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研究生: 姚奕安
I-An Yao
論文名稱: 藉由數值模式水平風場改善雷達回波外延即時預報系統:16個颱風個案統計分析
Improving radar echo Lagrangian extrapolation by blending numerical model wind information: statistical performance of 16 typhoon cases
指導教授: 鍾高陞
Kao-Shen Chung
口試委員:
學位類別: 碩士
Master
系所名稱: 地球科學學院 - 大氣科學學系
Department of Atmospheric Sciences
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 91
中文關鍵詞: 即時預報雷達回波外延
外文關鍵詞: nowcasting, echo extrapolation
相關次數: 點閱:6下載:0
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  • 此研究使用中央氣象局所提供的整合最大回波資料(Maximum Reflectivity)以及加拿大麥基爾大學(McGill University)所開發的雷達回波外延預報系統(McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation, MAPLE)針對16個颱風個案進行分析,以此檢視此外延法在即時預報上應用於臺灣地區颱風天氣系統的預表現。此外為了提升極短期預報(Nowcasting, 0-6小時)能力,將數值模式中所提供的風場資訊與回波外延之移動場進行結合。而本研究中除了使用最常見的連續校驗(continuous verification)以及絕對校驗(categorical verification),也加入了相鄰法(neighborhood method)進行評估預報結果,避免受到高解析度的影響低估模式預報能力。
    首先,為了得到良好的定量降水估計(Quantitative Precipitation Estimation, QPE),針對數個Z-R關係式進行檢驗。接著,由外延預報的結果顯示MAPLE在定量降水預報上可以提供約2小時的有效預報。但回波外延預報系統隨著時間預報結果會有嚴重的變形現象。藉由結合數值天氣預報的風場資訊來改進此現象,同時可以維持颱風旋轉特性以及能捕捉到雨帶在地形附近的結構。在透過上述之各項校驗分數的評量,整體而言,結合過後的即時預報系統可以將極短期預報的能力提升至3小時,有效的降低在外延預報中產生的位移不確定性。這樣的改善,尤其在颱風登入後至離臺這段時期,改善顯著。對於防災 、減災而言方面可以即時的提供天氣資訊。


    In this study, composite radar data from the Central Weather Bureau (CWB) of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan. In addition, in order to improve the nowcasting system, the information of the numerical model is blended into the system. In order to examine the performance of the nowcasting, continuous and categorical verification is used. However, grid-point verification is strict for high resolution and could underestimate the ability of the prediction system. Therefore, the neighborhood method is also applied for validation.
    First, in comparison to the rainfall amount from gauges, the best Z-R relationship is determined. Second, the statistical results of the radar echo extrapolation for 16 typhoon cases show that the quantitative precipitation nowcasting skill could persist for up to 2 hours. However, significant distortion for the rotational system is found after 2 hours. Therefore, the information of the numerical model is blended to capture and maintain the rotation of typhoon rain-band structures. When verifying the performance of the hybrid nowcasting system, whether from the aspect of categorical verification or the neighborhood method, in general, the hybrid scheme of the system further improves the nowcasting for up to 3 hours. Furthermore, the improvement of the hybrid scheme is more significant after the typhoon landed in Taiwan. For disaster prevention and mitigation, the nowcasting system can provide effective weather information immediately.

