博碩士論文 101681002 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:7 、訪客IP:3.226.243.130
姓名 戴聖倫(Sheng-Lun Tai)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 都卜勒雷達變分分析系統中地形解析能力之建置及其應用
(The Development and Application of a Terrain-Resolving Scheme for the Forward Model and Its Adjoint in the Four-Dimensional Variational Doppler Radar Analysis System (VDRAS))
相關論文
★ 單雷達風場反演—【移動坐標法】的特性分析與應用★ 由都卜勒風場反演熱動力場的新方法 ——TAMEX IOP#2颮線個案應用分析
★ 利用VAD技術及回波保守方程反演渦度場★ 利用單都卜勒雷達反演三維風場之研究─以數值模式資料驗證
★ 在地形上由都卜勒風場反演熱動力場★ 利用Extended-GBVTD方法反求非軸對稱颱風(颶風)風場結構
★ 同化雷達資料對數值預報影響之研究★ 使用系集卡曼濾波器同化都卜勒雷達資料之研究
★ 以3DVAR同化都卜勒雷達觀測及反演資料對於數值模擬結果的影響★ 台灣北部初秋豪雨個案之降雨特性研究
★ 同化都卜勒雷達資料改善模式預報之研究★ 2008年台灣西南部地區TRMM降雨雷達與七股雷達回波觀測比較分析及降雨估計應用研究
★ 使用四維變分同化都卜勒雷達資料以改進短期定量降雨預報★ 同化多部都卜勒雷達資料以提升降水預報能力之研究-2008 SoWMEX IOP8個案分析
★ 結合VDRAS、WRF與雷達網聯資料,以檢視對台灣地區短期降水預報改善之成效★ 結合都卜勒雷達觀測及反演氣象變數與COSMIC RO資料以改進模式預報之可行性研究
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 此研究將美國國家大氣研究中心所發展之都卜勒雷達變分分析系統(VDRAS)大幅
改進,成為一個可解析地形之雷達資料四維變分同化系統。其建置方式主要利用虛網格沉浸邊界法(GCIBM),將原來架構在笛卡爾座標上之向前(forward)及向後(backward)積分模式進行修改,不需要改變模式座標,即可於分析及預報中同時納入地形效應的影響。改進而成之新系統被命名為IBM_VDRAS,其擁有在各種複雜地形上進行雲模式向前預報及其伴隨模式之向後積分的能力,陡峭程度自平緩山坡至水平方向急遽變化高度之流體障礙物如建築物等,皆可合理進行解析。

為了評估建置後向前預報模式在地行解析能力之表現,吾人進行三種理想個案模擬實驗,包含(1)二維線性山嶽波、(2)三維背山渦旋及(3)對流不穩定情況下與WRF 模式平行預報。實驗結果顯示,更新後之向前積分模式可以正確及合理解析大氣流體在流經理想地形時所產生之擾動。另外,建築物尺度的模擬實驗,也展示了新的模式在解析地形上具有很大的彈性。除此之外,為了能同時測試修正後之伴隨模式之成果,吾人也進行了一個觀測系統實驗(OSSE)。在此實驗中,將WRF 模式預報資料當作真實場(Truth),取出其中部分資料,做成理想雷達觀測資料,最後利用IBM_VDRAS 同化資料後,檢視分析場與真實場之差異。評估結果顯示,不論在大氣動力、熱動力和微物理場量,IBM_VDRAS之反演結果皆存在相當準確性。

此外,IBM_VDRAS 也應用於一個真實個案的降雨過程分析。此劇烈降雨事件發生於2008 年6 月14 日,剛好處於當年西南氣流實驗(SoWMEX),第八個加強觀測期間 (IOP8)中。當時主要因為梅雨鋒面前方之雨帶通過台灣,而造成明顯降雨。從多變數分析驗證結果顯示,分析量皆與各種現地觀測資料如地面站、剖風儀、探空及徑向風吻合。利用高時空解析度分析場進行統計及探討發現,位於台灣西南沿海之低層輻和帶及其相關之局地環流可能是造成此降雨事件之主要機制。為了能夠了解其中繁雜之物理過程,吾人進行一系列敏感性實驗,主要能夠將地形效應、降水蒸發冷卻過程,以及兩者非線性交互作用所產生之影響程度分離出來,以利後續深入探討。經由將各個分量所造成之增量分析後發現,地形效應相較於蒸發冷卻過程,對於此天氣系統之發展影響較為明顯許多。然而,兩種效應單獨來說,皆與多變數分析中,與主要降雨機制有關之場量分布沒有太大相關,反而是兩者所產生之非線性交互作用與其有極高的相關性。從交互作用所造成的各場量之增量分
布,吾人發現到與此個案降雨機制非常接近之配置,自迎風坡延伸至臺灣西南沿岸之相對低溫區域,同時也是相對高壓分布的範圍,此機制形成沿岸之輻合,也影響了降雨的分布。由此可證明,地形效應與蒸發冷卻之交互作用,在這次降雨事件中扮演了極為重要的角
色。
摘要(英) The four-dimensional Variational Doppler Radar Analysis System (VDRAS) developed at the National Center for Atmospheric Research (NCAR) is significantly improved by
