Study of GRACE-based Estimation Techniques and Seasonal Drivers of Geocenter Motion Using Multichannel Singular Spectrum Analysis

This study aims to provide valuable scientific insights into various estimation techniques for Geocenter Motion (GCM) from the perspectives of signal analysis and seasonal variation driving factors, thereby enhancing GRACE users' understanding and application of GCM. Initially, it utilizes the Satellite Laser Ranging (SLR) technique with the network shift approach to estimate over 30 years of weekly GCM time series from 1994 to 2024. Subsequently, we employ two approaches to estimate three types of monthly GCM time series spanning more than 20 years from 2002 to 2023: combining GRACE data with an Ocean Bottom Pressure model (GRACE-OBP approach), the Fingerprint Approach (FPA), and the Fingerprint Approach with satellite altimetry data (FPA-SA, up to 2022). The former is referred to as SLR-based GCM estimates, while the latter, based on GRACE Earth gravity field models, is termed GRACE-based GCM estimates. Furthermore, this study pioneers the use of Multichannel Singular Spectrum Analysis (MSSA) for GCM analysis to unveil GRACE-based estimation techniques, especially focusing on the latest GRACE-based GCM estimates from the GRACE-OBP and FPA/FPA-SA approaches. MSSA is used to explore how variations in terrestrial water storage (TWS) and atmosphere-ocean (AO) drive GCM and contribute to seasonal variations through the analysis of correlations and lags between GCM and seasonal driving factors. The results indicate that the 160-day periodic signal detected in GRACE-based GCM estimates, linked to half the GRACE draconitic period, originates from systematic errors in higher-degree spherical harmonic coefficients of the Earth gravity field models, but is absent in SLR-based GCM estimates. Additionally, the study provides the first geophysical explanation linking the 120-day signal in GCM to global precipitation changes.