雷達徑向風資料同化對降雨系統模擬之影響研究

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姓名

林欣弘(Hsin-Hung Lin) 查詢紙本館藏

畢業系所

大氣物理研究所

論文名稱

雷達徑向風資料同化對降雨系統模擬之影響研究
(Effect of Doppler radial Velocity data assimilation on the simulation of different precipitation systems)

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摘要(中)

由於雷達擁有較高的空間與時間解析度，在台灣完整的雷達網聯之下，對於台灣地區降雨系統觀測會有很好的解析能力，尤其對於海上移入台灣的天氣系統或侵台颱風，能在系統尚未到達台灣之前，利用雷達觀測資料經由資料同化分析調整獲得較佳的模式初始場，用以改善模式預報天氣系統的準確度。本研究主要探討雷達徑向風資料同化對台灣區域中尺度氣象模式模擬各種降雨系統的影響。文中使用美國國家大氣研究中心(NCAR)所發展之中尺度氣象模式MM5以及三維變分資料同化系統(3D-VAR)，將雷達所觀測的都卜勒徑向風場經由資料同化調整分析場以獲得最佳的模式初始場，探討經雷達徑向風資料同化後的模式初始場對不同天氣型態的降雨系統模擬的影響。
由於三維變分資料同化對不同天氣系統的反應可能會有所不同，因此選擇了三種尺度大小差異較大的個案進行研究，包括冰雹對流系統、梅雨季鋒面降雨系統以及颱風。雹暴個案為一範圍僅幾十公里的劇烈對流個案，數值模式很難模擬出此系統的發展；梅雨鋒面個案為一有明顯水平風切線的天氣系統；颱風個案則是選擇2004年艾利颱風，此颱風經過台灣北部時，同時在北部山區造成劇烈降雨與災害。此三個案皆為由海上移近台灣的降雨系統，因此透過這三種天氣系統的模擬研究探討雷達徑向風資料同化對台灣地區降雨系統模擬的影響。雖然3D-VAR中所採用的平衡方程式會影響到熱力場與質量場，但是由同化後的增量分析結果中，氣溫與氣壓上的同化修正量很小，主要同化影響是反映在水平風場上，增量最大可達10-20 ms-1。另外經同化後的初始場對模擬的影響上，雖然在雹暴個案中雷達風資料同化不利於對流的發展，但在另外兩個天氣個案中的結果，徑向風同化對這兩種風場主導對流發展的個案的改善效果則很明顯。同化不僅改善風場結構與降雨分布的模擬，對颱風強度與颱風路徑的影響也很顯著，在此艾利颱風個案中可以修正颱風強度誤差約25 %。
另外在敏感度實驗設計上，針對分析場中有無雨水含量對資料同化修正風場的影響進行討論。雷達徑向速度除了由三維風場的分量所造成，亦會受雨滴的終端落速影響，因此分析場中是否有降雨的存在會影響到同化時對風場的調整。針對此問題，對本文選取的三個個案進行此同化敏感度實驗。結果顯示背景場的雨水含量對水平風場的同化調整影響，在雹暴個案中的差異超過1 ms-1，但在另外兩個水平風較強的個案中則對水平風的影響有限。另外敏感度實驗亦針對不同觀測資料進行同化測試，同化資料包括地面測站、探空資料、雷達徑向風及雙雷達合成水平風，討論不同類型的觀測資料同化對分析場中風場調整的差異以及對短時間預報的影響。不同觀測資料敏感度實驗結果中，探空資料、雷達徑向風與雙雷達合成風對颱風個案同化而言皆可加強其氣旋環流強度。而雙雷達風場在底層的風速修正效果是最好的，修正結果與實際探空觀測非常近似。雖然雷達徑向風對低層風速的修正效果不若雙雷達風，但其對風場對稱性結構調整比較顯著。

摘要(英)

