雷達觀測具有高時空解析度的優點，常使用於劇烈天氣的監控與觀測。本研究是延續及改善前人的工作，選取2008西南氣流實驗計畫(SoWMEX)的IOP8個案，利用多都卜勒雷達觀測資料，改善模式當時的初始場，增進模式降水定量預報之能力。此方法主要包含三大部分：(1)多都卜勒風場合成、(2)熱動力反演、(3)水氣調整。然而前人的實驗存在一些問題：(1)同化後模式初始場壓力與反演的壓力場有很大差異，顯示壓力場並沒有被完整地同化到模式中；(2)模式預報時邊界會出現錯誤回波；(3)定量降水預報的高估及位置偏差。
　　為了改善這些問題，吾人更換將反演之壓力場置入模式的方式，及利用新的水氣調整的方法，其結果顯示在預報回波結構及降水預報都有明顯的改善，且能修正前人實驗的問題，令降水預報更加準確。而在各雲微物理方案測試中，以WSM6物理方案較為合適此實驗的降水預報。
　　當進行熱動力反演時可利用探空資料來提供每個高度層的水平壓力擾動平均，但是當沒有探空資料時，本研究利用模式當時預報場的每層平均壓力擾動來代替探空所提供的水平壓力擾動平均，從而反演出大氣壓力場。本實驗結果顯示沒有使用探空資訊，其降雨預報結果會較有探空時的降水預報為高估，但其預報仍有一定程度的準確性，因此在沒有探空時此方法是可行的。若只利用模式預報而不作任何的雷達資料同化，其降水預報結果十分不理想，也顯示只需兩筆時間的雷達資料並透過此反演同化方法，便可對定量降水預報有很好的改善。
　　由於雷達觀測的範圍是有限制，因此利用二次雷達資料同化讓模式得到更密集的大氣狀態資訊，可以有效改善降水預報的效果。實驗結果顯示利用二次同化方法，可以修正一次同化的強降雨區位置誤差，也減少了模式預報強回波時出現高估的程度，且二次同化方法在定量降水檢驗的表現優於一次同化，但若把二次同化的時間推延至一次同化預報後兩小時，其預報將有更明顯的改善。

　　An important advantage of radar observations is their high temporal and spatial resolution data, which are suitable for heavy weather surveillance. The purpose of this study is to improve previous studies, which are to improve the initial field and hence the quantitative precipitation forecast (QPF) of the numerial model by using multiple-Doppler radar observation data. The assimilation technique includes three components: multiple-Doppler radar wind synthesis, thermodynamic retrieval and moisture adjustment. A case during IOP8, Southwest Monsoon Experiment (SoWMEX)2008 is selected in this study. Some problems have not solves in previous studies: such as the pressure field has not been fully assimilated into the model, the boundary of forecast field produces wrong reflectivity, and overestimate of the rainfall.
In this study we replace the method of the retrieval pressure embedding the model, and use a newly designed moisture adjustment method. The results show improvement in reflectivity structure and the accuracy precipitation of forecast. In the microphysics schemes test, WSM6 is a reasonable choice.
In assimilation test, the model QPF can be significantly improved after assimilating the radar data. In with or without sounding test, it is feasible to use the model outputs to replace the role played by a sounding for estimating the unknown constant at each altitude.
In second assimilation study, it can improve the retrieval atmospheric state variables, convection position and QPF in first assimilation. None of the fraction show which strategy is better. These two experiments produce comparable forecast, the model QPF may be more effective when the second DA is postponed until two hours.
 

尤心瑜和廖宇慶，2011:使用都卜勒氣象雷達資料改善模式定量降雨預報之可行　
　　性研究-以模擬資料測試之實驗結果。大氣科學，第39 期，1–24。
陳尉豪和廖宇慶，2012:同化多都卜勒雷達資料以改善模式定量降水預報－2008
　　SoWMEX IOP8 個案分析。大氣科學，第40 期，323-348。
鐘高陞、廖宇慶、陳台琦，2002：由都卜勒風場反演三維熱動力場的可行性研究
　　-以台灣地區颮線個案為例。大氣科學，第30 期，313–330。
邱健倫，2013：使用氣象雷達改善對流尺度定量降水預報研究 – 理想和真實個
　　案之分析結果。國立中央大學大氣物理所碩士論文，1-82。
黃沛瑜，2012:使用多部都卜勒/偏極化雷達分析凡那比颱風(2010)的眼牆重建
　　過程。國立中央大學大氣物理研究所碩士論文，1-90。
蔡宜君，2012:使用偏極化/多都卜勒雷達資料研究莫拉克颱風(2009)地形降雨特
　　性。國立中央大學大氣物理研究所碩士論文，1-83。
鄧仁星，2000：RASTA(Radar Analysis System for Taiwan Area)使用說明書。
Anthes, R. A., 1983: Regional models of the atmosphere in middle
　　latitudes. Mon. Wea. Rev., 111, 1306–1330.
Barnes, S. L., 1973: Mesoscale objective map analysis using weighted time
　　series observation. NOAA Tech. Memo. Erl Nssl-62, 60pp.
Chung, K. S., I. Zawadzki, M. K. Yau, and L. Fillion, 2009: Short-Term
　　Forecasting of a Midlatitude Convective Storm by the Assimilation of
　　Single–Doppler Radar Observations. Mon. Wea. Rev., 137, 4115–4135.
Crook, N. A., 1996: The sensitivity of moist convection forced by boundary
　　layer processes to low-level thermodynamic fields. Mon. Wea. Rev., 　　
　　124,1767–1785.
_____, and J. Sun, 2002: Assimilating radar, surface and profiler data 　
　　for the Sydney 2000 Forecast Demonstration Project. J. Atmos. Oceanic
　　Technol., 19, 888–898.
_____, and _____, 2004: Analysis and forecasting of the low-level wind
　　during the Sydney 2000 forecast demonstration project. Wea.
　　Forecasting, 19, 151–167.
Gal-Chen, T., 1978: A method for the initialization of the anelastic
equations: Implications for matching models with observations.
Mon. Wea. Rev., 106, 587–606.
Hu, M., M. Xue, and K. Brewster (2006a), 3DVAR and cloud analysis with
　　WSR-88D level-II data for the prediction of Fort Worth tornadic 　
　　thunderstorms.Part I: Cloud analysis and its impact, Mon. Weather
　　Rev., 134,675–698.
Hu, M., M. Xue, J. Gao, and K. Brewster (2006b), 3DVAR and cloud analysis
　　with WSR-88D level-II data for the prediction of Fort Worth tornadic
　　thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR,
　　Mon. Weather Rev., 134, 699–721.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization
of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.
Lin, Y.-J., H. Shen, T.-C. C. Wang, Z-S. Deng, and R. W. Pasken, 1990:
Characteristics of a subtropical squall line determined from TAMEX
dual-Doppler data. Part II: Dynamic and thermodynamic structures and
momentum budgets. J. Atmos. Sci., 47, 2382–2399.
Liou, Y.-C., 2001: The derivation of absolute potential temperature
perturbations and pressure gradients from wind measurements in
three-dimensional space. J. Atmos. Oceanic Technol., 18, 577–590.
Liou, Y.-C., J.-L. Chiou, W.-H. Chen, H.-Y. Yu, 2014: Improving the model
　　convective storm quantitative precipitation nowcasting by
　　assimilating state variables retrieved from multiple-Doppler radar
　　observations, Mon. Wea. Rev., 142, 4017–4035.
_____, T.-C. Chen Wang, and K. S. Chung, 2003: A three-dimensional
　　Variational approach for deriving the thermodynamic structure using
　　Doppler wind observations—An application to a subtropical squall
　　line. J. Appl.Meteor., 42, 1443–1454.
_____, Y.-J. Chang., 2009: A variational multiple–Doppler radar
　　three-dimensional wind synthesis method and its impacts on
　　thermodynamic retrieval. Mon. Wea. Rev., 137, 3992–4010.
Rogers R. R., and M.K. Yau,1989：A short course in cloud physics, Pergamon,
　　Oxford, England, 293pp.
Roux, F., 1985: Retrieval of thermodynamic fields from multiple-Doppler
　　radar data using the equations of motion and the thermodynamic
　　equation. Mon. Wea. Rev., 113, 2142–2157.
_____, 1988: The West African squall line observed on 23 June 1981 during
　　COPT 81: Kinematics and thermodynamics of the convective region. J. 　　
　　Atmos.Sci., 45, 406–426.
_____, and J. Sun, 1990: Single-Doppler observations of a West African
　　squall line on 27–28 May 1981 during COPT 81: Kinematics,
　　thermodynamics and water budget. Mon. Wea. Rev., 118, 1826–1854.
Schaefer, J. T., 1990: The critical success index as an indicator of
　　warning skill. Wea. Forecasting, 5, 570-575.
Shapiro, A., S. Ellis, and J. Shaw, 1995: Single-Doppler velocity
　　retrievals with Phoenix II data: Clear air and microburst wind
　　retrievals in the planetary boundary layer. J. Atmos. Sci., 52, 1265–
　　1287.
Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar
　　observations with an ensemble Kalman filter. Mon. Wea. Rev., 131,
　　1663–1677.
Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from
　　Doppler radar observations using a cloud model and its adjoint.
Part I: Model development and simulated data experiments. J. Atmos.
Sci., 54,1642–1661.
_____, and _____, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55,835–852.
_____, and _____, 2001: Real-time low-level wind and temperature analysis
using single WSR-88D data. Wea. Forecasting, 16, 117–132.
Tai, S.-L., Y.-C. Liou, J. Sun, S.-F. Chang, and M.-C. Kuo, 2011:
Precipitation Forecast using Doppler Radar Data, a Cloud Model with
Adjoint, and the Weather Research and Forecasting Model–Real Case
Studies during SoWMEX in Taiwan. Wea. Forecasting (Accepted)
Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler
radar data with a compressible nonhydrostatic model: OSS experiments.
Mon. Wea. Rev., 133, 1789–1807.
Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 2002a: Retrieval of
model initial fields from single-Doppler observations of a supercell
thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea.
Rev., 130, 433–453.
_____, _____, and _____, 2002b: Retrieval of model initial fields from
　　single-Doppler observations of a supercell thunderstorm. Part II:
　　Thermodynamic retrieval and numerical prediction. Mon. Wea.
　　Rev., 130, 454–476.
Xiao, Q., Y. H. Kuo, J. Sun, W. C. Lee, E. Lim, Y. R. Guo, and D. M. Barker,
　　2005: Assimilation of Doppler radar observations with a regional 　　
　　3DVAR System: Impact of Doppler velocities on forecasts of a heavy
　　rainfall case. J. Appl. Meteor., 44, 768–788.
Xue, M., D.-H. Wang, J.-D. Gao, K. Brewster, and K. K. Droegemeier
　　(2003), The Advanced Regional Prediction System (ARPS), storm-scale
　　numerical weather prediction and data assimilation, Meteorol. Atmos.
　　Phys., 82, 139–170.