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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/5013

    Title: 使用系集卡曼濾波器同化都卜勒雷達資料之研究;Doppler Radar Data Assimilation Using Ensemble Kalman Filter
    Authors: 黃國禎;Guo-Jhen Huang
    Contributors: 大氣物理研究所
    Keywords: 觀測系統模擬實驗;都卜勒雷達;資料同化;系集卡曼濾波器;ensemble Kalman filter;Doppler radar;observation system simulation experiments;data assimilation
    Date: 2007-07-04
    Issue Date: 2009-09-22 09:43:03 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 系集卡曼濾波器(EnKF)(Ensemble Kalman Filter)是一種資料同化方法。本研究是利用EnKF技術以及觀測系統模擬實驗(OSSE)(Observation System Simulation Experiments)來測試EnKF同化都卜勒雷達資料的表現。本研究使用的模式為ARPS(Advanced Regional Prediction System),此模式是ㄧ個包含多種微物理參數的非靜力可壓縮模式。模擬個案為1977年5月20日發生在美國中部的Del City Storm。本研究在無地形及有地形情況下,設計多組實驗,如「同化系集數目」、「同化時間間隔」、「同化次數」與「同化區域」等若干實驗,探討其對模式各氣象場預報誤差的影響,並且著重在定量降水預報度的改善。結果顯示,以40個系集樣本,每5分鐘同化一次,可得到足夠準確的結果。配合雷達觀測,盡量多次同化,可有助於誤差的降低,並避免誤差擴大而無法修正回來。在有地形時,只要在過山之前就同化初期暴風發展的資訊,便能快速地掌握住暴風的位置與型態,使後續的預報更加準確。同化一個小時雷達資料以後,此系集模式的降水預報能力約可維持至少一小時的準確度。使用EnKF同化雷達資料的確對降低預報誤差具有顯著的改進。 Ensemble Kalman filter (EnKF) is a method for data assimilation. In this study, we apply the Observation System Simulation Experiments (OSSE) type of experimental designs to explore the performance of assimilating Doppler radar data using EnKF. A general purpose non-hydrostatic compressible model, the ARPS (Advanced Regional Prediction System), with complex multi-class microphysics, is employed for conducting all the experiments. Artificial data sets are from a simulated classic storm case that occurred on 20 May 1977 in Del City, Oklahoma. With and without terrain, we investigate the impact of several factors on the model forecasts, with the emphasis on the issue of quantitative precipitation forecast (QPF). These factors consider the number of ensembles, the time interval and frequency of data injection, the area of data availability, and so on. The major results show that using 40 members, and assimilating the radar data once every 5 minutes, can effectively produce the forecasts with sufficient accuracy. Assimilating as many data sets as possible can help to reduce the errors, and prevent the errors from growing to an uncontrollable scale. When the terrain is present and becomes a potential blockage to the radar beams, and if one can assimilate into the model the information of the initial storm development before the storm reaches the lee side of the mountain, then it is still possible to catch the location and pattern of the storm. Such a measure makes the following model forecast maintain its accuracy, even after the storm passes the mountain, and arrives at a region where the radar beams are completely blocked. Finally, to obtain an accurate one-hour QPF also requires an one-hour of radar data assimilation. Overall speaking, the assimilation of Doppler radar data does reveal significant improvements on reducing the forecast errors.
    Appears in Collections:[大氣物理研究所 ] 博碩士論文

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