博碩士論文 104621004 詳細資訊




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姓名 陳立昕(Li-Hsin Chen)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 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
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摘要(中) 本研究採用系集法(Ensemble-based method),首次在台灣藉由天氣個案模擬,針對
對流尺度之預報誤差結構進行分析,個案選取 2008 年西南氣流聯合觀測實驗
(SoWMEX IOP8)期間,於 06 月 15 至 16 日之間生成之中尺度對流系統(Mesoscale
Convective Systems, MCSs),使用 WRF 單向巢狀網格,高解析度(3-km)之網格涵蓋台
灣本島與台灣海峽,以及部分巴士海峽。以 72 組系集預報結果作為樣本,運用統計方
法估算出背景(預報)誤差協方差(Background Error Covariance),其提供我們瞭解系集卡
爾曼濾波器(EnKF),在同化應用上之索引,藉此推斷觀測資料在同化期間,資訊導入
與傳遞的情況。
在 MCSs 影響期間,比較不同解析度(9, 3-km)之誤差結構,顯示高解析度的方差
量值較大,特別是垂直速度有最顯著的差異,高解析度能夠顯現較小尺度的不確定性。
在方差的時間序列中,方差隨著降雨率的強度增強而增加,降雨率減弱而趨緩。水平
風的方差結構中,擁有多重尺度作用的複雜結構,較小尺度的不確定性是由濕對流過
程(moist processes)產生;此外,降雨過程產生的冷池影響近地表溫度的方差。本研究
同時探討時間與空間上的誤差相關性,其亦受到濕對流過程影響。在交相關的部分,
溫度與垂直速度之正相關結構與潛熱釋放有關,低層之水平風 U 與 V,在台灣西南部
出現負相關之特徵,為西南氣流與地形交互作用產生。本研究對於預報誤差結構的評
估,期望能夠提供資料同化有利的策略,進而改進台灣地區雷達資料同化能力。
摘要(英) This study focus on the short-term forecast error structures with different resolution (9/
3 km) at meso- and convective scales. With a set of 72-member ensemble forecasts by
Weather Research and Forecasting (WRF) model, the error covariances are presented. A case
study during Southwest Monsoon Experiment intensive observing period 8 (SoWMEX-IOP8)
in 2008 is investigated. The characteristics of forecast error covariances are examined by the
spread and error correlation in state variables.
Compared with different resolution, the variance of state variables are larger in higher
resolution, particularly in vertical velocity. It indicates higher resolution run (3-km) can
better-represent the smaller scale uncertainties in this severe weather event. The time-series
of ensemble spread reveal that significant variances are associated with the strength of rainfall
rate. In the magnitude of horizontal wind, multi-scale interactions are found over the
southwesterly flow region. The temperature near surface has relatively large quantity of
variance in association with cold pool performances. Moist processes not only impact on the
distribution of variance, but paly important role of error correlation in temporal and spatial
structure. The cross correlation of the temperature and vertical velocity is strongly positive at
high level dominated by latent heat release. The negative cross correlation between zonal and
meridional wind over southwest quadrant of Taiwan illustrates the range affected by
orography. The information of forecast error provides optimal strategies of data assimilation,
especially for assimilating radar network over Taiwan area.
關鍵字(中) ★ 預報誤差協方差
★ 中尺度對流系統
關鍵字(英) ★ Forecast Error Covariance
★ Mesoscale Convective Systems
論文目次 目錄

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖表目錄 vii
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究目的 4
第二章 研究方法 5
2-1 資料同化之基本概念 5
2-1-1 原理介紹 5
2-1-2 誤差協方差之重要性說明 7
2-2 預報誤差之評估方法 8
2-2-1 方差 8 v

2-2-2 誤差相關係數 9
2-3 模式系統 9
2-4 初始系集之取得 10
第三章 個案概述 12
3-1 綜觀天氣分析 12
3-2 中尺度對流系統(Mesoscale Convective Systems, MCSs) 13
第四章 預報誤差結構分析 14
4-1 降雨模擬結果 14
4-2 方差結構 15
4-2-1 動力與熱力變數(水平風速、垂直速度、溫度與比濕) 15
4-2-2 水象變數( ??、??、??、?? 與 ?? ) 19
4-3 誤差相關性之結構 20
4-3-1 降雨、非降雨與層狀降雨區自相關比較 20
4-3-2 時間延遲自相關性 21
4-3-3 溫度誤差自相關性之垂直分布 21
4-3-4 雨水混合比誤差相關性 22
4-3-5 誤差交相關(cross correlation) 22 4-4 對流尺度預報誤差與資料同化效益之探討 23
第五章 總結與未來展望 25
5-1 總結 25
5-2 未來展望 26
參考文獻 27
附錄 32
I 卡爾曼濾波器 32
II 系集卡爾曼濾波器 33
III 局地化系集轉換卡爾曼濾波器 33
附表 36
附圖 37
參考文獻 曾忠一,2006:
大氣科學中的反問題
,國立編譯館主編,鼎文書局,台北市,
1288 頁。
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

