本研究模擬彰化外海一帶的風場，首先探討如何將天氣預報模式 (Weather Research and Forecasting, WRF) 模擬得到的風速資訊，以邊界條件的形式代入計算流體力學 (Computational Fluid Dynamics, CFD) 中，再以大渦流模型 (Large Eddy Simulation, LES) 計算得到新的結果(WRF-LES)，以獲得在時間上與空間上更高解析度的風場，希望透過結合WRF與LES兩者的特性以改善模擬結果，此即為WRF-LES的運作目的，最後再將模擬結果與量測數據做比較，並探討改善的效果。
　　在改善與分析方面，將WRF-LES分成兩個不同垂直高度之算例進行測試，分別為Case A (高度: 40m~1000m) 與 Case B (高度: 40m~200m)，並將WRF與Case A、Case B 三者的結果與量測數據進行比較，由於Case B在邊界上速度差異不大，造成其模擬結果與WRF相似，而Case A因對流邊界層的影響和邊界上速度差異性較大的關係，造成在模擬的結果上與WRF不再相似，因此將Case A做為之後WRF-LES的配置方式。最後再將WRF、WRF-LES與量測值做分析，就海上而言，無論WRF或WRF-LES的模擬結果都與量測值相似，至於在陸上模擬的結果中，發現當WRF底部 (高度40公尺處) 的模擬數據與量測值符合時，經WRF-LES的計算後，其結果會在高度100m以下與量測值達吻合。

The objective in this study is to improve the resolution of the wind field data from Weather Research and Forecasting (WRF). The offshore data of Changhua are collected from WRF and input to a CFD (Computational Fluid Dynamics) flow solver with Large Eddy Simulation (LES) as boundary conditions. The procedure of coupling the information between WRF and LES is the so-called WRF-LES approach. The numerical results of WRF-LES approach have been compared with anemometer on the sea and Light Detection and Ranging (LiDAR) on the land to validate the procedure and help to improve the predictions.
First of all, two numerical experiments at the same location but different computation domain with respect to the altitudes, namely Case A (40m ~ 1000m) and Case B (40m ~ 200m), have been utilized to evaluate the effect of the Convective Boundary Layer (CBL). It has been found out that the consideration of the CBL has more potential to improve the prediction of WRF-LES approach. Besides, the comparisons between measurement and numerical results have been found to very consistent on the premise that the WRF data under 40 m is also very close to the measurement data.

國立中山大學研究生學位論文審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
目錄 v
圖目錄 vi
表目錄 vii
符號說明 viii
第一章 緒論 1
1.1　前言 1
1.2　文獻回顧 2
1.2.1　WRF模式 2
1.2.2　LES模型 3
1.2.3　WRF-LES模型 4
第二章 研究方法 7
2.1　統御方程式 10
第三章 模擬結果 12
3.1　網格測試 12
3.2　比較算例CASE A和CASE B 15
3.3 　WRF-LES 模擬結果 21
3.4 　LES-LES 模擬結果 29
第四章 結論與未來展望 31
參考文獻 33

