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Toward a Refined Estimation of Geocenter Motion From GNSS Displacements: Mitigating Thermoelastic Deformation and Systematic Errors

Wei, Na, Zhou, Yuxin, Shi, Chuang, Xu, Xueqing, Rebischung, Paul, and Liu, Jingnan, 2025. Toward a Refined Estimation of Geocenter Motion From GNSS Displacements: Mitigating Thermoelastic Deformation and Systematic Errors. Journal of Geophysical Research (Solid Earth), 130(7):e2024JB028967, doi:10.1029/2024JB028967.

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@ARTICLE{2025JGRB..13028967W,
       author = {{Wei}, Na and {Zhou}, Yuxin and {Shi}, Chuang and {Xu}, Xueqing and {Rebischung}, Paul and {Liu}, Jingnan},
        title = "{Toward a Refined Estimation of Geocenter Motion From GNSS Displacements: Mitigating Thermoelastic Deformation and Systematic Errors}",
      journal = {Journal of Geophysical Research (Solid Earth)},
     keywords = {geocenter motion, GNSS inversion, thermoelastic deformation, GPS draconitic errors, denoising},
         year = 2025,
        month = jul,
       volume = {130},
       number = {7},
          eid = {e2024JB028967},
        pages = {e2024JB028967},
     abstract = "{The geocenter motion (GCM), associated with the degree-1 component of
        surface mass redistribution in the Earth's fluid envelope, is
        difficult to observe with sufficient precision. Estimating GCM
        through the degree-1 deformation approach assumes that seasonal
        Global Navigation Satellite System (GNSS) variations are
        primarily induced by surface mass loading. However, this is not
        the case for GNSS displacements due to the presence of prominent
        non-loading errors. For a refined estimation of GCM, we modeled
        and mitigated three types of non-loading errors, including
        bedrock thermoelastic deformation, GNSS draconitic errors (DRE),
        and background noises, in GNSS displacements derived from the
        International GNSS Service third reprocessing. Results
        demonstrate that thermoelastic deformation is a significant
        contributor to annual variations in the Z component with an
        amplitude of approximately 1.8 mm. Prominent non-seasonal
        scatters in the X and Y components are also significantly
        reduced by removing DRE and filtering out background noises.
        Besides, an abnormal fluctuation in the X component over the
        period 2012{\textendash}2014 has also been mitigated. Overall,
        by accounting for non-loading errors, the GNSS-derived GCM
        becomes more consistent with independent GCM estimates from the
        geophysical loading model, the method combined Gravity Recovery
        and Climate Experiment and Ocean Bottom Pressure data, and
        Satellite Laser Ranging. Taking the geophysical loading model as
        an example, the percentages of GNSS-derived GCM variances that
        can be explained are remarkably improved from (35\%, 60\%, and
        48\%) to (75\%, 68\%, and 73\%) in the X, Y, and Z components,
        respectively. Accurate modeling of non-loading errors can
        provide a perspective for obtaining refined geocenter estimates
        solely relying on GNSS displacements.}",
          doi = {10.1029/2024JB028967},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JGRB..13028967W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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