全球跨尺度天氣預報模式(MPAS)是由美國國家大氣研究中心(NCAR)所發展的新一代天氣預報模式，本篇研究將使用MPAS的60-15及60-15-3公里可變解析度全球網格模型模擬近年來的七個颱風，包含蘇迪勒(2015)、梅姬(2016)、尼莎(2017)、瑪莉亞(2018)、山竹(2018)、米塔(2019)、利奇馬(2019)。本篇論文裡的動力初始化方案將會建構real-case的渦旋作為初始場，並且積分一小時，在這一小時裡，模式會重新產生一個新的渦旋，我們便可將這個新的渦旋重新移到一小時前的位置，由此反覆來回積分，讓渦旋強度達到接近觀測的強度，使強度或路徑預報能夠接近觀測，以利於修正預報。
本篇研究的relocation方法在純量場是使用反距離權重法，在向量場是使用MPAS模式內建的投影法。在本篇研究中我們將使用不同物理參數化以及不同初始場強度和不同模式解析度的渦旋在使用渦旋初始化的差異，並會針對路徑預報、氣壓強度、風速強度等等進行探討，以及在渦旋靠近地形時要如何處理地形，使地形對於渦旋初始化的影響達到最小，以此測試MPAS模式在使用這個渦旋初始化方法可以能會遇到的問題。
從結果中我們可以看到我們可以將初始場較弱渦旋透過渦旋初始化的方法改善渦旋的強度使其和觀測一致。透過渦旋初始化的方法，我們可以發現模式解 析度60-15公里和積分強度上的限制，只能使渦旋增強到935hPa、48m/s，超越這個強度的渦旋須改用模式解析度60-15-3公里。從不同物理參數化中我們可以看到在路徑預報上使用渦旋初始化對於使用mesoscale_reference改善情況較convection permitting明顯，而在強度預報的部分使用convection permitting改善情況較mesoscale_reference明顯。從結果中我們也看到在路徑誤差的部分在某些個案中有明顯的改善、其餘個案則是些微改善。在強度預報中，不管是氣壓還是風速的預報，在所有個案都能被改善，除了發生強度最大的時刻會有稍微提早的現象。我們也發現初始場就和觀測差不多強度的渦旋使用渦旋初始化的結果期改善幅度就不明顯。

This study develops a dynamical vortex initialization for a global variable-resolution model (MPAS) in application to simulations of westbound typhoons approaching Taiwan. The MPAS employs 60-15-3 km variable-resolution with 3-km resolution in the vicinity of Taiwan. Seven westbound typhoons are investigated including Soudelor (2015), Megi (2016), Nesat (2017), Maria (2018), Mangkhut (2018), Mitag(2019), Lekima(2019) with different tracks and intensities. Dynamical vortex initialization schemes have been proposed and presented in literatures for regional models with uniform grids. In this study, the dynamical vortex initialization scheme conducts a real-case vortex (in a radius of 600 km of the typhoon center) as downscaled from the initial global operational analysis and then implants the generated vortex into the observed location in several tens of 1-h cycling model integration in 3-km resolution of hexagonal grids. In each 1-h cycle, the model re-generated vortex can be relocated and artificially amplified according to the best-track observations.
In this study, the mothed of the relocation in scalars is use Distance Weighted Interpolation method and vectors is use projection method. We will use different physics suites, initial strength, and model resolution to discuss the tracks and intensities in different typhoons, and we also conduct the situation when the vortex near the terrain, and we have to add some limit in vortex to make the influences of terrain minimize.
From the results, the vortex can be intensified to reach observation by the DVI scheme and can give a better structure and intensity than initial condition. The improvement of tracks error in mesoscalse reference physics suite is better than convection permitting physics suite, and v-match is slightly better than p-match. The improvement of intensities error in convection permitting physics suite is better than mesoscalse reference physics suite, and v-match is slightly better than p-match. For the most case, the intensities error can be improved, especially initial intensities error, but the tracks error is only improved in some case.

摘要……………………………………………………………………………………..……………..…………..........ii
Abstract……………………………………………………………………………………………………….….….…..iv
致謝…………………………………………………………………………..…………………………………..……….vi
目錄…………………………………………………………………………………………………………………….…vii
表目錄……………………………………………………………………………………………………………….....viii
圖目錄…………………………………………………………………………………………………………………....ix
一、 前言…………………………………………………………………………………………….………..…....1
二、 模式設定及個案實驗……………………………………………………………………..………..…4
2-1 MPAS模式設定……………………………………………………..……………………………….........4
2-2 實驗設計………………………..…………………………………………………………………..……..…….5
2-3 資料來源………………………………………………………………………………………..…………..……6
2-4 渦旋初始化方法……………………………………………………………………………..……………....6
2-5 反距離權重法……………………………………………………………………………..…………..……….7
2-6 投影法……………………………………………………………………………………..……………………….7
2-7 颱風個案…………………………………………………………………………………………...………....…8
2-7-1 蘇迪勒颱風………………………………….….……………………………….……...……..8
2-7-2 梅姬颱風…………………………………………………………….………………..…...…...9
2-7-3 尼莎颱風…………………….…………………………………………………..……..……..10
2-7-4 瑪莉亞颱風…………………………………………………..…………………………..…..11
2-7-5 山竹颱風………………………………………………………….…………………………….12
2-7-6 米塔颱風……………………………………..…………………………………………………13
三、 渦旋初始化模擬結果分析……………………………………………………..….………………14
3-1 蘇迪勒颱風……………………………………………………………..……………..…………..14
3-2 梅姬颱風…………………………………………..…………………..………………………..….16
3-3 尼莎颱風……………………………………………………………………..……………..……...20
3-4 瑪莉亞颱風……………………………………………………………………..………..………..21
3-5 山竹颱風……………………………………………………………..………………………..…...22
3-6 利奇馬颱風……………………………………………………………………………………..….22
四、 渦旋靠近地形時渦旋初始化模擬結果分析……………………………….……..……..23
4-1 蘇迪勒颱風…………………………………………………………….....……………....……..24
4-2 尼莎颱風…………………………………………………………………………...…...…....…..25
4-3 山竹颱風……………………………………………………………………………..…….….……26
4-4米塔颱風…………………………………………………………………….…………………….…26
五、 結論………………………………………………………………………………………….………..….....28
參 考 文 獻………………………………………………………………………………….………..………...…32
附 表……………………………………………………………………………………………….………………..….35
附 圖…………………………………………………………………………………………….…………………......38