[1] Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.Y., Wang, W., & Powers, J. G. (2008). A description of the advanced research WRF version 3 (No. NCAR/TN-475+ STR). Boulder, Colorado: National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division.
[2] Hu, X. M., Nielsen-Gammon, J. W., & Zhang, F. (2010). Evaluation of three planetary boundary layer schemes in the WRF model. Journal of Applied Meteorology and Climatology, 49(9), 1831-1844.
[3] Barthelmie, R. J., Sempreviva, A. M., and Pryor, S. (2010). The influence of humidity fluxes on offshore wind speed profiles. Annales Geophysicae , 28(5), 1043-1052.
[4] Muñoz Esparza, D. (2013). Multiscale modelling of atmospheric flows: towards improving the representation of boundary layer physics. Sint-Genesius-Rode, Belgium: Von Karman Institute for Fluid Dynamics.
[5] Shimada, S., Ohsawa, T., Chikaoka, S., & Kozai, K. (2011). Accuracy of the wind speed profile in the lower PBL as simulated by the WRF model. Sola, 7, 109-112.
[6] Chen, F., Yang, X., & Zhu, W. (2014). WRF simulations of urban heat island under hot-weather synoptic Conditions: The Case study of Hangzhou City, China. Atmospheric research, 138, 364-377.
[7] Leung, L. R., & Qian, Y. (2009). Atmospheric rivers induced heavy precipitation and flooding in the western US simulated by the WRF regional Climate model. Geophysical Research Letters, 36(3).
[8] Moeng, C. H., & Sullivan, P. P. (2015). Large-Eddy Simulation. Encyclopedia of Atmospheric Sciences, 2, 232–240.
[9] Smagorinsky, J. (1963). General circulation experiments with the primitive equations I. the basic experiment*. Monthly weather review, 91(3), 99-164.
[10] Deardorff, J. W. (1972). Numerical investigation of neutral and unstable planetary boundary layers. Journal of the Atmospheric Sciences, 29(1), 91-115.
[11] Moeng, C. H. (1984). A large-eddy-simulation model for the study of planetary boundary-layer turbulence. Journal of the Atmospheric Sciences, 41(13), 2052-2062.
[12] Leonard, A. (1975). Energy cascade in large-eddy simulations of turbulent fluid flows. Advances in geophysics, 18, 237-248.
[13] Talbot, C., Bou-Zeid, E., & Smith, J. (2012). Nested mesoscale large-eddy simulations with WRF: performance in real test Cases. Journal of Hydrometeorology, 13(5), 1421-1441.
[14] Leblebici, E., Ahmet, G., & Tuncer, I. H. (2012). Atmospheric turbulent flow solutions coupled with a mesoscale weather prediction model. In Torque Conference, Oldenburg, Germany.
[15] Zajaczkowski, F. J., Haupt, S. E., & Schmehl, K. J. (2011). A preliminary study of assimilating numerical weather prediction data into Computational fluid dynamics models for wind prediction. Journal of Wind Engineering and Industrial Aerodynamics, 99(4), 320-329.
[16] Liu, Y. B., Warner, T., Liu, Y. W., Vincent, C., Wu, W. L., Mahoney, B., Swerdlin, S., Parks, K. & Boehnert, J. (2011). Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications. Journal of Wind Engineering and Industrial Aerodynamics, 99(4), 308-319.
[17] Marjanovic, N. (2011). Mesoscale and Large-Eddy Simulations for Wind Energy. LawrenCe Livermore National Laboratory.
[18] Mirocha, J. D., Lundquist, J. K., & Kosovic, B. (2010). Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the Advanced Research WRF model. Monthly Weather Review, 138(11), 4212-4228.
[19] Kirkil, G., Mirocha, J., Bou-Zeid, E., Chow, F. K., & Kosovic, B. (2012). Implementation and evaluation of dynamic subfilter-scale stress models for large-eddy simulation using wrf *. Monthly Weather Review, 140(1), 266-284.
[20] Nozawa, K., & Tamura, T. (2012). Coupling method of WRF-LES and LES based on scale similarity model. The Seventh International Colloquium on Bluff Body Aerodynamics and Applications (BBAA7), Shanghai, China.
[21] Li, L., Zhang, L. J., Zhang, N., Hu, F., Jiang, Y., Xuang, C.Y., Jiang, W.M. (2010). Study on the Micro-scale simulation of wind field over Complex terrain by RAMS/FLUENT modeling system. The Fifth International Symposium on Computational Wind Engineering (CWE2010), Chapel Hill, North Carolina.
[22] Kinbara, K., Iizuka, S., Kuroki, M., & Kondo, A. (2010). Merging WRF and LES models for the analysis of a wind environment in an urban area. In Proceedings of the Fifth International Symposium on Computational Wind Engineering, Chapel Hill, North Carolina.
[23] Shaw, W. J., Lundquist, J. K., & Schreck, S. J. (2009). Research needs for wind resource characterization. Bulletin of the American Meteorological Society, 90(4), 535.
[24] Ehrhard, J., Khatib, I. A., Winkler, C., Kunz, R., Moussiopoulos, N., & Ernst, G. (2000). The microscale model MIMO: development and assessment. Journal of Wind Engineering and Industrial Aerodynamics, 85(2), 163-176.
[25] Stull, R. B. (2012). An introduction to boundary layer meteorology. Springer Science & Business Media. Chap.11