博碩士論文 986201012 詳細資訊


姓名 高晟傑(Chen-Chieh Kao)  查詢紙本館藏   畢業系所 大氣物理研究所
論文名稱 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
(Impact of Idealized GPS Radio Occultation Refractivity in predicting a Heavy Rainfall Event with the WRF-Local Ensemble Transform Kalman Filter System)
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摘要(中) 隨著新一代的掩星觀測即將升空,如何利用同化技術有效地提升掩星觀測的使用效率以改善模式初始場乃至降水預報變成至關重要的課題。系集卡曼濾波器的背景場誤差協方差矩陣隨時間與流場而更動,故能比3DVAR更準確地評估背景動力的特性、更有效地使用觀測;且不似4DVAR需建立伴隨模式,因此便於實作。本研究利用WRF-LETKF(Local Ensemble Transform Kalman Filter)同化系統及觀測系統模擬實驗(OSSE)探討新一代掩星觀測對於中尺度同化系統及預報可能造成之影響,及如何利用系集同化技術更有效利用此觀測資料。
  在OSSE實驗中,虛擬真實場為一類似SoWMEX 2008 IOP-8(06/14~17)真實個案降水分布之長時間模式積分。實驗設計共有三組,分別為掩星折射率的觀測數量敏感度測試、觀測誤差敏感度測試與多變量相關性測試。實驗結果顯示,雖然未同化任何觀測的系集降水預報無論分布與強度皆與真實場大相逕庭,但經過1至2日的同化後,系集預報便能捕捉與真實場類似的降水狀況,尤其是同化了掩星折射率的實驗隨觀測數量的增加得以更確切地預報出真實場的降水分布與強度,並能延長其與風場、水氣場等的預報能力達6至24小時。實驗結果亦顯示當觀測較精確時,雖然水氣場得以大幅修正,但風場與溫度場的分析與預報反而可能隨折射率同化數量增加而變差。此結果反應出過度密集的觀測資料可能影響分析增量中的變數平衡。透過控制多變量誤差協方差,可限制掩星觀測之貢獻。在本研究中,如掩星觀測僅調整溫,濕度場而不進行風場調整,結果顯示當風場在中高層(2.5km以上)得以優於使用完整多變量誤差協方差之分析修正。此顯示在中高層,掩星觀測之高準確度對於動力場的修正較無正面幫助。
  從實驗結果來看,折射率的同化約需1至2天才能在模式中發揮效果(spin-up)。當觀測較精確時,大量的觀測反而可能透過實驗初期尚不成熟的多變量關係錯誤地修正背景場,但是經過適當地調整多變量關係的同化策略後此種窘境得以獲得改善。
摘要(英) In this study, we perform OSSE experiment to evaluate the impact of next-generation satellites GPS radio occultation for regional data assimilation and prediction, particularly focusing on an event with heavy rainfall in Taiwan. The evaluation is based on the accuracy of the analyses derived from the WRF-LETKF system and the following prediction.
  For the OSSE experiment, the natural run is a 3-day simulation with a rainfall distribution similar to a real case (SoWMEX 2008 IOP-8) from the strong convection of the Mei-Yu front. There are three sets of experiments, including the sensitivity for observation density and observation accuracy, and the impact of using a multivariate covariance. Results show that the ensemble forecast can well capture the rainfall distribution similar to truth after spin-up time of LETKF. With the RO refractivity (REF), the wind and water vapor forecasts can be improved with a leadtime from 6 to 24 hours. With more accurate REFs, the advantage of REF data is clearly identified with even a moderate observation density with a resolution of 300km. However, we notice that even though the water vapor can be improved with the accurate observations, the quality of the analysis and forecast for wind and temperature is degraded because of the unbalance between variables. When restricting the impact of REF data to only the water vapor and temperature fields, the wind analysis becomes more accurate at mid to upper troposphere than the one using the full multivariate correlations. But, the effect on improving the heavy rainfall prediction is less clear.
