917–946, https://doi.org/10.1175/1520-0493(2000)128%3C0917:RCSOHO%3E2.0.CO;2.
Chan, J. C. L., R. T. Williams, 1987: Analytical and Numerical Studies of the Beta-Effect in Tropical Cyclone Motion. Part I: Zero Mean Flow. J. Atmos. Sci., 44, 1257-1265, https://doi.org/10.1175/1520-0469(1987)044%3C1257:AANSOT%3E2.0.CO;2
 , F. M. Ko, and Y. M. Lei, 2002: Relationship between potential vorticity tendency and tropical cyclone motion. J. Atmos. Sci., 59, 1317–1336, https://doi.org/10.1175/1520-0469(2002)059%3C1317:RBPVTA%3E2.0.CO;2
Chandrasekar, R., C. Balaji, 2012: Sensitivity of tropical cyclone Jal simulations to physics parameterizations. J. Earth Syst. Sci., 121, 923–946, https://doi.org/10.1007/s12040-012-0212-8
Chang, Y.-T., I-I Lin, H.-C. Huang, Y.-C. Liao and C.-C. Lien, 2019: The association of typhoon intensity increase with translation speed increase in the South China Sea. Sustainability. 12, 939, https://doi.org/10.3390/su12030939
Chang, S. W., and R. V. Madala, 1980: Numerical simulation of the influence of sea surface temperature on translating tropical cyclones. J. Atmos. Sci., 37, 2617–2630. https://doi.org/10.1175/1520-0469(1980)037%3C2617:NSOTIO%3E2.0.CO;2
Charney, J. G., and A. Eliassen, 1964: On the Growth of the Hurricane Depression. J. Atmos. Sci., 21, 68–75,
https://doi.org/10.1175/1520-0469(1964)021%3C0068:OTGOTH%3E2.0.CO;2.
Chen, H., and D.-L. Zhang, 2013: On the rapid intensification of Hurricane Wilma (2005). Part II: Convective bursts and the upper-level warm core. J. Atmos. Sci., 70, 146–162, https://doi.org/10.1175/JAS-D-12-062.1.
Chen, X. M., M. Xue, J. Fang, 2018: Rapid Intensification of Typhoon Mujigae (2015) under different sea surface temperatures structural changes leading to rapid intensification. J. Atmos. Sci., 75, 4313–4335, https://doi.org/10.1175/JAS-D-18-0017.1.
Choi, Y., K. S. Yun, K. J. Ha, K. Y. Kim, S. J. Yoon, and J. C. Chan, 2013: Effects of asymmetric SST distribution on straight-moving Typhoon Ewiniar (2006) and recurving Typhoon Maemi (2003). Mon. Wea. Rev., 141, 3950–3967,
https://doi.org/10.1175/MWR-D-12-00207.1
Cione, J. J., 2015: The relative roles of the ocean and atmosphere as revealed by buoy air sea observations in hurricanes. Mon. Wea. Rev., 143, 904–913,
https://doi.org/10.1175/MWR-D-13-00380.1.
Črnivec, N., R. K. Smith, and G. Kilroy, 2016: Dependence of tropical cyclone intensification rate on sea-surface temperature. Quart. J. Roy. Meteor. Soc., 142, 1618 1627, https://doi.org/10.1002/qj.2752.
Emanuel, K. A., 1986: An air‐sea interaction theory for tropical cyclones. Part I: Steady state maintenance. J. Atmos. Sci., 43, 585–605,
https://doi.org/10.1175/1520-0469(1986)043%3C0585:AASITF%3E2.0.CO;2.
Frank, W. M., E. A. Ritchie, 2001: Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes. Mon. Wea. Rev., 129, 2249–2269.
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700, https://doi.org/10.1175/1520-0493(1968)096%3C0669:GVOTOO%3E2.0.CO;2.
Holland, G. J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 2519–2541,
https://doi.org/10.1175/1520-0469(1997)054%3C2519:TMPIOT%3E2.0.CO;2.
Huang, C.-Y., T.-C. Juan, H.-C. Kuo and J.-H. Chen, 2020: Track deflection of Typhoon Maria (2018) during a westbound passage offshore of northern Taiwan: Topographic influence. Mon. Wea. Rev. (2020) 148 (11): 4519–4544, https://doi.org/10.1175/MWR-D-20-0117.1.
