(3.235.25.169) 您好!臺灣時間:2021/04/20 02:25
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:鄭羽廷
研究生(外文):Yu-Ting Cheng
論文名稱:同化雷達觀測與反演變數改善模式對流尺度 降雨預報能力:探討OSSE與真實個案
指導教授:廖宇慶
指導教授(外文):Yu-Chieng Liou
學位類別:碩士
校院名稱:國立中央大學
系所名稱:大氣科學學系
學門:自然科學學門
學類:大氣科學學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:114
中文關鍵詞:觀測系統模擬實驗熱動力反演水氣調整
相關次數:
  • 被引用被引用:0
  • 點閱點閱:30
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
  本研究探討如何使用都卜勒雷達資料改善複雜地形區域之降雨預報,透過觀測系統模擬實驗(Observing Systems Simulation Experiment, OSSE)測試得知水氣變數之重要性,因此本研究設計了一系列程序用來調整水氣。透過多都卜勒雷達風場合成方法(Wind Synthesis System using Doppler Measurement, WISSDOM)、熱動力反演與水氣溫度調整,反演複雜地形上的三維風、壓力、溫度與水氣場並同化進WRF,分別使用兩個真實個案探討反演變數是否可提升極短期降雨預報能力,第一個個案是2008年6月14日在台灣南部SoWMEX(South West Monsoon EXperiment)實驗期間的西南季風劇烈降水個案,而第二個是2014年8月19日在台灣北部發生的午後熱對流個案。
  由第一個個案預報結果顯示,同化雷達觀測與反演變數可維持一段時間的雨帶結構並改善模式降雨強度與分布;第二個個案則顯示水氣調整可重現對流系統之細微結構,雖然預報結果有捕捉到台北地區的強降雨,但局部地區仍存在預報錯誤。
  本研究的優點是所有方法皆可直接應用於地形上。此外,僅使用來自兩次體積掃描的雷達資料,即可改進模式極短期降雨預報。
This research discusses rainfall forecast improvement over mountainous regions using Doppler radar data. Through OSSE(Observing Systems Simulation Experiment) tests the importance of vapor is first identified. As a result, a procedure for adjusting vapor is designed. By assimilating three-dimensional meteorological fields obtained from multiple-Doppler radar wind synthesis and thermodynamic retrieval as well as vapor adjustment into WRF, the impact on rainfall forecast for two real cases are investigated. The first case is the heavy precipitation of a southwest monsoon event occurring on 14 June, 2008 in southern Taiwan during SoWMEX (South West Monsoon EXperiment), while the second case is an afternoon thunderstorm event occurring on 19 August, 2014 in northern Taiwan.
The forecast results of the first case show that the rainbands’ structure could be maintained for a period of time and the rainfall intensity and distribution are also improved after assimilating the radar observed and retrieved parameters. The second case demonstrates that the vapor adjustment scheme is able to reproduce some of the fine structures of the convective systems. Extreme rainfall in Taipei is well captured, but false alarm does exist.
The advantage of the methods developed in this research is that they can be applied directly over terrain. In addition, radar data from only two volume scans are sufficient and can be efficiently used to improve the model very short-term rainfall forecast.
中文摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 v
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 1
第二章 使用資料與研究方法 4
2.1 雷達觀測資料 4
2.1.1 七股雷達(RCCG) 4
2.1.2 SPOL 雷達(SPOL) 4
2.1.3 墾丁雷達(RCKT) 4
2.1.4 五分山雷達(RCWF) 5
2.1.5 中大雷達(NCU) 5
2.2 都卜勒雷達風場合成 5
2.2.1 價值函數 6
2.2.2 沉浸邊界法(Immersed Boundary Method, IBM) 10
2.3 熱動力反演 10
2.4 溫度場及水氣調整 13
2.4.1 回波-相對濕度統計 13
2.5 定量校驗方法 15
第三章 觀測系統模擬實驗 17
3.1 實驗設計 17
3.2 各組實驗結果 19
第四章 SoWMEX個案分析、反演與預報 22
4.1 2008 年西南氣流實驗(SOWMEX) IOP#8 1200UTC 個案 22
4.2 模式設定、使用資料與背景場 22
4.2.1 模式設定 22
4.2.2 使用資料 23
4.2.3 背景場 23
4.3 反演驗證與分析 23
4.3.1 反演風場投影之驗證 23
4.3.2 渦度方程診斷 24
4.3.3 反演場分析 24
4.4 預報實驗設計 25
4.5 預報結果 26
第五章 午後熱對流個案分析、反演與預報 28
5.1 2014 年08 月19 日北部弱綜觀午後熱對流個案 28
5.2 模式設定、使用資料與背景場 28
5.2.1 模式設定 28
5.2.2 使用資料 29
5.2.3 背景場 29
5.3 反演驗證與分析 29
5.3.1 反演風場投影之驗證 29
5.3.2 渦度方程診斷 30
5.3.3 背景風場同化驗證 30
5.3.4 反演場分析 31
5.4 預報實驗設計 31
5.5 預報結果 32
第六章 結語與未來展望 34
6.1 結語 34
6.2 未來展望 35
參考文獻 37
附表 41
附圖 43
鍾高陞、廖宇慶及陳台琦,2002:由都卜勒風場反演三維熱動力場的可行性研究-以臺灣地區颮線個案為例。大氣科學,第30期,313–330。
尤心瑜和廖宇慶,2011:使用都卜勒氣象雷達資料改善模式定量降雨預報之可行性研究-以模擬資料測試之實驗結果。大氣科學,第39期,1–24。
陳尉豪和廖宇慶,2011:同化多都卜勒雷達資料以改善模式定量降水預報-2008 SoWMEX IOP8 個案分析。大氣科學,第40期,323–348。
邱健倫,2013:使用氣象雷達改善對流尺度定量降水預報研究-理想和真實個案之分析結果。國立中央大學大氣物理所碩士論文,1–82。
廖浩彥,2014:利用雷達觀測直接反演氣象變數進行資料同化以改進短期定量降水預報-2008 SoWMEX IOP8 個案分析。國立中央大學大氣物理所碩士論文,1–89。
鄧詠霖,2015: 利用雷達觀測與反演變數改善 模式定量降水預報之能力 -2008 年西南氣流實驗 IOP#8 個案分析。國立中央大學大氣物理研究所碩士論文,1–111。
Barnes, S. L., 1973: Mesoscale objective map analysis using weighted time series observation. NOAA Tech. Memo. Erl Nssl-62, 60pp.
