An empirical orthogonal function model of total electron content over China

Authors

  • Tian Mao,

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
    2. National Center for Space Weather, China Meteorological Administration, Beijing, China
    3. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
    4. Graduate School of Chinese Academy of Sciences, Beijing, China
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  • Weixing Wan,

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
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  • Xinan Yue,

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
    2. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
    3. Graduate School of Chinese Academy of Sciences, Beijing, China
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  • Lingfeng Sun,

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
    2. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
    3. Graduate School of Chinese Academy of Sciences, Beijing, China
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  • Biqiang Zhao,

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
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  • Jianpeng Guo

    1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
    2. Graduate School of Chinese Academy of Sciences, Beijing, China
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Abstract

[1] In this paper a climatology model of total electron content (TEC) over China has been developed on the basis of the empirical orthogonal function (EOF) analysis using Global Positioning System (GPS) data from the International Global Navigation Satellite System Service (IGS) and Crust Movement Observation Network of China (CMONOC) covering almost the whole Chinese sector during 1996–2004. The model well represents observational data with mean bias of −0.00994 TECU (1 TECU = 1.0 × 1016 el· m−2) and standard deviation of 5.42 TECU. Then the EOF model and IRI have been used in three-dimensional variational (3DVAR) data assimilation experiments separately, and results reveal that the ability of assimilation nowcasting for the EOF model is better as it provides a more authentic background.

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