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姓名 邵彥銘(Yan-ming Shao)  查詢紙本館藏   畢業系所 大氣物理研究所
論文名稱 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善短期定量降雨預報: SoWMEX IOP8 個案分析
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摘要(中) 本研究使用一套結合局地系集轉換卡爾曼濾波器(LETKF)與天氣研究預報模式(WRF),同化多座都卜勒雷達資料,針對2008年西南氣流密集觀測實驗(IOP8)的兩個個案做分析與模擬,這兩個個案(0614、0616)分別都對台灣南部帶來龐大降雨,而本研究的實驗目的為探討此雷達資料同化系統是否能夠改善梅雨個案的短期預報降雨能力以及改善的程度。

在0614的個案裡,首先比較在觀測使用0dbz與沒使用0dbz的同化實驗,而結果顯示,在沒有雷達回波的位置補上0dbz能夠有效的壓制模式中錯誤的回波生成,且不會降低主要雨帶的降雨強度。而同化實驗長度由一小時加長為兩小時後降雨預報在預報的最後幾個小時比其他實驗都好,但是在前期受到EnKF起轉問題而有低估降雨的現象,對此,若以隨機擾動採樣進行EnKF初始化較能展現出梅雨時期的大尺度不確定性,亦較有利於同化。

在0616的個案裡,由於在台灣東部非雷達觀測區域的初始擾動偏濕,而使得實驗結果在此區域有較大的濕偏差。若直接使用ECMWF再分析資料所做的單一預報此偏差情形並沒有那麼嚴重,比對再分析資料後發現,水相粒子在擾動後起轉的分布情形與再分析場的濕度有關,由於此個案的再分析場濕度較大,濕偏差的情形也較嚴重,而此現象也因無觀測而有誤差持續累積的情形。但台灣西南方的降雨一樣有較好的估計,因此綜合兩個個案的實驗結果,使用WRF-LETKF雷達資料同化系統能有效改善梅雨的定量降雨預報結果。
摘要(英) The Local Ensemble Transform Kalman Filter (LETKF) method, coupled with Weather Research and Forecast (WRF) model, is applied to assimilate data from five Doppler radars in Taiwan, with the purpose of investigating the improvement on short-term quantitative precipitation forecast (QPF) for rainfall events occurred during the Mei-Yu season. Two heavy precipitation cases from the 2008 SoWMEX IOP#8 field experiments are selected.

The overall results demonstrate that by using WRF-LETKF to assimilate the radar data, the performance of model QPF for representing the Mei-Yu rainfall can be significantly improved. In the first case of June 14, 2008, it is found that by assimilating the 0 dBZ data, the spurious convection can be effectively suppressed. Extending the length of the radar data assimilation to two hours produces better rainfall forecast results. Generating initial perturbations from randomly selected, 6-hr apart data from the NCEP 1ox1o re-analysis data turns out to be a better way to capture the uncertainty related to the Mei-Yu frontal flow than the original NCEP NMC method does.