Chung, K. S., I. Zawadzki, M. K. Yau, and L. Fillion, 2009: Short-Term Forecasting of a Midlatitude Convective Storm by the Assimilation of Single–Doppler radar Observations. Mon. Wea. Rev., 137, 4115–4135.
Crook, N. A., 1996: The sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev., 124, 1767–1785.
Gal-Chen, T., 1978: A method for the initialization of the anelastic equations: Implications for matching models with observations. Mon. Wea. Rev., 106, 587–606.
Gao, S., Sun, J., Min, J., Zhang, Y., and Ying, Z., 2018: A Scheme to Assimilate “No Rain” Observations from Doppler Radar. Wea Forecasting, 33, 71-88.
Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, 84 pp.
Knight, C.A. and L.J. Miller, 1993: First Radar Echoes from Cumulus Clouds. Bull. Amer. Meteor. Soc., 74, 179–188.
Kollias, P., E.E. Clothiaux, M.A. Miller, B.A. Albrecht, G.L. Stephens, and T.P. Ackerman, 2007: Millimeter-Wavelength Radars: New Frontier in Atmospheric Cloud and Precipitation Research. Bull. Amer. Meteor. Soc., 88, 1608–1624.
Lin, Y., P. S. Ray, and K. W. Johnson, 1993: Initialization of a modeled convective storm using Doppler radar-derived fields. Mon. Wea. Rev., 121, 2757–2775.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.
Liou, Y.-C., 2001: The derivation of absolute potential temperature perturbations and pressure gradients from wind measurements in three-dimensional space. J. Atmos. Oceanic Technol., 18, 577–590.
_____, T.-C. Chen Wang, and K. S. Chung, 2003: A three-dimensional Variational approach for deriving the thermodynamic structure using Doppler wind observations - An application to a subtropical squall line. J. Appl. Meteor., 42, 1443–1454.
_____, J.-L. Chiou, W.-H. Chen, and H.-Y. Yu, 2014: Improving the model convective storm quantitative precipitation nowcasting by assimilating state variables retrieved from multiple-Doppler radar observations. Mon. Wea. Rev., 142, 4017–4035.
Liu, D. C., and J. Nocedal, 1988: On the limited memory BGFS method for large scale optimization. Dept. of Electric Engineering and Computer Science Tech. Rep. NAM 03, Northwestern University, 26 pp.
Natenberg, E., J. Gao, C. Ziegler, M. Xue, F.H. Carr, 2013: Analysis and Forecast of a Tornadic Thunderstorm Using Multiple Doppler Radar Data, 3DVAR, and ARPS Model. Adv. Meteorol., 2013, 281695. http://dx.doi.org/10.1155/2013/281695
Protat, A., and I. Zawadzki, 2000: Optimization of dynamic retrievals from a multiple-Doppler radar network. J. Atmos. Oceanic Technol., 17, 753–760.
Shapiro, A., S. Ellis, and J. Shaw, 1995: Single-Doppler velocity retrievals with Phoenix II data: Clear air and microburst wind retrievals in the planetary boundary layer. J. Atmos. Sci., 52, 265–1287
Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677.
Sun, J., and N. A. Crook, 1997: Dynamic and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiment. 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.
Tai, S.-L., Y.-C. Liou, J. Sun, S.-F. Chang, and M.-C. Kuo, 2011: Precipitation Forecast 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.
Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon. Wea. Rev., 133, 1789–1807.
Tseng, Y., and J. Ferziger, 2003: A ghost-cell immersed boundary method for flow in complex geometry. J. Comput. Phys., 192, 593–623.
Wang, H., J. Sun, S. Fan, and X. Y. Huang, 2013: Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events. J. Appl. Meteor. Climatol., 52, 889–902.
Weisman, M. L., and R. Rotunno, 2000: The use of vertical wind shear versus helicity in interpreting supercell dynamics. J. Atmos.Sci., 57, 1452–1472.
Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 2002a: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130, 433–453.
_____, _____, and _____, 2002b: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and numerical prediction. Mon. Wea. Rev., 130, 454–476.
Xiao, Q., Y. H. Kuo, J. Sun, W. C. Lee, E. Lim, Y. R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44, 768–788.
Yuter, S. E.and R. A. Houze, 1995: Three-Dimensional Kinematic and Microphysical Evolution of Florida Cumulonimbus. Part II : Frequency Distributions of Vertical Velocity, Reflectivity, and Differential Reflectivity. Mon. Wea. Rev. 123, 1941-1963.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