implementing a terrain-resolving scheme to its forward model and adjoint based on the Ghost Cell Immersed Boundary Method (GCIBM), which allows the topographic effects to be considered without the necessity to re-build the model on a terrain-following coordinate system. The new system, called IBM_VDRAS, is able to perform forward forecast and backward adjointmodel integration over non-flat lower boundaries, ranging from mountains with smooth slopes to buildings with sharp surfaces. To evaluate the performance of the forward model over complex terrain, idealized numerical experiments of a two-dimensional linear mountain wave and three-dimensional lee side vortices are first conducted, followed by a comparison with a simulation by the Weather Research and Forecasting (WRF) model. In addition, the building-scale simulation demonstrates its flexibility in resolving terrain as well. An Observation Simulation System Experiment (OSSE) is also conducted with the assimilation of simulated radar data to examine the ability of IBM_VDRAS in analyzing orographically forced moist convection. It is shown that the IBM_VDRAS can retrieve terrain-influenced three-dimensional
meteorological fields including winds, thermodynamic and microphysical parameters with reasonable accuracy.

This new system is utilized to study precipitation process in a real case of pre-frontal rainbands passed over southern Taiwan on June 14 of 2008, collected by IOP 8 of the 2008
Southwest Monsoon Experiment (SoWMEX). Prior to physical discussion, the multivariate analysis is verified by various types of measurements including mesonet stations, wind profiler, radiosonde and Doppler radar radial velocity. It shows that IBM_VDRAS has robust performance in retrieving the meteorological states. Further examinations on the frequent updated analyses demonstrate the importance of quasi-steady convergence line near southwestern coast of Taiwan.

The major mechanisms leading to the formation and maintenance of this convergence line is decomposed into pure topographic, pure evaporation cooling, nonlinear interactive effect, and is further explored through a series of sensitivity experiments. Pure topographic effect is found to
be the dominant factor in modulating the convection development. However, the contributions from nonlinear interaction and pure evaporation, although with weaker magnitudes, cannot be neglected. A schematic diagram is formulated to explain how a local circulation system is built and maintained to support the above mentioned quasi-stationary coastal convergence line, which is responsible for a long-lasting severe precipitation event. It is clear that the interaction among mountain blockage, evaporation cooling effect, and the development of an enhanced widespread high pressure zone over the southwest plain area, are the key factors to modulate the evolution of the convective system.
