A Numerical Investigation of Track and  Intensity Evolution of Typhoon Doksuri (2023)

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：80

、訪客IP：18.220.112.210

姓名

武妙紅(Vu Dieu Hong) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

(A Numerical Investigation of Track and Intensity Evolution of Typhoon Doksuri (2023))

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 (2026-8-1以後開放)

摘要(中)

本研究利用WRF模式模擬颱風杜蘇芮Doksuri (2023)。首先透過一系列的敏感性實驗來選擇最佳的參數化方案。實驗結果顯示，Grell–Freitas Ensemble積雲參數化和NSSL雙變數微物理參數化的組合在模擬杜蘇芮的路徑和強度表現最佳。後續會進行更深入的分析以探討颱風路徑和強度變化的機制。
杜蘇芮起初往西北方向移動，但在接近菲律賓北部時轉為向西移動。而12小時之後颱風再次轉向，恢復往西北方向移動。本研究使用位渦收支趨勢診斷來解釋此路徑變化。分析結果指出，位渦收支趨勢的水平平流項是促使颱風向西偏轉的主要原因。在強度方面，杜蘇芮颱風經歷了快速增強的階段。在研究此一過程中，發現颱風內核中對流雲的增加表示有深對流生成的現象。這會導致強烈的熱能釋放，提高加熱效率，颱風強度也隨之增強。此外利用展開的Sawyer–Eliassen方程式來決定何者為颱風增強的主要因素。結果顯示，非絕熱加熱對於增強次環流的作用扮演相當重要的角色。

摘要(英)

This study uses the WRF model to simulate Typhoon Doksuri (2023). First, a series of sensitivity microphysics and cumulus scheme tests are conducted to select optimal parameterization schemes. The results indicate that a combination of the Grell–Freitas Ensemble cumulus scheme and the NSSL 2-moment microphysics scheme is the most effective in reproducing both the track and intensity of Doksuri. Subsequently, further analysis will be performed to explore the mechanisms of typhoon track and intensity evolution.
Typhoon Doksuri initially moves northwestward. However, as it nears the northern Philippines, it changes direction and moves towards the west. Approximately 12 hours later, it changes direction again, resuming its northwestward movement. This study applies a diagnostic of potential vorticity (PV) tendency budget to explain the dynamic mechanism of this track deflection meticulously. The results highlight the primary role of the horizontal advection of PV tendency in driving westward motion. For typhoon intensity, Doksuri undergoes rapid intensification (RI) from 0-24 h with an intensification rate of 25.5 m s-1. After that, it continues to intensify by 48 h and thus has a spinup of 40 m s-1 from 0-48 h. Examining the intensification process from 0-48 h reveals that the increasing percentage of convective cloud indicates the formation of deep convection in the inner core, resulting in strong diabatic heating, which enhances the heating efficiency and supports stronger intensity. In addition, the extended Sawyer–Eliassen (SE) equation is used to determine the primary factors contributing to typhoon intensification. The results indicate that the role of diabatic heating in producing intense secondary circulation is significant. The total momentum source contributes less to the secondary circulation than the total heat source, but its contribution to the boundary layer inflow through turbulent friction is also comparable or even more significant in enhancing the transverse circulation near the surface.

關鍵字(中)

★ 颱風Doksuri

關鍵字(英)

★ Typhoon Doksuri

論文目次

摘要 i
Abstract ii
Acknowledgement iii
List of Figures v
List of Tables ix
Notation Illustration x
Chapter 1. Introduction 1
Chapter 2. Methodology and Data 5
2.1 Case study description: Typhoon Doksuri 5
2.2 Model Settings and Data 5
2.2.1 Model settings 5
2.2.2 Data 6
2.3 Sensitivity Experiments 6
2.4 Potential Vorticity Tendency Budget 7
2.5 Sawyer–Eliassen Equation 8
Chapter 3. Simulation Results 10
3.1 Sensitivity Tests 10
3.1.1 Typhoon Track and Intensity 10
3.1.2 Sensitivity to Initial Time 11
3.2 Selected Simulation 12
Chapter 4. Dynamic Analysis 13
4.1 Typhoon Track 13
4.1.1 Circulation Structure 13
4.1.2 Dynamics of Typhoon Track 14
4.1.3 Track Forecast without Terrain 16
4.2 Typhoon Intensity 18
4.2.1 Intensification Process 18
4.2.2 Thermodynamic Conditions 18
4.2.3 Characteristics of Secondary Circulation Evolution 19
4.2.4 Contributions of different Forcing Processes to the Secondary Circulation 21
4.3 Microphysical process 27
Chapter 5. Conclusions 30