Compared to conventional data, radar observations have an advantage of high spatial and temporal resolutions, and Doppler radars are capable of capturing detailed fow characteristics of rainfall systems. In addition, the high resolution radar observations can be used to retrieve three-dimensional mesoscale structures of dynamic and thermodynamic fields. In this study, the possible improvement of different kinds of weather systems predictions near Taiwan, particularly with regard to related rainfall forecasts, using Doppler radial wind observations is explored. Three cases of different precipitation systems was chosen for study, and a series of experiments and sensitivity tests were carried out using the Penn State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model Version 5 (MM5) with its three-dimensional variational (3D-VAR) data assimilation system.
In order to evaluate the impacts of Doppler radial velocity data assimilation, three different precipitation systems were chosed, include Hail strom, Mei-yu front and typhoon. These 3 weather systems have different dynamic and precipitation structures. Hail storm is small scale convection; Mei-yu front was observed a horizontal wind shear; Typhoon case had a large cyclonic circulation accompany heavy rainfall. The analyses of radar wind data assimilations were shown that the influences are slignt for pressure, vertical velocity and temperature. The major reponses of data assimilation are in the horizontal wind fields. Although the results are shown the Doppler wind data assimilation is worthless in the hail storm simulation, it is effective in other 2 convertive systems that are drived by dymanic structure. In the simulation results of Mei-yu and typhoon case, the horizontal wind structure and precipitation pattern were revised by date assimilation. The typhoon intensity also was increased and revised about 25 % errors from non-assimilation simulation.
Some sensitivity tests were demonstrated for the assimilation influences of rain water content and different observational data. The observational operator of Doppler radial velocity is related in terms of 3 dimensional wind and raindrop terminal velocity. The rain water content of background fields lead the deviation of horizontal wind in the data assimilation and the most deviations exceeded 1 ms-1. The dual-radar retrieval wind was also assimilated in one of the sensitivity experiments and was compared with Doppler radial wind for the verification of data assimilation impacts. The effects of dual-radar retrieval wind leads the best low level wind speed of typhoon over the land by the analysis of data assilation. The radar radial wind has the more symmetrical adjustment of dymanic structures than the dual-radar wind.

關鍵字(中)

★ 雷達徑向風
★ 資料同化
★ 3D-VAR

關鍵字(英)

★ 3D-VAR
★ data assimilation
★ Doppler radial velocity

論文目次

摘　要 I
ABSTRACT III
誌　謝 V
目　錄 VI
圖目錄 VIII
表目錄 XIV
第一章　前　言 1
1-1　研究動機 1
1-2　研究回顧 2
1-3　研究目的 4
第二章　研究方法 7
2-1　模式介紹 7
2-2　三維變分資料同化 9
2-3　觀測資料處理 12
2-4　雷達徑向風單點資料同化影響測試 14
第三章　雷達資料同化對雹暴與梅雨鋒面降雨模擬的影響 18
3-1　雹暴個案與實驗設計 18
3-2　雹暴同化模擬結果討論 19
3-3　梅雨鋒面個案與實驗設計 21
3-4　梅雨鋒面同化模擬結果討論 22
第四章　雷達資料同化對颱風模擬的影響 26
4-1　艾利颱風與實驗設計 26
4-2　資料同化分析 29
4-3　循環更新同化模擬結果討論 31
第五章　敏感度實驗 40
5-1　模式雨水含量對徑向風同化風場調整的影響 40
5-2　不同觀測資料對三維變分資料同化的影響 45
第六章　結　論 54
未來展望 58
參考文獻 59