關鍵字(中) ★ 沉浸邊界法
★ 四維變分資料同化
★ 雷達資料同化
★ 都卜勒雷達變分分析系統
★ 地形解析
★ 西南氣流實驗
關鍵字(英) ★ Immersed Boundary Method
★ 4DVar
★ Radar Data Assimilation
★ Variational Doppler Radar Analysis System (VDRAS)
★ Terrain-Resolving
★ SoWMEX/TiMREX
論文目次 Chinese Abstract………………………………………………………………………………………………I
English Abstract………………………………………………………………………………………………III
Acknowledgments…………………………………………………………………………………………………V
Table of Contents……………………………………………………………………………………………VI
List of Tables……………………………………………………………………………………………………X
List of Figures…………………………………………………………………………………………………X
Chapter 1 Introduction………………………………………………………………………………1
Chapter 2 Descriptions of Variational Doppler Radar Analysis
System (VDRAS)……………………………………………………………………………………………………5
2.1 Forward model……………………………………………………………………………………………5
2.2 Cost function and adjoint model……………………………………………6
Chapter 3 Implementation of terrain-resolving scheme by the
Ghost-Cell Immersed Boundary Method……………………………………………9
3.1 Ghost-Cell Immersed Boundary Method…………………………………9
3.2 Steps of the implementation………………………………………………………10
3.2.1 Identification of terrain boundary and grids classification……………………………………………………………………………………………………10
3.2.2 Interpolation for the image point…………………………………10
3.2.3 Ghost-Cell variable update……………………………………………………12
3.2.4 Implementation for adjoint model……………………………………13
Chapter 4 Tests on modified forward model……………………………15
4.1 Two-dimensional linear mountain wave………………………………15
4.2 Three-dimensional lee side vortex………………………………………16
4.3 Parallel forecast comparison with WRF……………………………17
4.4 Building-scale simulation……………………………………………………………20
Chapter 5 An Observation Simulation System Experiment(OSSE) with radar data assimilation………………………………………………………………22
5.1 Pseudo observation………………………………………………………………………………22
5.2 Experimental design……………………………………………………………………………22
5.3 Results of retrieval…………………………………………………………………………23
Chapter 6 Real case analysis: precipitation process analysis …………………………………………………………………………………………………………………………………………25
6.1 Overview of the real case……………………………………………………………25
6.1.1 Synoptic environment……………………………………………………………………26
6.1.2 Prefrontal rainbands evolution…………………………………………26
6.2 Radar data assimilation experiment……………………………………27
6.2.1 Data sources of the mesoscale background and assimilation…………………………………………………………………………………………………………27
6.2.2 Experiment design and assimilation strategy………29
6.2.3 Verification of multivariate analysis………………………30
6.2.4 Multivariate analysis…………………………………………………………………33
6.3 Sensitivity diagnosis in radar data assimilation experiments……………………………………………………………………………………………………………37
6.3.1 Design of sensitivity experiments…………………………………39
6.3.2 Increments over surface layer……………………………………………42
6.3.3 Increments of cross-mountain Y-axis average………43
6.3.4 Role played by evaporation process………………………………45
6.4 Schematic Diagram…………………………………………………………………………………46
Chapter 7 Summary and future work…………………………………………………48
7.1 Summary and conclusions…………………………………………………………………48
7.2 Future work…………………………………………………………………………………………………50
References………………………………………………………………………………………………………………51
Tables…………………………………………………………………………………………………………………………55
Figures………………………………………………………………………………………………………………………56
參考文獻
Ballard, S. P., Z. Li, D. Simonin, and J.-F. Caron, 2016: Performance of 4D-Var NWP-based nowcasting of precipitation at the Met Office for summer 2012. Quart. J. Roy. Meteor. Soc., 142, 472–487.
Barnes, S. L., 1964: A Technique for Maximizing Details in Numerical Weather Map Analysis. J. Appl. Meteor., 3, 396–409
Bosart, L.F., C.J. Vaudo, and J.H. Helsdon, Jr. 1972: Coastal frontogenesis. J. Appl. Meteor., 11, 1236–1258.
Chang, S.-F., J. Sun, Y.-C. Liou, S.-L. Tai, and C.-Y. Yang, 2014: The influence of erroneous background, beam-blocking and microphysical nonlinearity on the application of a
fourdimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts. Meteor. Appl., 21, 444–458.