  According to this study, the WRF-LETKF assimilation system needs 1 to 2 days to spin up the impact of the REF data. The localization of the variables for the multivariate covariance can be a useful strategy to accelerate the impact of GPS RO data.
關鍵字(中) ★ 數值天氣預報
★ 資料同化
★ 系集卡曼濾波器
關鍵字(英) ★ NWP
★ data assimilation
★ EnKF
論文目次 中文摘要………………………………………………………………………… i
英文摘要………………………………………………………………………… ii
誌謝……………………………………………………………………………… iii
目錄……………………………………………………………………………… iv
圖表說明………………………………………………………………………… vi
第一章、前言
1.1  研究動機與目的………………………………………………… 1
1.2  文獻回顧………………………………………………………… 2
第二章、個案介紹
2.1  真實個案(2008年6月14日~16日LST)綜觀天氣簡介… 4
2.2  模擬個案介紹…………………………………………………… 6
第三章、模式與同化系統
3.1  模式系統………………………………………………………… 7
3.2  同化方法………………………………………………………… 8
3.3  觀測與觀測算符
  3.3.1  探空……………………………………………………… 9
  3.3.2  局地掩星觀測折射率…………………………………… 10
第四章、實驗設計
4.1  實驗設計………………………………………………………… 11
4.2  模擬個案簡介…………………………………………………… 11
4.3  同化實驗設定…………………………………………………… 12
4.4  驗證方法………………………………………………………… 13
第五章、實驗結果與討論
5.1  觀測數量敏感度測試…………………………………………… 15
  5.1.1  折射率觀測數量對於風場、溫度與水氣的影響………… 15
  5.1.2  折射率觀測數量對於降水預報的影響…………………… 15
5.2  觀測誤差敏感度測試……………………………………………… 16
  5.2.1  觀測誤差對於風場、溫度與水氣的影響………………… 16
  5.2.2  觀測誤差對於降水預報的影響…………………………… 17
5.3  多變量相關性測試………………………………………………… 18
5.4  個案分析…………………………………………………………… 19
第六章、總結與未來展望
  6.1  總結………………………………………………………………… 21
  6.2  未來展望…………………………………………………………… 22
參考文獻………………………………………………………………………… 23
附錄……………………………………………………………………………… 27
附表與附圖……………………………………………………………………… 31
參考文獻 呂佳龍,2010:同化GPS掩星及其他觀測資料對梅雨模擬之影響。國立中央大學,大氣物理研究所,碩士論文,118頁。
迮嘉欣,2009:資料同化對臺灣地區颱風和梅雨模擬之影響。國立中央大學,大氣物理研究所,碩士論文,81 頁。
陳舒雅,2008:GPS 掩星觀測資料同化及對區域天氣預報模擬之影響。國立中央大學,大氣物理研究所,博士論文,137 頁
Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634–642.
Anthes, R. A., C. Rocken, and Y.-H. Kuo, 2000: Applications of COSMIC to meteorology and climate. Terr. Atmos. Oceanic Sci., 11, 115-156.
Barker, D. M., W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results. Mon. Wea. Rev., 132, 897–914.
Bishop, C. H., B. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436.
Chen, C.-S., Y.-L. Chen, 2003: The Rainfall Characteristics of Taiwan. Mon. Wea. Rev., 131, 1323–1341.
——, Y.-L. Lin, W.-C. Peng, C.-L. Liu, 2010: Investigation of a heavy rainfall event over souwestern Taiwan associated with a subsynoptic cyclone during the 2003 Mei-Yu season. Atmospheric Research., 95, 235-254.
Chen, S.-Y., C.-Y. Huang, Y.-H. Kuo, S. Sokolovskiy, 2011: Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data, Mon. Wea. Rev., 139 , 53-865.
Chen, Y.-L., X.-A. Chen, Y.-X. Zhang, 1994: A Diagnostic Study of the Low-Level Jet during TAMEX IOP 5. Mon. Wea. Rev., 122, 2257-2284.
