Anatomy of apparent seasonal variations from GPS‐derived site position time series

TLDR

Peering depicts known seasonal sources to reveal poorly understood contributors. The study derives apparent seasonal site position variations from 4.5 years of GPS data, explores them via peering, proposes a scaled sensitivity matrix to assess correlated parameters, and examines implications for improving terrestrial reference frame stability. The authors evaluate contributions from pole tide, ocean tide loading, atmospheric loading, nontidal oceanic mass, and groundwater loading, then assess unmodeled wet troposphere, bedrock thermal expansion, phase‑center variation errors, and orbital modeling errors using a scaled sensitivity matrix and by comparing solutions from multiple GPS analysis centers. Approximately 40 % of the annual vertical site‑position variations are explained by seasonal surface‑mass redistributions, while differences among analysis‑center solutions are the main source of residual seasonal effects.

Abstract

Apparent seasonal site position variations are derived from 4.5 years of global continuous GPS time series and are explored through the “peering” approach. Peering is a way to depict the contributions of the comparatively well‐known seasonal sources to garner insight into the relatively poorly known contributors. Contributions from pole tide effects, ocean tide loading, atmospheric loading, nontidal oceanic mass, and groundwater loading are evaluated. Our results show that ∼40% of the power of the observed annual vertical variations in site positions can be explained by the joint contribution of these seasonal surface mass redistributions. After removing these seasonal effects from the observations the potential contributions from unmodeled wet troposphere effects, bedrock thermal expansion, errors in phase center variation models, and errors in orbital modeling are also investigated. A scaled sensitivity matrix analysis is proposed to assess the contributions from highly correlated parameters. The effects of employing different analysis strategies are investigated by comparing the solutions from different GPS data analysis centers. Comparison results indicate that current solutions of several analysis centers are able to detect the seasonal signals but that the differences among these solutions are the main cause for residual seasonal effects. Potential implications for modeling seasonal variations in global site positions are explored, in particular, as a way to improve the stability of the terrestrial reference frame on seasonal timescales.