四維變分資料同化系統與衛星資料整合以重建台灣與周圍地區的高解析度氣象場

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姓名

黃熠程(Yi-Cheng,Huang) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

四維變分資料同化系統與衛星資料整合以重建台灣與周圍地區的高解析度氣象場
(Merging satellite data with a 4DVAR data assimilation system to reconstruct the high resolution 3D meteorological fields in Taiwan and vicinity)

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摘要(中)

美國國家大氣研究中心(NCAR)所發展之都卜勒雷達變分分析系統(VDRAS)，主要使用四維變分方法同化都卜勒雷達觀測資料，可以提供高時間、空間解析度之對流尺度大氣分析場。但是，由於海洋上之觀測資料缺乏，前人研究皆說明VDRAS運用於台灣島鄰近區域之分析頗具挑戰性。然而，衛星資料能提供海洋上大範圍的觀測與反演資料，例如ASCAT產品可以提供海表面10米風場資料，且不受雲雨區屏蔽。故本篇研究希望利用VDRAS同化ASCAT風場反演資料，改善海洋上觀測資料不足的問題，進而提升其對於對流系統之分析能力。
研究中使用已建置具地形解析能力之VDRAS版本（IBM_VDRAS），在此基礎上並改善前人使用之地面資料同化方法。其中，除了ASCAT風場資料，陸地上之地面站風場也一併同化。用以測試及分析之個案為2013年6月23日13 UTC於台灣西南沿海生成之颮線系統。在探討不同地面資料同化方法之實驗中發現，改善後的地面資料同化方法能避免分析場中觀測資料周圍梯度過大之情形，並能將觀測資訊合理散佈於三維空間。接著，吾人利用此改善之地面資料同化方法，進行各種資料同化效益實驗。研究結果指出，尚未加入地面資料同化時，VDRAS無法分析出颮線系統底層陸風及盛行西南風輻合之特徵。在同化ASCAT與地面站風場後，對於熱力、動力與雲微物理結構都有顯著改變。從垂直結構分析上，底層資料也合理反應至三維空間中。最後，與觀測資料之驗證結果也證明，同化ASCAT風場資料能顯著改善原本低估的西南風場，結合陸地與海上資料的同化後，更能提升三維空間雨水混合比的同化效果。

摘要(英)

The Variational Doppler Radar Analysis System (VDRAS) developed by National Center for Atmospheric Research (NCAR) is able to assimilate Doppler radar observations by using 4DVar method, and to provide high temporal and spatial meteorological states in convective scale. Due to lack of sea surface observation, it has been challenging to generate realistic analysis by using VDRAS in Taiwan and vicinity. As we know, satellite can measure in wide range on the ocean. For example, ASCAT (Advanced SCATterometer) data can be retrieved as product of sea surface 10 meters wind data, which avoids contamination caused by hydrometeors in the atmosphere. Therefore, this study is aimed to improve the accuracy of analysis by additionally assimilating satellite data by using VDRAS.
An improved surface data assimilation scheme has also been developed in updated version of VDRAS that was implemented with a terrain-resolving scheme (IBM_VDRAS). Note that horizontal wind data measured by mesonet and retrieved by the ASCAT are both processed and assimilated. A real case of squall line observed at 13UTC of June 23, 2013 near offshore of southwestern Taiwan is selected in current study.
Test on different surface assimilation schemes is conducted first, which indicates that, compare to previous scheme, modified surface data assimilation scheme can generate smoother and more reasonable analysis. With the updated surface data assimilation scheme, a series of experiments is then conducted to investigate the impacts of assimilation of different kinds of observational data. It reveals that the low-level wind convergence associated with the squall line is successfully analyzed with additional surface data assimilation. The enhanced prevailing southwesterly flow and seaward flow are clearly shown, which even modifies analyzed thermodynamic, dynamical and microphysical structures of the squall line. In addition, evaluations also demonstrate that, with merging of surface data, significant correction of underestimated southwesterly flow on the ocean and better minimization of rainwater mixing ratio are present.