——,Y.-C. Liou, J. Sun, and S.-L. Tai, 2016: The implementation of the ice phase microphysical process into a four-dimensional Variational Doppler Radar Analysis System (VDRAS) and its impact on parameter retrieval and quantitative precipitation nowcasting. J. Atmos. Sci., 73,
1015–1038.
Chien, M.-H., and C.-M. Wu, 2016: Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM), J. Adv. Model. Earth Syst., 8, 212–223.
Crook, A., and J. Sun, 2002: Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19, 888–898.
——, and——,2004: Analysis and forecasting of the low-level wind during the Sydney 2000 Forecast Demonstration Project. Wea. Forecasting, 19, 151–167.
Davis, C. A., and W.-C. Lee, 2012: Mesoscale analysis of heavy rainfall episodes from SoWMEX/TiMREX. J. Atmos. Sci., 69, 521–537.
Durran, D. R., and J. B. Klemp, 1983: A compressible model for the simulation of moist mountain waves. Mon. Wea. Rev., 111, 2341–2361.
Franke, R., 1982: Scattered data interpolation: Tests of some methods. Math. Comput., 38, 181–200.
Friedrich, K., E. Kalina, J. Aikins, M. Steiner, D. Gochis, P. Kurera, K. Ikeda, and J. Sun, 2016: Raindrop size distribution and rain characteristics during the 2013 Great Colorado Flood. J. Hydrometeor., 17, 53–72.
Gao, T., Y. H. Tseng, and X. Y. Lu, 2007: An improved hybrid Cartesian/immersed boundary method for fluid-solid flows. Int. J. Numer. Methods Fluids, 55, 1189–1211.
Gochis, D., and Coauthors, 2015: The Great Colorado Flood of September 2013. Bull. Amer. Meteor. Soc., 96, 1461–1487.
Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor., 34, 3–15.
Houze, R. A., Jr., 2012: Orographic effects on precipitating clouds. Rev. Geophys., 50, RG1001.
Huang, C.-Y., 2000: Aforward-in-time anelastic nonhydrostaticmodel in a terrain-following coordinate. Mon. Wea. Rev., 128, 2108–2133.
Janjic´, Z., 1977: Pressure gradient force and advection scheme used for forecasting with steep and small scale topography. Beitr. Phys. Atmos., 50, 186–189.
Kawabata, T., T. Kuroda, H. Seko, and K. Saito, 2011: A cloud resolving 4DVAR assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area. Mon. Wea. Rev., 139, 1911–1931.
Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.
Klemp, J.,W. Skamarock, and O. Fuhrer, 2003: Numerical consistency of metric terms in terrain-following coordinates. Mon. Wea. Rev., 131, 1229–1239.
Lim, E., and J. Sun, 2010: A velocity dealiasing technique using rapidly updated analysis from a four-dimensional Variational Doppler Radar Data Assimilation System. J. Atmos. Oceanic Technol., 27, 1140–1152.
Lundquist, K. A., F. K. Chow, and J. K. Lundquist, 2010: An immersed boundary method for the Weather Research and Forecasting model. Mon. Wea. Rev., 138, 796–817.
—, ——, and ——, 2012: An immersed boundary method enabling large-eddy simulations of flow over complex terrain in the WRF Model. Mon. Wea. Rev., 140, 3936–3955.
Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatidudes. John Wiley & Sons, 407 pp.
Mohd-Yusof, J., 1997: Combined immersed boundary/b-spline methods for simulations of flow in complex geometry. Center for Turbulence Research, Annual Research Briefs, NASA
Ames/Stanford University, 317–327.
Navon, I. M., X. Zou, J. Derber, and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Rev., 120, 1433–1446.
Peskin, C., 1972: Flow patterns around heart valves: A numerical method. J. Comput. Phys., 10, 252–271.
Phillips, N., 1957: A coordinate system having some special advantages for numerical forecasting. J. Meteor., 14, 184–185.