    摘要 i Abstract ii Acknowledgment iii Outline iv Table vi Figure vii Chapter 1: Introduction 1 Chapter 2: Case overview and Data 6 2.1 Data 6 2.1.1 Integrated radar network in Taiwan 6 2.1.2 Rain gauge data 7 2.1.3 ECMWF ERA-Interim 7 2.2 Cases introduction 8 Chapter 3: Methodology 9 3.1 The nowcasting system, MAPLE 9 3.1.1 Variational Echo Tracking technique 9 3.1.2 Semi-Lagrangian advection 11 3.2 Numerical Weather Prediction (NWP) model setup 12 3.3 Combination the VET with other sources 13 3.4 Quantitative Precipitation Estimation 13 3.5 Verification 15 3.5.1 Continuous Verification 16 3.5.2 Categorical Verification 16 3.5.3 Neighborhood method 18 Chapter 4:Results and discussion 20 4.1 Z-R relationship test 20 4.2 compared VET with the re-analysis wind field of ECMWF 22 4.3 Sensitivity test of the combination of VET and WRF wind fields 24 4.4 An example of the typhoon cases 27 4.5 Statistical performance of 16 typhoon cases 31 Chapter 5:Conclusion and future work 35 5.1 Conclusion 35 5.2 Future work 38 Reference 41 Table 46 Figure 50

    Reference
    Atencia, A., and I. Zawadzki, 2014: A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part I: Lagrangian Ensemble Technique. Mon. Wea. Rev., 142, 4036-4052.
    Bellon, A., I. Zawadzki, A. Kilambi, H. C. Lee, Y. H. Lee, and G. J. A.-P. J. o. A. S. Lee, 2010: McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique. ASIA-PAC J ATMOS SCI, 46, 369-381.
    Bowler, N. E., C. E. Pierce, and A. W. Seed, 2006: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q. J. Roy. Meteor. Soc., 132, 2127-2155.
    Chan, J. C. L., and W. M. Gray, 1982: Tropical Cyclone Movement and Surrounding Flow Relationships. Mon. Wea. Rev., 110, 1354-1374.
    Chang, P.-L., P.-F. Lin, B. Jong-Dao Jou, and J. Zhang, 2009: An Application of Reflectivity Climatology in Constructing Radar Hybrid Scans over Complex Terrain. J. Atmos. Ocean. Tech., 26, 1315-1327.
    Chen, J.-Y., W.-Y. Chang, and T.-C. C. Wang, 2017: Comparison of Quantitative Precipitation Estimation in Northern Taiwan Using S- and C-band Dual-Polarimetric Radars. Atmospheric Sciences, 45, 57-81.
    Cheng, C.-J., and T.-H. Lee, 2017: Enhancing the ABLER algorithm on Rainstorm Velocity-Field Estimation by Jointly Optiminzed Piecewise-Linear Functions and Tracking with Principle-Velocity Transform. National Taiwan University Master Thesis.
    Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology. J. Atmos. Ocean. Tech., 10, 785-797.
    Dudhia, J., 1988: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J. Atmos. Sci., 46, 3077-3107.
    DuFran, Z., C. J. R, and S. B, 2009: Improved Precipitation Nowcasting Algorithm Using a High-resolution NWP Model and National Radar Mosaic. 34th Conference on Radar Meteorology.
    Germann, U., and I. Zawadzki, 2002: Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology. Mon. Wea. Rev., 130, 2859-2873.
    ——, 2004: Scale Dependence of the Predictability of Precipitation from Continental Radar Images. Part II: Probability Forecasts. J. Appl. Meteorol, 43, 74-89.
    Germann, U., I. Zawadzki, and B. Turner, 2006: Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction. J. Atmos. Sci., 63, 2092-2108.
    Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. J. Geophys. Res., 29, 38-31-38-34.
    Halperin, D. J., H. E. Fuelberg, R. E. Hart, and J. H. Cossuth, 2016: Verification of Tropical Cyclone Genesis Forecasts from Global Numerical Models: Comparisons between the North Atlantic and Eastern North Pacific Basins. Wea. Forecasting, 31, 947-955.
    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.
    Laroche, S., and I. Zawadzki, 1994: A Variational Analysis Method for Retrieval of Three-Dimensional Wind Field from Single-Doppler Radar Data. J. Atmos. Sci., 51, 2664-2682.
    ——, 1995: Retrievals of Horizontal Winds from Single-Doppler Clear-Air Data by Methods of Cross Correlation and Variational Analysis. J. Atmos. Ocean. Tech., 12, 721-738.