The same model setup and assimilation method is applied to the second event on June 16, 2008. The pattern and amount of the forecasted rainfall pattern and over southwestern Taiwan indicates a very encouraging result. However, the rainfall prediction over eastern Taiwan becomes unrealistic strong, and this over-estimation cannot be mitigated due to the lack of radar data in this area. This indicates the importance of having a complete radar coverage over Taiwan and vicinity area.
關鍵字(中) ★ WRF-LETKF雷達資料同化系統
★ SoWMEX IOP8
關鍵字(英) ★ SoWMEX IOP8
論文目次 目錄
摘要 I
Abstract II
致謝 III
目錄 IV
圖表目錄 VI
一、 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 IOP8 6月14日個案介紹 3
二、 研究方法 5
2-1 LETKF資料同化方法 5
2-2 Weather Research and Forecasting Model 7
2-3 雷達觀測算符 8
2-4 雷達資料處理與超級觀測化 9
2-5 觀測資料中補0dbz 11
三、 實驗設計 12
3-1 實驗長度與策略 12
3-2 同化結果之診斷分析方式 13
3-3 降雨驗證方法與分數 14
四、 IOP8 6月14日 結果討論 16
4-1 NoDA與DA實驗之比較 16
4-2 同化零回波與沒同化零回波之比較 16
4-3加長同化實驗長度之結果討論 19
4-4 PM(Probability Matched mean)降雨估計法 21
4-5使用隨機擾動製作背景場之結果討論 23
4-6降雨分數之驗證 26
五、 IOP8 6月16日 結果討論 29
5-1 IOP8 0616個案介紹 29
5-2 實驗設計 29
5-3 分析結果之討論 30
5-4 降雨結果之討論 31
六、 結論與未來展望 33
6-1 結論 33
6-2 未來展望 35
參考文獻 37
附表 42
附圖 44
參考文獻 陳尉豪與廖宇慶,2012: 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。
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。
高晟傑,2012: 利用WRF-LETKF同化系統探討掩星折射率觀測對於強降水事件預報之影響。國立中央大學大氣物理所碩士論文,1 – 70。
邱健倫,2013: 使用氣象雷達改善對流尺度定量降水預報研究 – 理想與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。
蔡直謙,2014: 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善定量降水即時預報:莫拉克颱風(2009)。國立中央大學大氣物理所博士論文,1 – 71。
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。
Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observation. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805-1824.
Aksoy, A., D. C. Dowell, and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter of assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 1273-1292.
Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
Anderson, J. L., 2006: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A, 59, 210-224.
Anderson, J. L., 2009: Spatially varying adaptive covariance inflation for ensemble filters. Tellus A, 61, 72-83.
Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, 3081-3094.
Dowell, D. C., F. Zhang, L. J. Wicher, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982-2005.
Dowell, D. C., L. J. Wicher, C. Snyder, 2011: Ensemble kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma city supercell: influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272-294.
Ebert, Elizabeth E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461-2480.
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo method to forecast error statistics. J. Geophys. Res., 99, 10143-10162.
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.
Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon. Wea. Rev., 139, 511-522.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D, 230, 112-126.
Jung, Y., G. Zhang, and M. Xue, 2008a: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operator for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228-2245.
Kalnay, E., H. Li, T, Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: 4-D-Var or ensemble Kalman filter? Tellus A, 59, 758-773.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snew field in a cloud model. J. Clomate Appl. Meteor., 22, 1065-1092.
Lindskog, M., .K. Salonen, H. Ja ̈rvinen, and D. B. Michelson, 2004: Doppler radar wind data assimilation with HIRLAM 3DVAR. Mon. Wea. Rev., 132, 1081-1092.
Liou, Y.-C., 2001: The derivation of absolute potemtial temperature perturbations and pressure gradients from wind measurements in three-dimensional space. J. Atmos. Oceanic Technol., 18, 577-590
Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519-1535.
Snyder, C. and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with and ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677.
Sun, J., 2005: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, 793-813.
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.
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.
Tsai, C.-C., S.-C. Yang, and Y.-C. Liou 2014: Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radardata asiimilation system: Observing system simulation experiments. Tellus A, 66, 21804, doi: 10.3402/tellusa.v66.21084.
Whitaker, J. S. and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y. and Toth, Z. 2008, Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463-481.
Yang, S.-C., S.-H. Chen, S.-Y. Chen, C.-Y. Huang, C.-S. Chen, 2014: Evaluating the impact of the COSMIC RO bending angle data on predicting the heavy precipitation episode on 16 June 2008 during SoWMEX IOP8. Mon. Wea. Rev., 142:11, 4139-4163.
Zhang, F., C. Snyder, and J. Sun, 2004: Impact of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238-1253.
Zhang, F., Z. Meng, and A. Aksoy, 2005: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: perfect model experiments. Mon. Wea. Rev., 134, 722-736.
Zhang, F., Z. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: imperfect model experiments. Mon. Wea .Rev, 135, 1403-1423.
Zhang, F., Z. Meng, and A. Aksoy, 2007: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: comparison with 3DVAR in a real-data case study. Mon. Wea .Rev, 136, 522-540.
Zhang, F., Z. Meng, and A. Aksoy, 2008: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: comparison with 3DVAR in a month-long experiment. Mon. Wea .Rev, 136, 3671-3682.
Zhang, F., Y. Wang, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105-2125.
指導教授 廖宇慶、楊舒芝 審核日期 2015-8-7
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