關鍵字(中)

★ 都卜勒雷達變分分析系統

關鍵字(英)

★ VDRAS

論文目次

摘要 i
Abstract ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 viii
第一章緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究目的 4
1.3論文架構 5
第二章研究方法 6
2.1 都卜勒雷達變分分析系統…………………………………………………. 6
2.1.1 中尺度背景場……………………………………………...………………………6
2.1.2 雲解析模式………………………………………………………………...………7
2.1.3 價值函數………………………………………………………..………………….9
2.1.4 伴隨模式……………………………………………………………………….…11
2.2 地形解析數值方法……………………………………………………………………13
2.3 驗證參數………………………………………………………………………………14
第三章地面資料同化技術………………………………………………………………….16
3.1 地面觀測項……………………………………………………………………………16
3.2 風場變數轉轉換……………………………….…………………...…………………16
3.3 四點地面資料同化方法…………………………………………………………...….17
3.4 客觀地面資料同化方法…………………………………………………………...….18
第四章真實個案研究 20
4.1 個案介紹…………………………………...………………………………………….20
4.2 資料處理…………………………………………………...………………………….21
4.2.1 雷達資料品質控管…………………………………………………...………..…21
4.2.2 地面資料品質控管………………………………………………………….....…23
4.2.2 衛星資料品質控管…………………………………………………………….…23
第五章地面資料同化方法比較結果 26
5.1 模式設定與實驗設計...………….………………...………………………………….26
5.2 驗證結果…………………………………………………………...………………….27
5.3 增量場比較………………………………………………………………………...….28
5.4 分析場分析………………………………………………………………………...….29
第六章衛星資料同化實驗結果…………………………………………………………….31
6.1 模式設定與實驗設計...…………………...…….…………………………………….31
6.2 背景場分析………………………………………………...………………………….31
6.3 驗證結果…………………………………………………………………...………….32
6.4 增量場分析…………………………………………………………………………....33
6.5 分析場分析……………………………………………………………………………35
6.6 對流系統結構分析……………………………………………………………...…….37
第七章結論與未來展望…………………………………………………………………….39
7.1 結論 39
7.2 未來展望 40
參考文獻 42
附表 48
附圖 51

參考文獻

陳依涵，2016：發展地面資料同化方法以改善都卜勒雷達變分分析系統之分析與預報能力。國立中央大學大氣物理所碩士論文，1–95。

賴佑晟，2017：海表面風場與通量於熱帶氣旋發展影響之探討。國立中央大學大氣物理所碩士論文，1–76。

蘇俊瑋，2016：利用觀測資料與多都卜勒風場反演系統做垂直速度上的驗證。國立中央大學大氣物理所碩士論文，1–60。

張少凡，2013：同化策略及冰相微物理對四維變分都卜勒雷達分析系統(VDRAS)於定量降雨預報之影響研究。國立中央大學大氣物理所博士論文，1–81。

戴聖倫，2010：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。國立中央大學大氣物理所碩士論文，1–86。

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。國立中央大學大氣物理所碩士論文，1–83。

Anton Verhoef, Marcos Portabella, Ad Stoffelen, 2012: High-Resolution ASCAT Scatterometer Winds Near the Coast. IEEE Trans. Geosci. Remote Sens., 50, 2481-2487.

Barnes, S. L., 1973: Mesoscale objective map analysis using weighted time series observations. NOAA Tech. Memo. Erl Nssl-62, 60pp.

Bi, L., J. Jung, M. Morgan, and J. L. Marshall, 2011: Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System. Monthly Weather Review, 139, 3405-3421.

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.

Chou, K.H., C.-C. Wu, S.-Z. Lin 2013: Assessment of the ASCAT wind error characteristics by global dropwindsonde observations. Journal of Geophysical Research: Atmospheres, 118, 9011-9021.

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 four-dimensional 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.

Chen, X. C., K. Zhao, J. Z. Sun, B. W. Zhou, and W. C. Lee, 2016: Assimilating surface observations in a four-dimensional Variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case. Adv. Atmos. Sci., doi: 10.1007/s00376-016-5290-0., in press.

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., 19:1, 151-167.

EUMETSAT, ASCAT Products Guide, 2011. [Online]. Available: http://www.eumetsat.int.

Franke, R., 1982: Scattered data interpolation: Tests of some methods. Math. Comput., 38, 181–200.