Huang, C. Y., C. A. Chen, S. H. Chen, and D. S. Nolan, 2016: On the upstream track deflection of tropical cyclones past a mountain range: Idealized experiments. J. Atmos. Sci., 73, 3157–3180, https://doi.org/10.1175/JAS-D-15-0218.1
Hsu, L. H., S. H. Su, R. G. Fovell, and H. C. Kuo, 2018: On typhoon track deflections near the east coast of Taiwan. Mon. Wea. Rev., 146(5), 1495–1510, https://doi.org/10.1175/MWR-D-17-0208.1
Kanase, R. D., and P. S. Salvekar, 2015: Impact of physical parameterization schemes on track and intensity of severe cyclonic storms in Bay of Bengal. Meteorol. Atmos. Phys., 127, 537–559, https://doi.org/10.1007/s00703-015-0381-5
Kaplan, J., and M. DeMaria, 2003: Large‐scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea. Forecasting, 18, 1093–1108, https://doi.org/10.1175/1520-0434(2003)018%3C1093:LCORIT%3E2.0.CO;2.
 , M. DeMaria, J. A. Knaff, 2010: A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 25, 220–241, https://doi.org/10.1175/2009WAF2222280.1.
Katsube, K., and M. Inatsu, 2016: Response of tropical cyclone tracks to sea surface temperature in the western North Pacific. J. Climate, 29, 1955–1975, https://doi.org/10.1175/JCLI-D-15-0198.1
Knaff, J. A., M. DeMaria, C. R. Sampson, J. E. Peak, J. Cummings, and W. H. Schubert, 2013: Upper oceanic energy response to tropical cyclone passage. J. Climate, 26, 2631–2650, https://doi.org/10.1175/JCLI-D-12-00038.1.
Lee, C. Y., and S. S. Chen, 2012: Symmetric and asymmetric structures of hurricane boundary layer in coupled atmosphere–wave–ocean models and observations. J. Atmos. Sci., 69, 3576–3594, https://doi.org/10.1175/JAS-D-12-046.1.
Li, D.-Y., and C.-Y. Huang 2018: The influences of orography and ocean on track of Typhoon Megi (2016) past Taiwan as identified by HWRF. Journal of Geophysical Research: Atmospheres, 123, 11, 492–11,517, https://doi.org/10.1029/2018JD029379
Lin, I.-I., C.-C. Wu, K. A. Emanuel, I.-H. Lee, C.-R. Wu and I.-F. Pun, 2005: The interaction of supertyphoon Maemi (2003) with a warm ocean eddy. Mon. Wea. Rev., 133, 2635–2649, https://doi.org/10.1175/MWR3005.1.
 , C.-C. 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,
https://doi.org/10.1175/2008MWR2277.1.
Lin, Y. L., S. Y. Chen, C. M. Hill, and C. Y. Huang 2005: Control parameters for track continuity and deflection associated with tropical cyclones over a mesoscale mountain. Journal of the Atmospheric Sciences, 62, 1849–1866.
https://doi.org/10.1175/JAS3439.1
Mandal, M., U. C. Mohanty, P. Sinha, and M. M. Ali, 2007: Impact of sea surface temperature in modulating movement and intensity of tropical cyclones. Natural Hazards., 41, 413–427, https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1007%2Fs11069-006-9051-8.
Mandal, M., U. C. Mohanty, and S. Raman, 2004: A Study on the Impact of Parameterization of Physical Processes on Prediction of Tropical Cyclones over the Bay of Bengal with NCAR/PSU Mesoscale Model. Natural Hazards., 31, 391–414, https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1023%2FB%3ANHAZ.0000023359.24526.24
Mei, W., C.-C. Lie, I.-I. Lin, and S.-P. Xie, 2015: Tropical cyclone induced ocean response: A comparative study of the South China Sea and tropical Northwest Pacific. J. Climate, 28, 5952–5968, https://doi.org/10.1175/JCLI-D-14-00651.1.
 , and C. Pasquero, 2013: Spatial and temporal characterization of sea surface temperature response to tropical cyclones. J. Climate, 26, 3745–3765,
https://doi.org/10.1175/JCLI-D-12-00125.1.
Merrill, R. T., 1988: Environmental influences on hurricane intensification. J. Atmos. Sci., 45, 1678–1687,
https://doi.org/10.1175/1520-0469(1988)045%3C1678:EIOHI%3E2.0.CO;2
Molinari, J., P. Dodge, D. Vollaro, K. L. Corbosiero, and F. Marks, Jr., 2006: Mesoscale aspects of the downshear reformation of a tropical cyclone. J. Atmos. Sci., 63, 341–354, https://doi.org/10.1175/JAS3591.1.
 , and D. Vollaro, 2010: Rapid intensification of a sheared tropical storm. Mon. Wea. Rev., 138, 3869–3885, https://doi.org/10.1175/2010MWR3378.1.
