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

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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 2012–2014 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.
Funding Information
  • National Natural Science Foundation of China (42174028, 41931075, 42030109, 41774007)
  • Natural Science Foundation of Shanghai Municipality (24ZR1476800)

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