參考文獻

Barker, D. M., W. Huang, Y.-R. Guo, and A. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note, 68 pp.
Barker, D. M., W. Huang, Y.-R. Guo, A. Bourgeois, and Q. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897–914.
Chen, S.-H., 2007: The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations. Mon. Wea. Rev., 135, 549-566.
Chen, S. Y., C. Y. Huang, Y. H. Kuo, Y. R. Guo and S. Sokolovskiy, 2009: Assimilation of GPS Refractivity from FORMOSAT-3/COSMIC Using a Nonlocal Operator with WRF 3DVAR and Its Impact on the Prediction of a Typhoon Event. Terr. Atmos. Oceanic Sci., 20, 133-154.
Chou, K.-H., and C.-C. Wu, 2008: Typhoon initialization in a mesoscale model-Combination of the bogused vortex and the dropwindsonde data in DOTSTAR. Mon. Wea. Rev., 136, 865-879.
Deardorff, J. W., 1972: arameterization of the planetary Boundary layer for use in general circulation models. Mon. Wea. Rev., 100, 93-106.
Fujiyoshi, T., T. Endoh, T. Yamada, K. Ksuboki, Y. Tachibana, and G. Wakahana, 1990: Determination of a Z–R relationship for snowfall using a radar and high sensitivity snow gauges. J. Appl. Meteor., 29, 147–152.
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, J., and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with simulated Doppler radar data. Mon. Wea. Rev., 136, 945-963.
Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457–469.
Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note, NCAR/TN-398+STR, 117 pp.
Grell, G. A., J. Dudhia, and D. R. Stauffer, 1995: A description of the fifth-generation Penn State/NCAR Mesoscale Model. NCAR Technical Note, 122 pp.
Hong, S. Y. and H. L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322-2339.
Hu M., and M. Xue, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699-721.
Hu M., and M. Xue, 2007: Impact of Configurations of Rapid Intermittent Assimilation of WSR-88D Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case. Mon. Wea. Rev., 135, 507-525.
Jian, G.-J., S.-L, Shieh, and J.A. McGinley, 2003: Precipitation simulation associated with Typhoon Sinlaku (2003) in the Taiwan area using the LAPS diabatic initialization for MM5. Terr. Atmos. Oceanic Sci., 14, 261-288.
Jorgenson, D. P., and P. T. Willis, 1982: A Z–R relationship for hurricanes. J. Appl. Meteor., 21, 356–366.
Jung. Y., M. Xue, G. Zhang and J. M. Straka, 2008: Assimilaton of simulated Ploarimetric Radar Data for a Convective Strom Using the Ensemble Kalman Filter. Part II: Impact of Polarimetric Data on Strom Analysis. Mon. Wea. Rev., 136, 2246-2260.
Liou, Y.-C., 2001: Toward deducing the 3-D thermodynamic structure from only wind fields - A validation study using simulated data set. Terr. Atmos. Oceanic Sci., 12, 15-38.
Liou, Y.-C. and I.-S. Luo, 2001: An investigation of the moving frame single-Doppler wind retrieval technique using Taiwan Area Mesoscale Experiment (TAMEX) low-level data, J. Appl. Meteo., 40, 1900-1917.
Montmerle, T., and C. Faccani, 2009: Mesoscale assimilation of radial velocities from Doppler radar in a pre-operational framework. Mon. Wea. Rev., 137, 1939-1953.
Ray, P. S., C. L. Ziegler, W. Bumgarner, and R. J. Serafin, 1980: Single- and multi-Doppler radar observations of tornadic storms. Mon. Wea. Rev., 108, 1607–1625.
Ray, P. S., B. C. Johnson, K. W. Johnson, J. S. Bradberry, J. J. Stephens, K. K. Wagner, R. B. Wilhelmson, and J. B. Klemp, 1981: The morphology of several tornadic storms on 20 May 1977. J. Atmos. Sci., 38, 1643–1663.
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., D. W. Flicker, and D. K. Lilly, 1991: Recovery of threedimensional wind and temperature fields from simulated single-Doppler radar data. J. Atmos. Sci., 48, 876–890.
Sun, J., 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.
Sun, J., 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.
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.
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.
Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 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.
Wu, C.-C., and Y.-H. Kuo, 1999: Typhoons Affecting Taiwan: Current Understanding and Future Challenges. Bull. Amer. Meteor. Soc., 80, 67-80.
Wu, C.-C., and K.-H. Chou, Y. Wang, and Y.-H. Kuo, 2006: Tropical cyclone initialization and prediction based on four-dimensional variational data assimilation. J. Atmos. Sci., 63, 2383-2395.
Wu, C.-C., K.-H. Chou, P.-H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa, 2007: The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR. Wea. Forecasting, 22, 1157-1174.
Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, E. Lim, Y.-R. Guo, D. M. Barker, 2005: Assimilation of Doppler radar observations with a Regional 3D-Var system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Met., 44, 768-78.
Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2007: An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Land fall. J. Appl. Meteor. Climat., 46, 14-22.
Xiao, Q., X. Zhang, C. Davis, J. Tuttle, G. Holland and. P. J. Fitzpatrick, 2009: Experiment of Hurricane Initialization with Airborne Doppler Radar Data for the Advanced Research Hurricane WRF (AHW) Model. Mon. Wea. Rev., 137, 2759-2777.
Xue M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework cased on the Ensemble Square Root Kalman Filter for evaluation the impact of data from radar networks on thunderstorm analysis and forecasting. J. Atmos. Oceanic technol., 23, 46-66.
Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observations on the convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238–1253.
Zhao, Q., and Y. Jin, 2008: High-resolution radar data assimilation for hurricane Isabel (2003) at landfall. Bull. Amer. Meteor. Soc., 89, 1355-1372.

指導教授

林沛練(Pay-Liam Lin)

審核日期

2011-7-26

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