Hersbach, H., and P. Janssen, 2007a: Preparation for assimilation of surface-wind data from ASCAT at ECMWF. ECMWF Research Department Memo., ECMWF, Reading, United Kingdom

Hsu, S. A., E. A. Meindl, and D. B. Gilhousen, 1994: Determining the Power-Law Wind-Profile Exponent under Near-Neutral Stability Conditions at Sea. Journal of Applied Meteorology, 33, 757-765.

Houze, R. A. 1989: Observed structure of mesoscale convective systems and implications for large-scale heating. Quart. J. Roy. Meteor. Soc., 115, 425-461.

——, Jr., S. A. Rutledge, M. I. Biggerstaff, and B. F. Smull, 1989: Interpretation of Doppler weather radar displays in midlatitude mesoscale convective systems. Bull. Amer. Meteor. Soc.,70, 608-619.

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.

Klemp, Joseph B., Robert B. Wilhelmson, 1978: The Simulation of Three-Dimensional Convective Storm Dynamics. J. Atmos. Sci.,35, 1070-1096.

Kessler, E., 1969: On the Distribution and Continuity of Water Substancein Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

Liu, C.-Y., G.-R. Liu, T.-H. Lin, C.-C. Liu, H. Ren, and C.-C. Young, 2014: Using Surface Stations to Improve Sounding Retrievals from Hyperspectral Infrared Instruments, IEEE Trans. Geosci. Remote Sens, 52, 11, 6957-6963, doi:10.1109/TGRS.2014.2305992.
——, J. Li, S.-P. Ho, G.-R. Liu, T.-H. Lin, and C.-C. Young, 2016: Retrieval of Atmospheric Thermodynamic State from Synergistic Use of Radio Occultation and Hyperspectral Infrared Radiances Observations, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, Vol. 9, no. 2, doi:10.1109/JSTARS.2015.2444274.

Li, Y., X. Wang and M. Xue, 2012: Assimilation of radar radial velocity data with the WRF ensemble-3DVAR hybrid system for the prediction of hurricane Ike (2008) . Mon. Wea. Rev. , in press.

Liu, W. T., K. B. Katsaros and J. A. Businger, 1979: Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface. J. Atmos. Sci., 36, 1722–1735.

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.

Miller, M. J., and R. P. Pearce, 1974: A three-dimentional primitive equation model of cumulonimbus convection. Quart. J. Roy. Meteor. Soc., 100, 133–154.

OSI SAF, ASCAT Wind Product User Manual, SAF/OSI/CDOP/KNMI/ TEC/MA/126, San Francisco, CA, 2011. [Online]. Available: http://www. osi-saf.org.

Pan, X., X. Tian, X. Li, Z. Xie, A. Shao, and C. Lu (2012), Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method, J. Geophys. Res., 117, D17113, doi:10.1029/2012JD017684.

Peterson, E. W., and J. P. Hennessey, 2010: On the Use of Power Laws 557 for Estimates of Wind Power Potential. J. Appl. Meteor, 17, 390-394.

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: 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.

——, and N. A. Crook, 2001: Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Sci., 19, 888-898.

——, 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.

Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789–1807.

Tripoli, G. J., and W. R. Cotton, 1981: The use of ice-liquid water potential temperature as a thermodynamic variable in deep atmospheric models. Mon. Wea. Rev., 109, 1094–1102.

Tseng, Y., and J. Ferziger, 2003: A ghost-cell immersed boundary method for flow in complex geometry. J. Comput. Phys., 192, 593–623, doi: 10.1016/ j.jcp. 2003.07.024.

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.

——, Y.-C. Liou, J. Sun, S. –F. 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). J. Atmos. Sci., 145. 289-306.

Verspeek, J., M. Portabella, A. Stoffelen and A. Verhoef Calibration and Validation of ASCAT winds OSI SAF technical report, SAF/OSI/KNMI/TEC/TN/163, 2008.

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.

Xiao, Q., and J. Sun, 2007: Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3381–3404.

——, 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.

Xue, M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square rootKalman filter for evaluating impact of data fromradar networks on thunderstorm analysis and forecast. J. Atmos. Oceanic Technol., 23, 46–66.

指導教授

廖宇慶(Yu-Chieng,Liou)

審核日期

2017-7-11

推文