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研究生:楊伯謙
研究生(外文):Bo-cian Yang
論文名稱:改善四維變分都卜勒雷達變分分析系統(VDRAS)與天氣研究預報模式(WRF)結合流程以提升台灣之定量降水預報
指導教授:廖宇慶
學位類別:碩士
校院名稱:國立中央大學
系所名稱:大氣物理研究所
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:2013
畢業學年度:102
語文別:中文
論文頁數:77
中文關鍵詞:四維變分定量降水預報
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台灣的地形複雜且四面環海,氣象上因海上無測站觀測資料可以提供分析,所以雷達扮演了重要的角色,而高聳的山脈也增加了預報的困難度。由美國國家大氣研究中心(National Center for Atmospheric Research, NCAR)發展的都卜勒雷達變分分析系統(Variational Doppler Radar Analysis System,VDRAS),是藉由雷達所得到的徑向風和回波的資料,經四維變分資料同化方法(4DVAR)來反演出天氣系統之三維動力場及熱力場,並進行預報。前人研究曾在台灣使用VDRAS檢視同化雷達資料後的預報能力,並與WRF(Weather Research and Forecasting)模式結合並進行預報,其預報的表現有很明顯的改善。本研究的目的是延續前人以結合VDRAS與WRF來進行預報的方式,但改善整個流程中出現的若干問題,具體而言,包括如何加入VDRAS產生的氣壓場到WRF中,如何消除邊界上的錯誤回波,以及如何改善預報回波減弱等問題,期望能進一步提升短期的定量降水預報(Quantitative Precipitation Nowcasting)。
本研究利用的個案為2008年台灣西南氣流實驗(SoWMEX/TiMREX)第八次密集觀測期間(IOP8)中6月14日的梅雨鋒面進行研究分析。第一部分,是對於VDRAS同化前的背景場相對濕度進行調整,目的為了解決模式邊界上的錯誤回波,目的是為了使預報的回波不會受到其影響。第二部分則是VDRAS的分析場與WRF模式作結合的修正,先前的研究並無更新WRF壓力場,對於壓力分布的動力結構只能用其他變數場對其影響,效果較為緩慢,但壓力為WRF模式的診斷變數,故本實驗修改程式以加入更新的壓力場到WRF進行測試,另一方面,在兩模式結合後會發生預報回波減弱的問題,故將結合時VDRAS的相對濕度進行一些調整,其結果發現有提升的實驗可以將回波減弱的區域調整回來,對於定量降水預報有明顯改善。

The topography of Taiwan is complex. Taiwan is surrounded by sea, where no observation data can be found; besides, tall mountains make the difficulty higher in weather forecast. Thus, radar plays a crucial role in the observation. The Variational Doppler Radar Analysis System (VDRAS) developed by National Center for Atmospheric Research (NCAR) is a system that uses the 4DVAR technique to assimilate the radar reflectivity and radial wind observations, which is capable to inverse the three-dimensional kinematic and thermodynamic fields within a weather system. Then, the forecast comes out. Previous studies in which the analysis fields from VDRAS were merged with MRF showed prominent improvement in results. The purpose of this study is to continue the model of combing VDRAS and WRF. But a few problems arise in the process of the improving. In fact, problems like how to put the pressure field derived from VDRAS into WRF, how to eliminate wrong reflectivity, and how to improve the weakening of radar reflectivity are waiting to be fixed. The study is expected to promote the short-term of Quantitative Precipitation Nowcasting.
  A real case of Mei-Yu front occurred on 14 June 2008 during Southwest Monsoon Experiment (SoWMEX) IOP8 is selected. In the first part of experiment, the relative humidity in the background field of the VDRAS before assimilation would be adjusted, which is done to avoid wrong reflectivity from boundary of model and make reflectivity not affected. The second part is to correct the VDRAS analysis field merged with WRF model. Previous studies do not update the WRF pressure field. The pressure-distributed dynamic structure can only be affected by other variable fields, so the results come out slowly. Since pressure is the diagnostic variables in the WRF model, this study corrects the formula, adding a updated pressure filed to the WRF model. On the other hand, two models merged will produce the problem of the weakening reflectivity, so relative humidity of VDRAS is adjusted when they are merged. The promoted experiment turns out to adjust back the domain where the reflectivity is weakened, and suggests an effective way of Quantitative Precipitation Nowcasting.

中文摘要 i
英文摘要 ii
致謝 iv
目錄 v
圖表說明 vii
第一章 緒論 1
1.1前言 1
1.2文獻回顧 2
1.3研究目的 3
第二章 資料控管及模式介紹 5
2.1雷達資料品質控管及處理 5
2.2都卜勒雷達變分分析系統VDRAS 5
2.2.1中尺度背景場 6
2.2.2雲模式 7
2.2.3四維變分資料同化 8
2.3 WRF模式系統 10
2.4校驗方法 11
第三章 個案介紹 14
3.1 2008年西南氣流實驗 14
3.2 IOP8個案介紹 15
第四章 改善VDRAS之中尺度背景場之敏感度實驗 18
4.1 VDRAS模式設定及實驗設計 18
4.2 分析場的結果分析 20
第五章 改善VDRAS與WRF結合過程之定量降水預報實驗 22
5.1 壓力變數的取代以及調整濕度 22
5.2 預報結果及檢驗定量降水 24
第六章 結論與未來展望 27
6.1 結論 27
6.2 未來展望 28
參考文獻 30
附錄 33
附表 35
附圖 36

Barnes, S. L., 1973: Mesoscale objective map analysis using weighted time series observations. NOAA Tech. Memo. Erl Nssl-62, 60pp.
Crook, N. 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 J. Sun, 2004: Analysis and Forecasting of the Low-Level Wind during the Sydney 2000 Forecast Demonstration Project. Weather and Forecasting, 19, 151-167.
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.
Kawabata J., H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Wakazuki, 2007: An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensiojnal variational data assimilation system, J. Meteor. Soc. Japan, 85, 255-276.
Kawabata, T., T. Kuroda, H. Seko, and K. Saito, 2011:A cloud-resolving 4D-Var assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area, Mon. Wea. Rev., AMS early release version.
Lin, Y., P. Ray, and K. Johnson, 1993: Initialization of a modeled convective storm using Doppler radar derived fields. Mon. Wea. Rev., 121, 2757–2775.
Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observation using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642–1661.
____, and N. A. Crook, 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.
Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, 117–132.
____, 2005a: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, 793–813.
____, 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.
____, M. Chen, and Y. Wang, 2010: A frequent-updating analysis system based on radar, surface, and mesocale model data for the Beijing 2008 Forecast Demonstration Project. Wea. Forecasting, 25, 1715-1735.
Warner, T. T., E. E. Brandes, C. K. Mueller, J. Sun, and D. N. Yates, 2000: Prediction of a flash flood in complex terrain. Part I: A comparison of rainfall estimates from radar, and very short range rainfall simulations from a dynamic model and an automated algorithmic system. J. Appl. Meteor., 39, 815–825.
Tai, S.-C., 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.
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.
____, and J. Sun, 2007: Multiple radar data assimilation and short -range QPF of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3318–3404.

楊靜伃,2012:使用四維變分都卜勒雷達變分分析系統(VDRAS)與WRF改善短期定量降水預報。
鄧仁星,2000:RASTA(Radar Analysis System for Taiwan Area)使用說明書。 

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