GPS-derived geocenter motion from the IGS second reprocessing campaign

Abstract

GPS data processing methods and theories are under continuous refinement in the past 30years. Using the latest products is supposed to provide more stable and reliable geocenter estimates. In this paper, geocenter estimates from deformation inversion approach with new observations of IGS second data reprocessing campaign (IG2) are investigated. Results indicate that our IG2-derived geocenter motion estimates agree well with solutions from network approach for SLR. The truncated degree 5 exhibits the highest consistency between GPS-inverted geocenter estimates and the SLR results in both annual amplitudes and phases. Then, the GPS-derived geocenter motions are compared with results from other different approaches. We find that except for a discrepancy in the annual phase estimates of Z component, geocenter motions predicted with the IG2 data are in line with those based on other techniques. In addition, the effects of the translational parameters and the comparison with the IGS first data reprocessing campaign (IG1)-estimated geocenter motions are investigated, and results demonstrate that the translation parameters should be estimated when inversing the geocenter motion with the newly IG2 solutions and the advantage of the IG2 data reprocessing over the previous IG1 efforts. Finally, we address the impacts of post-seismic effects and the missing ocean data on the IG2-derived solutions. After removing the stations affected by large earthquakes, the amplitudes of Y component become higher, but the annual phases of the Y component become far away from the SLR solutions. Comparisons of the equivalent water height from the IG2-estimated coefficients and the solutions from the estimation of the circulation and climate of the ocean indicate that the differences between the two types of solutions vary with different truncated degrees, and the consistency is getting worse and worse with the truncated degree grows. Further researches still need to be done to invert surface mass variation coefficients from various combinations of GPS observations, ocean models and other datasets.