以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：18

、訪客IP：3.138.118.250

姓名	游承融(Cheng-Rong You)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	利用雙偏極化雷達觀測資料進行極短期天氣預報評估─2008年西南氣流實驗IOP8期間颮線系統個案 (Evaluating the Performance of Very Short-term Forecast by Dual-Polarimetric Radar Observations ─2008 SoWMEX-IOP8 Squall Line Case)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	本研究使用雙偏極化雷達資料模擬器(polarimetric radar data simulator, PRDS)將模式輸出轉換成雙偏極化雷達參數後，與NCAR S波段雙偏極化雷達之雙偏極化雷達參數進行分析場與極短期數值天氣預報的比較。此外，使用Goddard、WSM6、WDM6和Morrison四種雲微物理參數化方案進行模擬。進行短預報前，使用WRF-LETKF雷達資料同化系統同化雷達回波與徑向風，以獲取最佳分析場。目的在於掌握較佳雲動力結構後，利用雙偏極化雷達參數驗證數值模式在雲微物理方面的表現，並了解不同雲微物理參數化方案對於雙偏極化雷達參數之模擬狀況。 　　經資料同化後，系集平均分析場雨帶的強度與分佈和雷達觀測結果相近。後續則利用單一決定性預報與系集平均場短期預報進行同化效益的討論，以傳統列聯表分數之累積雨量校驗結果顯現同化後結果較佳。此外，利用contoured frequency by altitude diagrams (CFADs)進行系集平均分析場與每小時之累積預報結果，回波在四個方案中的CFAD分布和觀測相似，其中單矩量的Goddard和WSM6更接近觀測。而差異反射率在單矩量模擬中，表現較佳，但四種方案皆有高估的狀況。比差異相位差則是在對流區的掌握度較好，層狀區的改進差異不大。模式預報後第一小時結果與觀測相比後，顯示仍能維持雷達參數良好的垂直分布狀況，但隨著預報時間拉長至第三小時，垂直分布結果已和觀測大相逕庭，與未同化之單一決定性預報之分佈無異。研究結果顯示，利用雙偏極化雷達參數驗證模式表現，可以了解模式在雲微物理過程的掌握能力外，亦可了解到雷達資料同化的效益與同化傳統雷達資料對於雙偏極化雷達參數的影響。此外，也顯示同化雙偏極化雷達參數之必要，並可提供模式更佳的初始場。
摘要(英)	In this research, a polarimetric radar data simulator (PRDS) is used to validate analysis results and short-term forecast outputs that have been converted to polarimetric radar data by comparing with the NCAR S-Pol dual-pol parameters. The WRF-LETKF system is utilized to assimilate radar reflectivity and Doppler wind to obtain the optimal analysis. And then the analysis is used as the initial condition of short-term forecasts. In addition, four different microphysics parameterization schemes are used in the study. After making sure the analysis resembles to true atmospheric state, we try to use polarimetric radar data to validate model performance, especially focusing on microphysics processes. Additionally, we also can understand the difference among microphysics schemes when it comes to simulating polarimetric radar data. The analysis fields quite resemble to the observation. The traditional forecast skill scores prove that the short-term forecasts are much better after radar data assimilation. By examining contour frequency by altitude diagrams (CFADs), results of reflectivity (Z_H) are improved in four schemes. The single moment schemes such as Goddard and WSM6 perform better than the other two. The improvements of differential reflectivity (Z_DR) are obvious in single moment simulations, but all four schemes are overestimate the value. And specific differential phase (K_DP) distributions are better in convective areas. The first-hour forecast agrees with the observation, and the general vertical structure can be held well. But after a three-hour forecast, there is no resemblance between observation and model. To sum up, we can know that polarimetric radar data can help us validate the model and impact on polarimetric data when assimilating transitional radar data. Last, It is shown that polarimetric radar data are necessary to be assimilated for providing better analysis fields.
關鍵字(中)	★ 模式驗證 ★ 雙偏極化雷達參數 ★ 偏極化雷達資料模擬器 ★ 微物理參數化方案 ★ 資料同化	關鍵字(英)	★ model validation ★ dual polarimetric radar data ★ polarimetric radar data simulator ★ microphysics parameterization ★ data assimilation
論文目次	摘要 i Abstract ii 致謝 iii 圖目錄 vi 表目錄 ix 一、緒論 1 1-1 前言 1 1-2 文獻回顧 2 1-3 研究目的 5 二、 SoWMEX IOP8個案檢視與文獻回顧 6 2-1 研究個案介紹 6 2-2 IOP#8 相關文獻回顧 7 三、模式設定與資料同化策略 8 3-1 研究模式與介紹 8 3-2 同化系統介紹 9 3-3 雷達資料使用與處理 11 四、總體微物理方案簡介 13 五、雙偏極化雷達觀測資料模擬器 16 5-1 概念介紹 16 5-2 敏感度測試 19 六、校驗方式 21 6-1累積降雨校驗分數計算 21 6-2方均根誤差(root mean square error) 22 6-3 CFAD (Contoured Frequency by Altitude Diagram) 23 七、實驗結果與討論 24 7-1 同化前後回波場分布與傳統分數校驗 24 7-2　CFAD比較─分析場表現 27 7-3　CFAD比較─模式預報場表現 30 7-4 CFAD在層狀與對流區的比較 32 7-5 以Morrison方案討論同化變數調整的差異 34 八、結論與未來展望 36 參考文獻 39 附圖 44 附表 89
