Abstract As an empirical three-dimensional ionospheric electron density model, NeQuick2 can generally represent the temporal and spatial characterization of ionospheric Total Electron Content (TEC) at a global scale. However, mismodeling errors usually exist between NeQuick2 representation and actual state of short-term ionospheric variation in small scales. In this contribution, an adaptation method is proposed for the NeQuick2 model based on data ingestion with GNSS-derived ionospheric TEC data. The relationship between the background NeQuick2 Slant TEC (STEC) estimations and real STEC measurements can be properly modelled with a ‘scaling factor’ in a highly efficient manner. In order to verify the feasibility and usefulness of the algorithm, validation experiments are carried out with GNSS tracking stations in China and the surrounding areas under different levels of solar activity. The results show that the bias of NeQuick2 corrections after adaptation are mostly within 1.0 TECu, suggesting that the systematic bias of the original NeQuick2 model can be significantly reduced. In comparison with about 50%-70% STEC RMS errors corrected by the original NeQuick2 model, the NeQuick2 model after adaptation can generally correct about 80% − 90% STEC RMS errors, which is also better than the CODE final GIM product as it has smaller residual bias and uses fewer reference stations. Therefore, the proposed algorithm can be satisfactorily applied in wide area GNSS ionospheric corrections.


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    Titel :

    Adaptation of the NeQuick2 model for GNSS wide-area ionospheric delay correction in China and the surrounding areas


    Beteiligte:
    Li, Wen (Autor:in) / Li, Zishen (Autor:in) / Wang, Ningbo (Autor:in) / Liu, Ang (Autor:in) / Wang, Xiaoming (Autor:in) / Wang, Liang (Autor:in) / Yuan, Hong (Autor:in)

    Erschienen in:

    Advances in Space Research ; 68 , 12 ; 4884-4897


    Erscheinungsdatum :

    2020-11-21


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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