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姓名	陳勁宏(Chin-Hung Chen)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	不同微物理方案在雲可解析模式的系集預報分析: SoWMEX-IOP8 個案 (Analysis of Using Different Microphysics Schemes for the Cloud-Resolving Ensemble forecasts during SoWMEX-IOP8)
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摘要(中)	本研究的目的為了解不同微物理參數化方案(包括單矩量及雙矩量)在雲可解析模式對系集預報架構下系集離散度之影響及其特性。我們首先透過單一預報分析不同方案的基本特性，接著利用系集法分析不同方案在系集架構下的誤差結構並且探討這些方案對於同樣初始條件下的敏感度。我們選用 WRF 模式模擬西南氣流實驗期間 2008 年 6 月 16 日(SoWMEX-IOP8)在台灣 2 西南岸之豪大雨個案。在所有系集實驗中，系集初始場皆使用經由區域資料同化系統(WRF-LETKF)所得之分析系集。實驗共選用了四種不同的微物理方案來進行分析，包含 GCE、MOR、WSM6 和 WDM6 方案。 研究結果顯示雙矩量的微物理參數化方案(MOR, WDM6)並不一定能夠製造出比單矩量(GCE, WSM6)方案更大的系集離散度。而不同的微物理方案在水相粒子的分布上有著很大的差異，特別是在冰相粒子的部分又更為明顯。結果顯示在系集離散度的發展上 GCE 方案幾乎在每一個變數都有相對較大的離散度。主要是因為相比於其他方案，GCE 方案有著相對較強，且變動量大的垂直速度並又伴隨著非常有效率的相態轉換造成潛熱釋放的離散度增大。而這些微小尺度的放熱以及吸熱的過程又會進一步影響到更大尺度的溫度以及風場的離散度。雙矩量方案的效益在比較同一類型的微物理方(WSM6 和 WDM6)時才會顯現出來。WDM6 在所有變數上都有比 WSM6 大的離散度，尤其是在雨水以及雲水的部分，而即便兩方法使用相似的冰相設定，似乎雙矩量處理在暖雨過程所造成的離散度會進一步的影響到冰相變數的離散度。 因此，若以混成微物理法建立系集，本研究結果顯示混和 GCE 以及 WDM6方案將能比較有效的增加整體上的系集離散度以掌握不同方面的預報誤差。最後我們也發現不同發展者所設計的微物理方案最大的差異是在冰項微物理過程的處理上面，而這些差異會影響到不同的潛熱釋放特徵，進而再影響到更大尺度的溫度場以及風場。
摘要(英)	In this study, we aim to understand the effects of different microphysics (MP)schemes [including single- (SM) and double-moment (DM)] on the ensemble spread under the framework of ensemble forecasts in the cloud-resolving model. We first analyze the basic features of these schemes through the deterministic forecasts then using the ensemble method to focus on the comparison of the ensemble-based error structures and investigate the sensitivity of the initial conditions to different MP schemes. The simulation for the heavy rainfall event during the Southwest Monsoon Experiment on June 16, 2008 (SoWMEX-IOP8) is examined with the WRF model. In all ensemble experiments, the initial conditions are obtained from the regional data assimilation system (WRF-LETKF). Four different MP schemes were selected for analysis, including GCE, MOR, WSM6, and WDM6 schemes. Results show that the DM schemes (MOR, WDM6) do not necessarily produce a larger ensemble spread than the SM schemes (GCE, WSM6). Different MP schemes have great differences in the distribution of hydrometeors, especially with the icerelated variables. Results show that GCE has a relatively large spread in almost every variable. This is mainly because GCE has a relatively strong and variable vertical velocity associated with the efficient phase transition results in the spread of latent heat release increases and further affect the spread of larger-scale temperature and wind fields. The benefits of the DM scheme are only apparent when comparing similar MP schemes (WSM6, WDM6). Especially in the rainwater and cloud water, even if the two methods use similar ice processes, it seems that the spread caused by the DM treatment in the warm rain processes will further affect the spread of ice-related variables. Therefore, if the ensemble is established by the multi-MP method, the results of this study show that the combination of GCE and WDM6 schemes would be more effective in increasing the overall ensemble spread to represent the forecast error in different aspects. Finally, we found that the ice-related processes are handled very differently with different MP scheme developers and these difference will affect the pattern of latent heat release which in turn affect larger-scale temperature and wind fields.