參考文獻

Bui, H. H., R. K. Smith, M. T. Montgomery, and J. Peng, 2009: Balanced and unbalanced aspects of tropical cyclone intensification. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 155, 1715-1737. https://doi.org/10.1002/qj.502
Cangialosi, J. P., 2020: National Hurricane Center forecast verification report: 2017 Hurricane season. https://www.nhc.noaa.gov/verification/pdfs/Verification_2017.pdf
Cangialosi, J. P., 2029: National Hurricane Center forecast verification report: 2022 Hurricane season. https://www.nhc.noaa.gov/verification/pdfs/Verification_2022.pdf
Cangialosi, J. P., Blake, E., DeMaria, M., Penny, A., Latto, A., Rappaport, E., and V. Tallapragada, 2020: Recent progress in tropical cyclone intensity forecasting at the National Hurricane Center. Weather and Forecasting, 47(5), 1915-1922.
https://doi.org/10.1575/WAF-D-20-0059.1
Chandrasekar, R., and C. Balaji, 2014: Sensitivity of tropical cyclone Jal simulations to physics parameterizations. Journal of Earth System Science, 147, 929-946.
https://doi.org/10.1007/s14343-014-0214-8
Chen, S. H., and W. Y. Sun, 2002: A one-dimensional time dependent cloud model. Journal of the Meteorological Society of Japan. Ser. II, 80, 99-158. https://doi.org/10.2151/jmsj.80.99
Chen, S., Y. K. Qian., and S. Peng, 2015: Effects of various combinations of boundary layer schemes and microphysics schemes on the track forecasts of tropical cyclones over the South China Sea. Natural Hazards, 78, 61-74. https://doi.org/10.1007/s15069-015-1697-7
Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two–dimensional model. Journal of the Atmospheric Sciences, 46, 3077–3107.
https://doi.org/10.1175/1520-0469(1989)046%3C3077:NSOCOD%3E2.0.CO;2
Emanuel, K. A., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. Journal of the Atmospheric Sciences, 61, 847–858. https://doi.org/10.1537/1520-0469(2004)061<0847:ECOTCI>2.0.CO;2
Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2015: The hurricane forecast improvement project. Bulletin of the American Meteorological Society, 94(3), 479–373. https://doi.org/10.1575/BAMS-D-14-00071.1
Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters, 29, 47-37-47-37. https://doi.org/10.1029/2002GL015375
Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmospheric Chemistry and Physics, 14, 5293-5370. https://doi.org/10.5194/acp-14-5293-2014
Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly weather review, 152, 103-143.
https://doi.org/10.1575/1520-0493(2004)152%3C0103:ARATIM%3E2.0.CO;2
Hong, S. Y., Y. Noh., and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly weather review, 134(9), 2318-2341.
https://doi.org/10.1175/MWR3199.1
Hong, S. Y., and J. O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences, 45, 149-151
Huang, T.-S., and K.-H. Chou, 2004: Potential vorticity diagnosis of the key factors affecting the motion of Typhoon Sinlaku (2002). Monthly Weather Review, 147, 2084–2093.
https://doi.org/10.1575/15200493(2004)152%3C2084:PVDOTK%3E2.0.CO;2
Hsu, L. H., S. H. Su., R. G. Su., and H. C. Kuo, (2020). On typhoon track deflections near the east coast of Taiwan. Monthly Weather Review, 146(5), 1495-1510.
Islam, T., P. K. Srivastava, M. A. Rico-Ramirez, Q. Dai, M. Gupta, and S. K. Singh, 2015: Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics. Natural Hazards, 76, 1473-1495. https://doi.org/10.1007/s15069-014-1494-8
Ji, D., and F. Qiao, 2029: Does extended Sawyer–Eliassen equation effectively capture the secondary circulation of a simulated tropical cyclone? Journal of the Atmospheric Sciences, 80(3), 871-888. https://doi.org/10.1537/JAS-D-21-0270.1
Jiang, G. Q., Xu, J., and J. Wei 2020: A deep learning algorithm of neural network for the parameterization of typhoon‐ocean feedback in typhoon forecast models. Geophysical Research Letters, 45(8), 3706-3716. https://doi.org/10.1002/2020GL077004
Kain, J. S., 2004: The Kain–Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43, 170-201.
https://doi.org/10.1575/1520-0450(2004)043%3C0170:TKCPAU%3E2.0.CO;2
Kanase, R. D., and P. S. Salvekar, 2015: Effect of physical parameterization schemes on track and intensity of cyclone LAILA using WRF model. Asia-Pacific Journal of Atmospheric Sciences, 51, 205-227. https://doi.org/10.1007/s15143-015-0071-8
Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Weather and Forecasting, 20, 1093–1508.