Nasrollahi, N., A. Aghakouchak, J. Li, X. Gao, K. Hsu, S. Sorooshian, 2012: Assessing the impacts of different WRF precipitation physics in hurricane simulations. Weather Forecast., 27, 1003–1016, https://doi.org/10.1175/WAF-D-10-05000.1
Oey, L. Y., T. Ezer, D. P. Wang, S. J. Fan, and X. Q. Yin, 2006: Loop current warming by Hurricane Wilma. Geophys. Res. Lett., 33, L08613,
https://doi.org/10.1029/2006GL025873.
Raju, P. V. S., J. Potty, and U. C. Mohanty, 2011: Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorol. Atmos. Phys., 113, 125–137, https://doi.org/10.1007/s00703-011-0151-y
Reasor, P. D., M. D. Eastin, and J. F. Gamache, 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603–631, https://doi.org/10.1175/2008MWR2487.1.
Shay, 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,
https://doi.org/10.1175/1520-0493(2000)128%3C1366:EOAWOF%3E2.0.CO;2.
Smith, R. K., and M. T. Montgomery, 2015: Toward Clarity on understanding tropical cyclone intensification. J. Atmos. Sci., 72, 3020– 3031, https://doi.org/10.1175/JAS-D-15-0017.1.
 , J. A. Zhang, and M. T. Montgomery, 2017: The dynamics of intensification in a Hurricane Weather Research and Forecasting simulation of Hurricane Earl (2010). Quart. J. Roy. Meteor. Soc., 143, 293–308, https://doi.org/10.1002/qj.2922.
Srinivas C.V., R. Venkatesan, D. V. Bhaskar Rao, and D. Hari Prasad, 2007: Numerical simulation of Andhra severe cyclone (2003): model sensitivity to boundary layer and convection parameterization. Pure Appl Geophys, 164, 1465-1487, https://doi.org/10.1007/s00024-007-0228-1
Srinivas, C.V., D. V. Bhaskar Rao, V. Yesubabu, R. Baskaran, and B. Venkatraman, 2013: Tropical cyclone predictions over the Bay of Bengal using the high-resolution Advanced Research Weather Research and Forecasting (ARW) model. Q. J. Roy. Meteor. Soc., 139, 1810–1825, https://doi.org/10.1002/qj.2064
Stern, D. P., and D. S. Nolan, 2011: On the vertical decay rate of the maximum tangential winds in tropical cyclones. J. Atmos. Sci., 68, 2073–2094,
https://doi.org/10.1175/2011JAS3682.1.
Sun, J., and L. Y. Oey, 2015: The influence of the ocean on Typhoon Nuri (2008). Mon. Wea. Rev., 143, 4493–4513, https://doi.org/10.1175/MWR-D-15-0029.1.
Sun, J., Oey, L. Y., Chang, R., Xu, F., & Huang, S. M., 2015: Ocean response to typhoon Nuri (2008) in western Pacific and South China Sea. Ocean Dynamics, 65, 735–749, https://doi.org/10.1007/s10236-015-0823-0
Tallapragada, V., L. Bernardet, M. Biswas, I. Ginis, Y. Kwon, Q. Liu, et al., 2015: Hurricane Weather Research and Forecasting (HWRF) Model: 2015 scientific documentation. NCAR/TN-522+STR, http://dx.doi.org/10.5065/D6ZP44B5.
Tang, C. K., J. C. L. Chan, 2014: Idealized simulations of the effect of Taiwan and Philippines topographies on tropical cyclone tracks. Q. J. R. Meteorol. Soc, 140, 1578–1589, https://doi.org/10.1002/qj.2240
Trivedi, D. K., P., Mukhopadhyay, and S. S. Vaidya, 2006: Impact of physical parameterization schemes on the numerical simulation of Orissa super cyclone (1999). Mausam, 57, 97–110.
Tuleya., R. E., Y. Kurihara, 1982: A note on the sea surface temperature sensitivity of a numerical model of tropical storm genesis. Mon. Wea. Rev., 110, 2063-2069, https://doi.org/10.1175/1520-0469(1980)037%3C2617:NSOTIO%3E2.0.CO;2
Wang, Y., M. T. Montgomery, and B. Wang, 2004: How much vertical shear can a tropical cyclone resist? Bull. Amer. Meteor. Soc., 85, 661-662.
Wang, X., C. Wang, L. Zhang, and X. Wang, 2015: Multi-decadal variability of tropical cyclone rapid intensification in the Western North Pacific. J. Climate, 28, 3806–3820, https://doi.org/10.1175/JCLI-D-14-00400.1.