關鍵字(中)	★ 微物理方案 ★ 系集預報 ★ 系集離散度	關鍵字(英)	★ Microphysics scheme ★ Ensemble forecast ★ Ensemble spread
論文目次	摘要 i Abstract ii Acknowledgment iii Outline iv Table vi Figures vi Chapter 1: Introduction 1 Chapter 2: Case overview 5 2.1 Synoptic overview 5 2.2 Evolution of convection and precipitation 6 2.3 Long-lived MCS mechanisms 6 Chapter 3: Experiment design 8 3.1 Initial condition 8 3.2 Model setup 8 Chapter 4: Microphysics schemes 10 4.1 Goddard scheme 11 4.2 WSM6 scheme 11 4.3 WDM6 scheme 12 4.4 Morrison scheme 13 Chapter 5: Results and discussion 14 5.1 Basic features of different schemes through the deterministic forecasts 14 5.1.1 Convective system and rainfall analysis 14 5.1.2 The characteristics of hydrometeor distribution 16 5.1.3 Analysis of microphysical process tendencies 18 5.2 Performance of ensemble forecasts 20 5.2.1 Evaluation based on the single state 21 5.2.2 The probabilistic analysis 22 5.3 Evaluate the ensemble spread through the ensemble method 23 5.3.1 The ensemble spread of microphysical variables 24 5.3.2 The ensemble spread of thermodynamic variables 25 5.3.3 The ensemble spread of dynamic variables 27 5.3.4 The impact of MP schemes on representing forecast error 28 Chapter 6: Conclusion and future work 30 6.1 Conclusion 30 6.2 Future work 32 References 33 Appendix 40 I. Probability matched ensemble mean (PMEM) 40 II. Neighborhood Ensemble Probability(NEP) 41
參考文獻	李志昕、洪景山，2011：區域系集預報系統研究：物理參數化擾動。大氣科學，39，95-116。 洪景山、曹嘉宏，2011：利用Cressman客觀分析法於網格化台灣自動雨量觀測資料之探討。大氣科學，39，201-214。 簡芳菁、洪玉秀，2010：梅雨季西南氣流氣候平均與個案之數值研究。大氣科學，38，237-267。 周俊宇，2012: 西南氣流實驗(IOP8個案)觀測分析與數值模擬：雲微物理結構特徵及參數法方案比較。中央大學大氣物理研究所碩士論文。 陳立昕， 2017: 利用系集法估計與檢驗對流尺度之預報誤差：SoWMEX IOP8 個案分析。中央大學大氣物理研究所碩士論文。 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。 Ancell, B. C., C. F. Mass and G. J. Hakim, 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 2008-2024. Chang, W.-Y., W.-C. Lee and Y.-C. Liou, 2015: The Kinematic and Microphysical Characteristics and Associated Precipitation Efficiency of Subtropical Convection during SoWMEX/TiMREX. Mon. Wea. Rev., 143, 317-340. Chung, K.-S., W. G. Chang, L. Fillion and M. Tanguay, 2013: Examination of situation-dependent background error covariances at the convective scale in the context of the ensemble Kalman filter. Mon. Wea. Rev., 141, 3369–3387. Davis, C. A. and W.-C. Lee 2012: Mesoscale Analysis of Heavy Rainfall Episodes from SoWMEX/TiMREX. J. Atmos. Sci., 69, 521-537. Dawson Ⅱ, D. T., M. Xue, J. A. Milbrandt and M. K. Yau, 2010: Comparison of Evaoration and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms. Mon. Wea. Rev., 138, 1152–1171. Dowell, D. C., L. J. Wicker and 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. Duda, J. D., X. Wang, F. Kong and M. Xue, 2014: Using Varied Microphysics to Account for Uncertainty in Warm-Season QPF in a Convection-Allowing Ensemble. Mon. Wea. Rev., 142, 2198-2219. Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480. Ebert, E. E., 2008: Fuzzy verification of high-resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 51–64. Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610–2627. ──, ──, and ──, 2004: Precipitation and Evolution Sensitivity in Simulated Deep Convective Storms: Comparisons between Liquid-Only and Simple Ice and Liquid Phase Microphysics. Mon. Wea. Rev., 132, 1897–1916. Tapiador, F. J., W.-K. Tao, J. J. Shi, C. F. Angelis, M. A. Martinez, C. Marcos, A. Rodriguez, A. Hou, 2012: A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain. J. Appl. Meteor., 51, 489-504. Hacker, J. P. and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screenheight observations in a parameterized PBL. Mon. Wea. Rev., 133, 3260-3275. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysics scheme. Mon. Wea. Rev., 132, 103–120. ──, and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme. Journal of the Korean Meteorological society, 42, 129-151. Houtekamer, P. L., L. Lefaivre, and J. Derome, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–1242. Jacques, D., W. G. Chang, S. J. Baek, T. Milewski, L. Fillion, K. S. Chung and H. Ritchie, 2017: Developing a convective-Scale EnKF data assimilation system for the Canadian MEOPAR Project. Mon. Wea. Rev., 145, 1473-1494. Jones, T. A. and D. J. Stensrud, 2015: Assimilating Cloud Water Path as a Function of Model Cloud Microphysics in an Idealized Simulation. Mon. Wea. Rev., 143, 2052–2081. 