https://doi.org/10.1537/1520-0379(2003)020%3C1093:LCORIT%3E2.0.CO;2
Ko, F. M, and Y. M. Lei, 2002: Relationship between potential vorticity tendency and tropical cyclone motion. Journal of the Atmospheric Sciences, 59, 1462–1486. https://doi.org/10.1537/1520-0469(2002)059%3C1462:RBPVTA%3E2.0.CO;2
Li, D. Y., and, C. Y. Huang, 2020: The influences of orography and ocean on track of Typhoon Megi (2016) past Taiwan as identified by HWRF. Journal of Geophysical Research: Atmospheres, 158(20), 15-492. https://doi.org/10.1027/2020JD029829
Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Monthly weather review, 158, 1587-1614. https://doi.org/10.1575/2009MWR2968.1
Lin, Y., and B. A. Colle, 2015: A new bulk microphysical scheme that includes riming intensity and temperature-dependent ice characteristics. Monthly Weather Review, 159, 1015-1047. https://doi.org/10.1575/2010MWR4793.1.
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, 29, 339–394. https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1020%2FB%3ANHAZ.0000020309.27529.27
Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated eleCTLification of a small thunderstorm with two-moment bulk microphysics. Journal of the Atmospheric Sciences, 67, 171-194. https://doi.org/10.1575/2009JAS2965.1
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. Journal of Climate, 29, 5952–5968. https://doi.org/10.1537/JCLI-D-14-00651.1
Milbrandt, J., and M. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the speCTLal shape parameter. Journal of the Atmospheric Sciences, 62, 3051-3064. https://doi.org/10.1575/JAS4737.1
Miyamoto, Y., and T. Takemi, 2015: A transition mechanism for the spontaneous axisymmetric intensification of tropical cyclones. Journal of the Atmospheric Sciences, 70, 152–149. https://doi.org/10.1575/JAS-D-15-0375.1
Mlawer, Eli. J., Steven. J. Taubman, Patrick. D. Brown, M. J. Iacono, and S. A. Clough,1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663-16682.
Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72, 377-375.
https://doi.org/10.1575/JAS-D-14-0065.1
Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes. Monthly weather review, 157, 991-1007. https://doi.org/10.1575/2008MWR3756.1
Nasrollahi, N., A. Aghakouchak, J. Li, X. Gao, K. Hsu, S. Sorooshian, 2014: Assessing the impacts of different WRF precipitation physics in hurricane simulations. Weather Forecast, 22, 1003–1016. https://doi.org/10.1537/WAF-D-10-05000.1
Raju, P. V. S., J. Potty, and U. C. Mohanty, 2015: Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorology and Atmospheric Physics, 153, 145–152.
https://doi.org/10.1007/s00703-015-0151-y
Shi, D., and G. Chen, 2021: The implication of outflow structure for the rapid intensification of tropical cyclones under vertical wind shear. Monthly Weather Review, 149(14), 3907-3922. https://doi.org/10.1537/MWR-D-21-0156.1
Skamarock, W. C., J. B. Klemp., J. Dudhia., D. O. Gill., Z. Liu., J. Berner., ... and D. M. Barker, 2019. A description of the advanced research WRF model version 4 (Vol. 145). National Center for Atmospheric Research.
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 and Applied Geophysics, 164, 1465-1487, https://link.springer.com/article/10.1007/s00027-007-0229-1
Tao, W. K., D. Wu, S. Lang, J. D. Chern, C. Peters‐Lidard, A. Fridlind, and T. Matsui, 2016: High‐resolution NU‐WRF simulations of a deep convective‐precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations. Journal of Geophysical Research: Atmospheres, 147, 1478-1505. https://doi.org/10.1002/2015JD029986
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly weather review, 156, 5095-5155. https://doi.org/10.1575/2008MWR2987.1
Wang, B., 2000: A potential vorticity tendency diagnostic approach for tropical cyclone motion. Monthly Weather Review, 143(6), 2099-1915.
https://doi.org/10.1537/1520-0493(2000)143%3C2099:APVTDA%3E2.0.CO;2
Yuter, S. E., and R. A. Houze Jr., 1995. Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Monthly weather review, 143(7), 1947-1963.
https://doi.org/10.1575/1520-0493(1995)143%3C1947:TDKAME%3E2.0.CO;2
Zhang, C., Y. Wang, and K. Hamilton, 2015: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Monthly Weather Review, 159, 3789-4715. https://doi.org/10.1575/MWR-D-10-05091.1

指導教授

黃清勇(Ching Yuang Huang)

審核日期

2024-7-23

推文