蘇胤瑞，2017:颱風渦旋初始化對全球模式MPAS模擬之影響。國立中央大學，大
氣物理研究所，碩士論文，1-69。
陳舒雅，2008: GPS掩星觀測資料同化及對區域天氣預報模擬之影響。國立中央
大學，大氣物理研究所，博士論文，1-107。
黃建翔，2018:侵臺颱風之高解析度全球模式模擬研究。國立中央大學，大氣物理
研究所，碩士論文，1-106。
阮子齊，2019:利用高解析度全球模式FV3GFS探討侵台颱風瑪莉亞(2018)受地形
影響之路徑偏折。國立中央大學，大氣物理研究所，碩士論文，1-53。
Bender, M. A., R. J. Ross, R. E. Tuleya, and Y. Kurihara, 1993: Improvements in tropical cyclone track and intensity forecasts using the GFDL initialization system. Mon. Wea. Rev., 121, 2046-2061.
Cha, D.-H., and Y. Wang, 2013: A dynamical initialization scheme for real-time forecasts of tropical cyclones using the WRF Model. Mon. Wea. Rev., 141, 964–986.
Chen, C.-T., Y.-L. Chen, and H. V. Nguyen, 2014: The spin-up process of a cyclone vortex in a tropical cyclone initialization scheme and its impact on the initial TC structure. SOLA, 10, 93-97.
Hendricks, E. A. and M. S. Peng, 2013: Evaluation of Multiple Dynamic Initialization Schemes for Tropical Cyclone Prediction. Mon. Wea. Rev., 141, 4028-4048.
Holland, G. J. 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 1212-1218.
Huang, Y.-H. and C.-C. Wu,2011: The influence of island topography on typhoon track deflection. Mon. Wea. Rev., 139, 1708-1727.

Hsiao, L.-F., C.-S. Liou, T.-C. Yeh, Y.-R. Guo, D.-S. Chen, K.-N. Huang, C.-T. Terng, and J.-H. Chen, 2010: A vortex relocation scheme for tropical cyclone initialization in advanced research WRF. Mon. Wea. Rev., 138, 3298-3315.
Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 2030-2045.
Kwon, I.-H., and H.-B. Cheong, 2010: Tropical cyclone initialization with a spherical high-order filter and an idealized three-dimensional bogus vortex. Mon. Wea. Rev., 138, 1344-1367.
Liu, H.-Y., and Y. Wang, 2018: A Dynamical Initialization Scheme for Tropical Cyclones under the Influence of Terrain. Wea. Forecasting, 33, 641-659.
Liu, J., S. Yang, L. Ma, X. Bao, D. Wang, and D. Xu, 2013: An initialization scheme for tropical cyclone numerical prediction by enhancing humidity in deep-convection region. J. Climate, 52, 2260-2277.
Liu, H.-Y., and Z.-M. Tan, 2016: A dynamical initialization scheme for binary tropical cyclones. Mon. Wea. Rev., 144, 4787–4803.
Nguyen, H. V., and Y.-L. Chen, 2011: High-resolution initialization and simulations of typhoon Morakot (2009). Mon. Wea. Rev., 139, 1463-1491.
Nguyen, H. V., and Y.-L. Chen, 2014: Improvements to a tropical cyclone initialization scheme and impacts on forecasts. Mon. Wea. Rev., 142, 4340-4356.
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.
Park, S.-H. , J. B. Klemp, and W. C. Skamarock, 2014: A comparison of mesh refinement in the global MPAS-A and WRF models using an idealized normal-mode baroclinic wave simulation, Mon. Wea. Rev., 142, 3614-3634.

Pu, Z.-X., and S. A. Braun, 2001: Evaluation of bogus vortex techniques with four-dimensional variational data assimilation. Mon. Wea. Rev., 129, 2033-2039.
Rappin, E. D., D. S. Nolan, and S. J. Majumdar, 2013: A highly configurable vortex initialization method for tropical cyclones. Mon. Wea. Rev., 141, 3556-3575.
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, 2383-2395.
Wu, C.-C. , Y.-H. Huang, and G.-Y. Lien, 2012: Concentric eyewall formation in typhoon Sinlaku (2008). part I: assimilation of T-PARC data based on the ensemble kalman filter (EnKF). Mon. Wea. Rev., 140, 506-527.
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.
Zhang, S., T. Li, X. Ge, M. Peng, and N. Pan, 2012: A 3DVAR-Based dynamical initialization scheme for tropical cyclone predictions. Wea. Forecasting, 27, 473-483.