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. Lin, Y.-L, R. D. Farley, and H. D. Orville, 1983: Bulk parametrization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065-1092. Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165-166. Milbrandt, J. A., and M. K. Yau, 2005a: A multimoment bulk microphysics parameter-ization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051–3064. ——, and ——, 2005b: A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 3065–3081. Min, K.-H., S. Choo, D. Lee and G. Lee, 2015: Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations. Weather Forecast, 30, 1571-1589. Morrison, H., J. A. Curry and V.I. Khvorostyanov, 2005: A New Double-Moment Mi-crophysics Parameterization for Application in Cloud and Climate Models. Part Ⅰ: Description. J. Atmos. Sci., 62, 1665-1677. ──, G. Thompson and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing straitiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 991–1007. ──, and J., Milbrandt, 2011: Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations. Mon. Wea. Rev., 139, 1103–1130. Poterjoy, J. and F. Zhang, 2011: Dynamics and Structure of Forecast Error Covariance in the Core of a Developing Hurricane. J. Atmos. Sci., 68, 1586-1606. Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2014: The Analysis and Pre-diction 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., 141, 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, 2017: Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar DA. Mon. Wea. Rev., 145, 2257-2279. Rowe, A. K., S. A. Rutledge, and T. J. Lang, 2011: Investigation of Microphysical Processes Occurring in Isolated Convection during NAME. Mon. Wea. Rev., 139, 424-443. Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the ‘‘seeder-feeder’’ process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185–1206. Snyder, C. and F. Zhang, 2003: Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter. Mon. Wea. Rev., 131, 1663-1677. Tao, W.-K., J. Simpson and M. Mccumber, 1989: An Ice-Water Saturation Adjustment. Mon. Wea. Rev., 117, 231-235. ──, and ──, 1993: Goddard Cumulus Ensemble Model. Part Ⅰ: Model Description. Terr. Atmos. Oceanic Sci., 4, 19-54. ──, and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137. ──, J. J. Shi, S. S. Chen, S. Lang, P.-L Lin, S.-Y Hong, C. P.-Lidard and A. Hou, 2011: The Impact of Microphysical Schemes on Hurricane Intensity and Track. Asia-Pacific J. Atmos., 47, 1-16. Tapiador, F. J., W.-K. Tao, J. J. Shi, C. F. Angelis, M. A. Martinez, C. Marcos, A. Rodriguez and A. Hou, 2012: A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain. J. Appl. Meteor., 51, 489-504. 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. ──, Y. L. Chen, S. Y. Chen, Y. H. Kuo and P. L. Lin, 2017: Impacts of Including Rain Evaporative Cooling in the Initial Conditions on the Prediction of a Coastal Heavy Rainfall Event during TiMREX. Mon. Wea. Rev., 145, 253-277. 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. ── and ──, 2015: Convective Intensity, Vertical Precipitation Structures, and Microphysics of Two Contrasting Convective Regimes During the 2008 TiMREX. J. Geophys. Res. Atmos., 120, 4000-4016. Xue, M., Y. Jung and G. Zhang, 2010: State Estimation of Convective Storms with a Two-Moment Microphysics Scheme and an Ensemble Kalman Filter: Experiments with Simulated Radar Data. Q. J. R. Meteorol. Soc., 136, 685-700. Yang, S.-C., S.-H. Chen, S.-Y. Chen, C.-Y. Huang and 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, 4139-4163. Zhang, F., 2002: Mesoscale Predictability of the “Surprise” Snowstorm of 24-25 Janu-ary 2000. Mon. Wea. Rev., 130, 1617-1632. ──, C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185. ──, 2005: Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev., 133, 2876-2893.
指導教授	鍾高陞 楊舒芝(Kao-Shen Chung Shu-Chih Yang)	審核日期	2018-7-30
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