Cucurull, L., 2010: Improvement in the Use of an Operational Constellation of GPS Radio Occultation Receivers in Weather Forecasting. Wea. Forecasting, 25, 749–767.
Dudhia, J., 1989:Numerical study of convection observed during the winter monsoon experiment using a two-dimensional model. J. Atmos. Sci., 46, 3077-3107.
Healy, S. B., A. M. Jupp, and C. Marquardt, 2005: Forecast impact experiment with GPS radio occultation measurements. Geophys. Res. Lett., 32, L03804, doi:10.1029/2004GL020806.
——, and J.-N. Thepaut, 2006: Assimilation experiments with CHAMP GPS radio occultation measurements. Quart. J. Roy. Meteor. Soc., 132, 605–623.
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.
Huang, C.-Y., Y.-H. Kuo, S.-Y. Chen, C.-T. Terng, F.-C. Chien, P.-L. Lin, M.-T. Kueh, S.-H. Chen, M.-J. Yang, C.-J. Wang, and A. S. K. A. V. P. Rao, 2010: Impact of GPS radio occultation data assimilation on regional weather predictions. GPS Solut, 14, 35–49.
Hunt, E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–126.
Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
——, and ——, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, edited by K. A. Emanuel and D. J. Raymond, Amer. Meteor. Soc., 246 pp.
Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: "Variable localization" in an Ensemble Kalman Filter: application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110. doi:10.1029/2010JD014673.
Kuo, Y.-H., X. Zou, and W. Huang, 1997: The impact of GPS data on the prediction of an extra-tropical cyclone: An observing system simulation experiment. Dyn. Atmos. Oceans, 27, 439–470.
Liu, H., J. Anderson, Y.-H. Kuo, C. Snyder, and A. Caya, 2008: Evaluation of a Nonlocal Quasi-Phase Observation Operator in Assimilation of CHAMP Radio Occultation Refractivity with WRF, Mon. Wea. Rev., 136, 242-256.
——, ——, ——, and K. Raeder, 2007: Importance of forecast error multivariate correlations in idealized assimilation of GPS radio occultation data with the Ensemble Adjustment filter. Mon. Wea. Rev., 135, 173-185.
——, X. Zou, H. Shao, R. Anthes, J. Chang, J. Tseng, and B. Wang, 2001: Impact of 837 GPS/MET bending angle profiles on assimilation and forecasts for the period June 20-30, 1995. J. Geophys. Res., 106, 31 771–31 786.
Liu, J., H. Li, E. Kalnay, E. J. Kostelich, and I. Szunyogh, 2009: Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 137, 3918-3932.
Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Clim. Appl. Meteor., 22, 1065-1092.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated during TAMEX IOP 5. Mon. Wea. Rev., 122, 2257-2284.
Ninomiya, K., 2004: Large- and mesoscale features of Meiyu-Baiu front associated with intense rainfalls. East Asian Monsoons, edit. C.-P. Chang, World Scientific, 404-435.
Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415–428.
Wu, L., Y. Shao, A. Y. S. Cheng, 2011: A diagnostic study of two heavy rainfall events in South China. Meteorol. Atmos. Phys., 111, 13-25.
Yang, M.-J., F.-C. Chien, and M.-D. Cheng, 2000: Precipitation parameterization in a simulated Mei-Yu front. Terr., Atmos., and Oceanic Sci., 11, 393-422.
Yang, S.-C., E. Kalnay and T. Miyohsi, 2011: Improving EnKF spin-up for typhoon assimilation and prediction, Wea. Forecasting, under revision.
Zou, X., H. Liu, R. A. Anthes, H. Shao, J. C. Chang, and Y. J. Zhu, 2004: Impact of CHAMP radio occultation observation on global analyses and forecasts in the absence of AMSU radiance data. J. Meteor. Soc. Japan, 82, 533–549.
指導教授 楊舒芝(Shu-Chih Yang) 審核日期 2012-1-31
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