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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/67461


    Title: 應用地面GPS觀測以及掩星觀測進行電離層資料同化分析;Ionospheric Data Assimilation Analysis Using Ground-based GPS and Radio Occultation Observations
    Authors: 林其彥;Lin,Chi-Yen
    Contributors: 太空科學研究所
    Keywords: 電離層;資料同化分析;地面全球定位系統衛星觀測;福衛三號掩星觀測;福衛七號掩星觀測;Ionosphere;Data Assimilation Analysis;Ground-based GPS Observation;FORMOSAT-3/COSMIC Radio Occultation Observation;FORMOSAT-7/COSMIC-2 Radio Occultation Observation
    Date: 2015-05-05
    Issue Date: 2015-07-30 18:41:45 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 電離層資料同化模式將離層觀測資料代入背景模式重建電離層三維分佈電子密度的方法。其以高斯馬爾可夫卡爾曼濾波器(Gauss-Markov Kalman filter)同化背景模型國際參考電離層模式(International Reference Ionosphere),同化地面GPS觀測以及福爾摩沙三號(FORMOSAT-3/COSMIC)無線電掩星觀測兩種不同類型的全電子含量觀測資料。電離層背景模式誤差和觀測誤差之共變異數矩陣在電離層資料同化的過程中佔有舉足輕重的作用。隨空間位置變化的相關性矩陣則採用來自國際參考電離層模式輸出之電子濃度分佈,並經由經驗正交函數計算建模。觀測系統模擬實驗顯示透過隨空間位置變化之相關性矩陣,建制電離層背景模式誤差共變異數矩陣,應用於同化全電子含量觀測資料,可以獲得較高品質的電離層資料同化電子濃度分佈結果。代入地面GPS觀測以及福衛三號無線電掩星觀測之電離層全電子含量資料同化結果,並與Millstone Hill異調散射雷達之電子濃度垂直結構相互比較顯示同化福衛三號掩星觀測全電子含量資料可改進電離層資料同化電子濃度垂直結構。另外,藉由觀測系統模擬實驗,探討阿貝爾反演以及電離層資料同化電子濃度垂直剖面結構之精確度,並比較兩種方法所獲得之電離層電子濃度結構。電離層資料同化模式加入卡爾曼濾波器預測步驟,配合著卡爾曼濾波器觀測更新步驟,以地面GPS和福衛三號無線電掩星資料,進行全球三維電離層重建。電離層資料同化模擬結果顯示,於數據同化過程中,卡爾曼濾波器預測和測量更新步驟,可以有效增加三維全球電離層資料同化模式的準確性。此外,本研究也進行以福爾摩沙七號(FORMOSAT-7/COSMIC-2) 無線電掩星觀測以及地面GPS觀測,進行三維全球電離層資料同化模式的觀測系統模擬實驗。結果說明同化福衛七號無線電掩星觀測,可以增加電離層資料同化模式的精準度明顯超過原使用福衛三號無線電掩星觀測結果。總結,電離層資料同化模式採用隨空間位置變化之背景模型誤差共變異數矩陣,卡爾曼濾波預測步驟,以及卡爾曼濾波觀測更新步驟,可以同化地面GPS觀測以及無線電掩星觀測,重建三維電離層電子密度分佈。;Ionospheric data assimilation is a powerful approach to reconstruct the three-dimensional distribution of ionospheric electron density from various types of observations. The ionospheric data assimilation model based on the Gauss-Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model is used to assimilate two different types of total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing System Simulation Experiments (OSSEs) suggest that assimilation of TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation (RO) observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. The ionospheric electron density structures, including the Weddell Sea Anomaly, are reconstructed from electron density profiles retrieved by the Abel inversion techniques and obtained using the Kalman filter data assimilation measurement update are compared to each other in OSSEs and real data analysis. The Kalman filter forecast step is incorporated into the data assimilation procedure made only of the Kalman filter measurement update step in order to reconstruct the ionosphere globally by assimilating both ground-based GPS and RO observations. The OSSE results show that the data assimilation procedure, consisting of both the forecast and measurement update steps of the Kalman filter, can increase the accuracy of the data assimilation model over the procedure consisting of the Kalman filter measurement update step alone. Finally, the OSSEs of assimilating FORMOSAT-7/COSMIC-2 (F7/C2) RO and ground-based GPS data in the data assimilation model are implemented, the OSSEs results demonstrate that the F7/C2 RO data can increase model accuracy more than assimilating F3/C RO data. The new ionospheric data assimilation model that employs the location-dependent background model error covariance, Kalman filter forecast step, and Kalman filter measurement update step could reconstruct the three-dimensional ionospheric electron density distribution satisfactorily from both ground- and space-based GPS observations.
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