參考文獻	邵彥銘，2015：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，國立中央大學大氣物理所碩士論文，　　1-78頁。 陳薇鈞，2011：2008年西南氣流實驗IOP8雷達折射指數場特性之研究。國立中央大學大氣物理所碩士論文，1-73頁。 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頁。 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　　頁。 鄧詠霖，2015：利用雷達觀測與反演變數改善模式定量降水預報之能力－2008　　年西南氣流實驗IOP#8個案分析。國立中央大學大氣物理所碩士論文，1-95頁。 鄭翔文，2017：雷達資料同化於多重尺度天氣系統(梅雨)的強降雨預報影響：　　SoWMEX IOP#8個案研究國立中央大學大氣物理所碩士論文，1-68頁。 盧可昕，2018：利用雙偏極化雷達及雨滴譜儀觀測資料分析2008年西南氣流實驗期間強降雨事件的雲物理過程。國立中央大學大氣物理所碩士論文，1-91頁。 Augros, C, Caumont, O, Ducrocq, V, Gaussiat, N. Assimilation of radar dual polarization observations in the AROME model. Q J R Meteorol Soc. 2018; 144: 1352– 1368. Dawson, D.T., M. Xue, J.A. Milbrandt, and M.K. Yau, 2010: Comparison of Evaporation and Cold Pool Development between Single-Moment and Multi-moment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms. Mon. Wea. Rev., 138, 1152–1171 ——, E.R. Mansell, Y. Jung, L.J. Wicker, M.R. Kumjian, and M. Xue, 2014: Low Level ZDR Signatures in Supercell Forward Flanks: The Role of Size Sorting and Melting of Hail. J. Atmos. Sci., 71, 276–299, 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. Hong, Song–You, Jimy Dudhia, and Shu–Hua Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120. 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. Johnson, M., Y. Jung, D. Dawson, and M. Xue, 2016: Comparison of simulated polarimetric signatures in idealized supercell storms using two-moment bulk microphysics schemes in WRF. Mon. Wea. Rev., 144, 971–996, Jung, Y., G. Zhang, and M. Xue, 2008: Assimilation of simulated polarimetric radar data for a convective storm using ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228–2245. Jung, Y., M. Xue, G. Zhang, and J.M. Straka, 2008: Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part II: Impact of Polarimetric Data on Storm Analysis. Mon. Wea. Rev., 136, 2246–2260, ——, ——, and G. Zhang, 2010: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. J. Appl. Meteor. Climatol., 49, 146–163. ——, ——, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 1457–1475. Kumjian, M.R. and A.V. Ryzhkov, 2008: Polarimetric Signatures in Supercell Thunderstorms. J. Appl. Meteor. Climatol., 47, 1940–1961, ——, C.P. Martinkus, O.P. Prat, S. Collis, M. van Lier-Walqui, and H.C. Morrison, 2019: A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics. J. Appl. Meteor. Climatol., 58, 113–130, Lim, K.–S. S., and S.–Y. Hong, 2010: Development of an effective double–moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612. Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051–3064. Min, K., S. Choo, D. Lee, and G. Lee, 2015: Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations. Wea. Forecasting, 30, 1571–1589, Morrison, H., G. Thompson, V. Tatarskii, 2009: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One– and Two–Moment Schemes. Mon. Wea. Rev., 137, 991–1007 ──, and Jason A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287-311. Pfeifer, M., G. C. Craig, M. Hagen, and C. Keil, 2008: A polarimetric radar forward operator for model evaluation. J. Appl. Meteor. Climatol., 47, 3202–3220 Putnam, B.J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2014: The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter. Mon. Wea. Rev., 142, 141–162 ——, ——, ——, G. Zhang, and F. Kong, 2017: Simulation of Polarimetric Radar Variables from 2013 CAPS Spring Experiment Storm-Scale Ensemble Forecasts and Evaluation of Microphysics Schemes. Mon. Wea. Rev., 145, 49–73 ——, ——, ——, N. Snook, and G. Zhang, 2019: Ensemble Kalman Filter Assimilation of Polarimetric Radar Observations for the 20 May 2013 Oklahoma Tornadic Supercell Case. Mon. Wea. Rev., 147, 2511–2533, Ryzhkov, A. V., M. Pinsky, A. Pokrovsky, and A. P. Khain, 2011: Polarimetric radar observation operator for a cloud model with spectral microphysics. J. Appl. Meteor. Climatol., 50, 873– 894, Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G Duda, X.- Y. Huang, W. Wang, and J. G. Powers, 2008: A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. 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., 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. Tsai, C. C. ,Y. Jung, 2017:Sensitivities of Very Short-Term Numerical Prediction to Polarimetric Radar Data Assimilation: Typhoon Soudelor (2015) 38th Conference on Radar Meteorology. ——, S.-C. Yang, and Y.-C. Liou 2014: Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: Observing system simulation experiments. Tellus A, 66, 21804, Tu, C. C., Y. L. Chen, C. S. Chen, P. L. Lin and P. H. Lin, 2014: A comparison of two heavy rainfall events during the Terrain-Influenced Monsoon Rainfall Experiment(TiMREX) 2008. Mon. Wea. Rev., 142, 2436-2463. Xu, W., E. J. Zipser, Y.-L. Chen, C. Liu, Y.-C Liou, W.-C. Lee, and B. J.-D. Jou, 2012: An Orography-associated extreme rainfall event during TiMREX: Initiation, Storm Evolution, and Maintenance, Mon. Wea. Rev., 140, 2555-2574.
指導教授	鍾高陞 蔡直謙(Kao-Shen Chung Chih-Chien Tsai)	審核日期	2019-7-29
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare