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。本研究主要目的為利用多部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(Quantitative Precipitation Forecast：QPF)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)水汽調整。
吾人選取2008西南氣流實驗計畫(SoWMEX)中所觀測到的IOP8個案，作為本研究的實驗對象。使用中央氣象局七股雷達(RCCG)、墾丁雷達(RCKT)及美國國家大氣研究中心(NCAR)所屬的SPOL雷達，於2008年6月14日1200UTC當時的回波及徑向風觀測資料，反演出三維風場結構，接著透過動量方程計算大氣熱動力場，並且利用回波等條件對水汽進行調整，最後同化至模式中。本研究使用NCAR Weather Research and Forecasting (WRF) Model作為計算平台。
本研究設計了一系列實驗，主要的結果有：(1)Kain-Fritsch及WSM6 Scheme為最佳的積雲參數化與微物理組合、(2)水汽的調整有其必要性、(3)風場合成與熱力反演時需考慮雪的存在、(4)以多部雷達網連增加資料覆蓋量對同化結果有重要的影響。
經過本方法調整模式初始場，實驗顯示模式的預報能力可達三小時，雖然降水有高估之趨勢，但相較未同化前的降水分佈會更趨近於觀測。未來更可將本方法用於測試午後對流或甚至颱風降雨系統的預報上。

An important advantage of radar observations is their high temporal and spatial resolutions, which are suitable for heavy weather surveillance. The purpose of this study is to improve the initial field and hence the quantitative precipitation forecast (QPF) of the numerical 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. The radar data in use are the reflectivity and radial wind of the RCCG and RCKT radars from CWB and the SPOL radar from NCAR at June 14, 2008. The 3-D winds, retrieved from the radar and sounding data, are utilized to calculate thermodynamic fields by the momentum equations. The moisture field is updated if some conditions, including a minimum reflectivity of 30 dBZ, occur. The numerical model in use is the Weather Research and Forecasting (WRF) model from NCAR.
Some conclusions are made after a series of experiments: (1) A combination of Kain-Fritsch cumulus parameterization and WSM6 microphysics schemes gives the best result; (2) The moisture adjustment is necessary; (3) Both wind and thermodynamic retrieval algorithms consider the effect of snow; (4) Using multiple-Doppler radar data is necessary because a larger data coverage leads to better results. The above assimilation technique in this case significantly improves the accuracy of the forecast for at least 3 hours compared with the one without data assimilation in spite of overestimated precipitation. We expect applications of this technique to the cases of afternoon convection and even typhoons in the future.

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。
鄧仁星，2000：RASTA(Radar Analysis System for Taiwan Area)使用說明書。
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。
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.
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.
_____, 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.