    Lee, H. C., Y. H. Lee, J.-C. Ha, D.-E. Chang, A. Bellon, I. Zawadzki, and G. J. A.-P. J. o. A. S. Lee, 2010: McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part II: Real-time verification for the summer season. ASIA-PAC J ATMOS SCI, 46, 383-391.
    Lee, T.-H., T.-S. Huang, F.-Y. Chang, H.-Y. Shueh, and C.-H. Liu, 2013: The Advection Based Lagrangian-Eulerian Regression (ABLER) Scheme for Storm Tracking. 2013 APEC Typhoon Symposium.
    Li, P. W., and E. S. T. Lai, 2004: Short-range quantitative precipitation forecasting in Hong Kong. J. Hydrol., 288, 189-209.
    Li, P. W., W. K. Wong, and E. S. T. Lai, 2005: RAPIDS—A new rainstorm nowcasting system in Hong Kong. Proc. WWRP Symp. on Nowcasting and Very Short Range Forecasting, Toulouse, France, World Weather Research Program, 7.17.
    Liang, Q., Y. Feng, W. Deng, S. Hu, Y. Huang, Q. Zeng, and Z. Chen, 2010: A composite approach of radar echo extrapolation based on TREC vectors in combination with model-predicted winds. Adv. Atmos. Sci., 27, 1119-1130.
    Liu, C.-H., and T.-H. Lee, 2014: A Study on Typhoon Rainfall Echo Velocity Estimation Using the Advection-Based Lagrangian Eulerian Regression Algorithm. National Taiwan University Master Thesis.
    Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization. Math. Program., 45, 503-528.
    Mandapaka, P. V., U. Germann, L. Panziera, and A. Hering, 2011: Can Lagrangian Extrapolation of Radar Fields Be Used for Precipitation Nowcasting over Complex Alpine Orography? Wea. Forecasting, 27, 28-49.
    Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery. Mon. Wea. Rev., 134, 49-78.
    Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663-16682.
    Navon, I. M., and D. M. Legler, 1987: Conjugate-Gradient Methods for Large-Scale Minimization in Meteorology. Mon. Wea. Rev., 115, 1479-1502.
    Pan, J.-W., K.-S. Chung, H.-H. Lin, T.-C. C. Wang, and I.-A. Yao, 2018: Feasibility Assessment of Applying Variational Radar Echo Tracking Method over Complex Terrain in Taiwan. Atmospheric Sciences, 46, 1-34.
    Rinehart, R. E., and E. T. Garvey, 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287-289.
    Roberts, N. M., and H. W. Lean, 2008: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events. Mon. Wea. Rev., 136, 78-97.
    Roebber, P. J., 2009: Visualizing Multiple Measures of Forecast Quality. Wea. Forecasting, 24, 601-608.
    Seed, A. W., 2003: A Dynamic and Spatial Scaling Approach to Advection Forecasting. J. Appl. Meteorol., 42, 381-388.
    Skok, G., and N. Roberts, 2016: Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts. Q. J. Roy. Meteor. Soc., 142, 2599-2610.
    Sokol, Z., J. Mejsnar, L. Pop, and V. Bližňák, 2017: Probabilistic precipitation nowcasting based on an extrapolation of radar reflectivity and an ensemble approach. Atmos. Res., 194, 245-257.
    Tao, W.-K., 2003: Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model. Meteor. Atmos. Phys., 82, 97-137.
    Turner, B. J., I. Zawadzki, and U. Germann, 2004: Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE). J. Appl. Meteorol., 43, 231-248.
    Tuttle, J. D., and G. B. Foote, 1990: Determination of the Boundary Layer Airflow from a Single Doppler Radar. J. Atmos. Ocean. Tech., 7, 218-232.
    Wu, C.-C., K.-H. Chou, P.-H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa, 2007: The Impact of Dropwindsonde Data on Typhoon Track Forecasts in DOTSTAR. Wea. Forecasting, 22, 1157-1176.
    Xin, L., G. Reuter, and B. Larochelle, 1997: Reflectivity‐rain rate relationships for convective rainshowers in Edmonton. Atmosphere-Ocean, 35, 513-521.

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