,38,237-267。
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,
78 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,71 頁。
Aksoy, A., D. C. Dowell and C. Snyder, 2010: A multicase comparative assessment of
the ensemble Kalman filter for assimilation of radar observations. Part II: Short-
range ensemble forecasts. Mon. Wea. Rev., 138, 1273-1292.
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.
Bishop, C. H., and D. Hodyss, 2007: Flow-adaptive moderation of spurious ensemble
correlations and its use in ensemble-based data assimilation. Quart. J. Roy. Meteor.
Soc., 133, 2029–2044.
Bouttier, F., 1994: A dynamical estimation of forecast error covariances in an
assimilation system. Mon. Wea. Rev., 122, 2376-2390.
Brousseau, P., L. Berre, F. Bouttier and G. Desroziers, 2011: Background-error
covariances for a convective-scale data-assimilation system: AROME-France 3D-
Var. Quart. J. Roy. Meteor. Soc., 137, 409-422.
Caron, J. F. and L. Fillion, 2010: An examination of background error correlations
between mass and rotational wind over precipitation regions. Mon. Wea. Rev., 138,
563-578.
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.
Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon
experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–
3107.
——, 1996: A multilayer soil temperature model for MM5. Preprints, Sixth PSU/NCAR
Mesoscale Model Users’ Workshop, Boulder, CO, PSU/NCAR, 49–50.
Fabry, F. and J. Z. Sun, 2010: For how long should what data be assimilated for the
mesoscale forecasting of convection and why? Part I: On the propagation of initial
condition errors and their implications for data assimilation. Mon. Wea. Rev., 138,
242-255.
Grell, G. A. and D. Dévényi, 2002: A generalized approach to parameterizing
convection combining ensemble and data assimilation techniques. Geophy. Res.
Lett., 29.
Hacker, J. P. and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screen-
height observations in a parameterized PBL. Mon. Wea. Rev., 133, 3260-3275.
Hollingsworth, A., and P. L ö nnberg, 1986: The statistical structure of short-range
forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, 38A, 111–136.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an
explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.
Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A
system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–
1242.
——, and S. E. Sand H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman
filter technique. Mon. Wea. Rev., 126, 796-811.
——, and F. Q. Zhang, 2016: Review of the ensemble Kalman filter for atmospheric
data assimilation. Mon. Wea. Rev., 144, 4489-4532.
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.
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.
Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability.
Cambridge Univ. Press, Cambridge, England, 341 pp.
L ö nnberg, P., and A. Hollingsworth, 1986: The statistical structure of short-range
forecast errors as determined from radiosonde data. Part II : The covariance of
height and wind errors. Tellus, 38A, 137-161.
M é n é trier, B., T. Montmerle, L. Berre and Y. Michel, 2014: Estimation and diagnosis
of heterogeneous flow-dependent background-error covariances at the convective
scale using either large or small ensembles. Quart. J. Roy. Meteor. Soc., 140, 2050-
2061.
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 correlated-
kmodel for the longwave. J. Geophys. Res., 102, 16 663–16 682.
Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface
layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163-187
(in Russian).
Parrish, D. F. and J. C. Derber, 1992: The National Meteorological Centers spectral
statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
Pereira, M. B., and L. Berre, 2006: The use of an ensemble approach to study the
background error covariances in a global NWP model. Mon. Wea. Rev., 134, 2466–
2489
Poterjoy, J. and F. Q. Zhang, 2011: Dynamics and structure of forecast error covariance
in the core of a developing hurricane. J. Atmos. Sci., 68, 1586-1606.
Pu, Z. X., S. X. Zhang, M. J. Tong and V. Tallapragada, 2016: Influence of the self-
consistent regional ensemble background error covariance on hurricane inner-core
data assimilation with the GSI-based hybrid system for HWRF. J. Atmos. Sci., 73,
4911-4925.
Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with
continuously cycling limited-area 3DVAR, ensemble Kalman filter, and ‘‘hybrid’’
variational–ensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738.
——, Z. Q. Liu and X. Y. Huang, 2015: Sensitivity of limited-area hybrid variational-
ensemble analyses and forecasts to ensemble perturbation resolution. Mon. Wea.
Rev., 143, 3454-3477.
Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the
Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137.
Toth, Z. and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of
perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330.
Tsai, C. C., 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 , 66A, 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.
——, 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. X., E. J. Zipser, Y. L. Chen, C. T. 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.
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.
Yeh, H. C. and Y. L. Chen, 2002: The role of offshore convergence on coastal rainfall
during TAMEX IOP 3. Mon. Wea. Rev., 130, 2709-2730.
Zhang, F. Q., 2005: Dynamics and structure of mesoscale error covariance of a winter
cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev., 133,
2876-2893.
Zhang, S. Q., M. Zupanski, A. Y. Hou, X. Lin and S. H. Cheung, 2013: Assimilation of
precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon. Wea. Rev., 141, 754-772.
指導教授 鍾高陞(Kao-Shen Chung) 審核日期 2017-8-23
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