Rotunno, R., and R. A. Houze Jr., 2007: Lessons on orographic precipitation from the Mesoscale Alpine Programme. Quart. J. Roy. Meteor. Soc., 133, 811–830.
Schär, C., and D. R. Durran, 1997: Vortex formation and vortex shedding in continuously stratified flows past isolated topography. J. Atmos. Sci., 54, 534–554.
Smith, R. B., 1980: Linear theory of stratified hydrostatic flow past an isolated mountain. Tellus, 32A, 348–364.
Smolarkiewicz, P. K., and R. Rotunno, 1989: Low Froude number flow past three-dimensional obstacles. Part I: Baroclinically generated lee vortices. J. Atmos. Sci., 46, 1154–1164.
Sun, J., 2005: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, 793–813.
——, and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642–1661.
——, and ——, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55, 835–852.
——, and ——, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, 117–132.
——,and Y. Zhang, 2008:Analysis and prediction of a squall line observed during IHOP using multiple WSR-88D observations. Mon. Wea. Rev., 136, 2364–2388.
——, and H. Wang, 2013: Radar data assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a squall line over the U.S. Great Plains. Mon. Wea. Rev., 141, 2245–2264.
——, M. Chen, and Y. Wang, 2010: Frequent-updating analysis system based on radar, surface, and mesoscale model data for the Beijing 2008 Forecast Demonstration Project. Wea.
Forecasting, 25, 1715–1735.
Tai, S.-L., Y.-C. Liou, J. Sun, S.-F. Chang, and M.-C. Kuo, 2011: Precipitation forecasting using Doppler radar data, a cloud model with adjoint, and the Weather Research and Forecasting model: Real case studies during SoWMEX in Taiwan. Wea. Forecasting, 26, 975–992.
Tai, S.-L., Y. Liou, J. Sun, and S. Chang, 2017: The development of a terrain-resolving scheme for the forward model and its adjoint in the four-dimensional Variational Doppler Radar Analysis System (VDRAS). Mon. Wea. Rev., 145, 289–306.
Tseng, Y., and J. Ferziger, 2003: A ghost-cell immersed boundary method for flow in complex geometry. J. Comput. Phys., 192, 593–623.
Tu, C.-C., Y.-L. Chen, S.-Y. Chen, Y.-H. Kuo, and P.-L. Lin, 2016: Impacts of Including Rain-Evaporative Cooling in the Initial Conditions on the Prediction of a Coastal Heavy Rainfall Event during TiMREX. Mon. Wea. Rev., 145, 253–277.
Wang, H., J. Sun, X. Zhang, X. Huang, and T. Auligne, 2013: Radar data assimilation with WRF 4D-Var. Part I: System development and preliminary testing. Mon. Wea. Rev., 141, 2224–2244.
Weckwerth, T. W., L. J. Bennett, L. J. Miller, J. V. Baelen, P. D. Girolamo, A. M. Blyth, and T. J. Hertneky, 2014: An observational and modeling study of the processes leading to deep, moist convection in complex terrain. Mon. Wea. Rev., 142, 2687–2708.
Wulfmeyer, V., and Coauthors, 2008: The Convective and Orographically-induced Precipitation Study: A research and development project of the World Weather Research Program for improving quantitative precipitation forecasting in low-mountain regions. Bull. Amer. Meteor. Soc., 89, 1477–1486.
Xu, W., E. J. Zipser, Y.-L. Chen, C. Liu, Y.-C. Liou, W.-C. Lee, and B. J.-D. Jou (2012), An orography-associated extreme rainfall event during TiMREX: Initiation, storm evolution, and maintenance, Mon. Wea. Rev., 140, 2555–2574.
Yamazaki, H., and T. Satomura, 2010: Non-hydrostatic atmospheric modelling using a combined Cartesian grid. Mon. Wea. Rev., 138, 3932–3945.
指導教授 廖宇慶(Yu-Chieng Liou) 審核日期 2017-7-26
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