Wang, Y., and C.-C. Wu, 2004: Current understanding of tropical cyclone structure and intensity changes - A review. Meteorol. Atmos. Phys., 87, 257–278,
https://doi.org/10.1007/s00703-003-0055-6.
Wang, Y., M. T. Montgomery, and B. Wang, 2004: How much vertical shear can a tropical cyclone resist? Bull. Amer. Meteor. Soc., 85, 661-662.
Wu, C. C., C. Y. Lee, and I. I. Lin, 2007: The effect of the ocean eddy on tropical cyclone intensity. J. Atmos. Sci., 64, 3562–3578, https://doi.org/10.1175/JAS4051.1.
 , T.-S. Huang, W.-P. Huang and K.-H. Chou, 2003: A New Look at the Binary Interaction: Potential Vorticity Diagnosis of the Unusual Southward Movement of Tropical Storm Bopha (2000) and Its Interaction with Supertyphoon Saomai (2000)., Amer. Meteor. Soc., 1289-1300,
https://doi.org/10.1175/1520-0493(2003)131%3C1289:ANLATB%3E2.0.CO;2
 , T.-S. Huang and K.-H. Chou, 2004: Potential Vorticity Diagnosis of the Key Factors Affecting the Motion of Typhoon Sinlaku (2002). Mon. Wea. Rev., 132, 2084–2093, https://doi.org/10.1175/1520-0493(2004)132%3C2084:PVDOTK%3E2.0.CO;2
Wu, L., B. Wang, and S. A. Braun, 2005: Impacts of air–sea interaction on tropical cyclone track and intensity. Mon. Wea. Rev., 133, 3299–3314,
https://doi.org/10.1175/MWR3030.1
 , B. Wang, 2000: A potential vorticity tendency diagnostic approach for tropical cyclone motion. Mon. Wea. Rev, 128(6), 1899-1911,
https://doi.org/10.1175/1520-0493(2000)128%3C1899:APVTDA%3E2.0.CO;2
Wu, C.-C., W.-T. Tu, I.-F. Pun, I-I. Lin, and M. S. Peng, 2016: Tropical cyclone-ocean interaction in Typhoon Megi (2010) – A synergy study based on ITOP observations and atmosphere-ocean coupled model simulations, J. Geophys. Res. Atmos., 121,153–16 7, https://doi.org/10.1002/2015JD024198
Yablonsky, R. M., I. Ginis, B. Thomas, V. Tallapragada, D. Sheinin, and L. Bernardet, 2015: Description and analysis of the ocean component of NOAA’s operational Hurricane Weather Research and Forecasting Model (HWRF). J. Atmos. Oceanic Technol., 32, 144–163, https://doi.org/10.1175/JTECH-D-14-00063.1.
Yau, M. K., Y. Liu, D.-L. Zhang, and Y. Chen, 2004: A multi-scale numerical study of Hurricane Andrew (1992). Part VI: Small-scale inner-core structures and wind streaks. Mon. Wea. Rev., 132, 1410-1433,
https://doi.org/10.1175/1520-0493(2004)132%3C1410:AMNSOH%3E2.0.CO;2
Yu, H., W. Huang, Y. H. Duan, J. C. L. Chan, P. Y. Chen, and R. L. Yu, (2007): A simulation study on pre-landfall erratic track of Typhoon Haitang (2005). Meteorol. Atmos. Phys., 97, 189–206. https://doi.org/10.1007/s00703-006-0252-1
Yun, K. S., J. C. Chan, and K. J. Ha, 2012: Effects of SST magnitude and gradient on typhoon tracks around East Asia: A case study for Typhoon Maemi (2003). Atmospheric Research, 109-110, 36–51.
https://doi.org/10.1016/j.atmosres.2012.02.012
Zhang, D. L., Y. Liu, and M. K. Yau, 2001: A multi-scale numerical study of Hurricane Andrew (1992). Part IV: Unbalanced flows. Mon. Wea. Rev., 129, 92–107,
https://doi.org/10.1175/1520-0493(2001)129%3C0092:AMNSOH%3E2.0.CO;2.
Zhao, X., and J. C. L. Chan, 2016: Changes in tropical cyclone intensity with translation speed and mixed-layer depth: Idealized WRF-ROMS coupled model simulations. Q. J. R. Meteorol. Soc., 143, 152–163. https://doi.org/10.1002/qj.2905
指導教授 黃清勇(Ching-Yuang Huang) 審核日期 2021-1-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明