Benchmarking GPS stations: an improved way to identify the GPS sensitivity

Global Positioning System (GPS) stations are affected by a plethora of real and system-related signals and errors that occur at various temporal and spatial resolutions. Geophysical changes related to mass redistribution within the Earth system, common mode components, instability of GPS monuments or thermal expansion of ground, all contribute to the GPS-derived displacement time series. Different spatial resolutions that real and system-related errors occur within are covered thanks to the global networks of GPS stations, characterized presently by an unprecedented spatial density. Various temporal resolutions are covered by displacement time series which span even 25 years now, as estimated for the very first stations established. However, since the GPS sensitivity remains unrecognized, retrieving one signal from this wide range of processes may be very uncertain. Up to now, a comparison between GPS-observed displacement time series and displacements predicted by a set of models, as e.g. environmental loading models, was used to demonstrate the accuracy of the model to predict the observed phenomena. Such a comparison is, however, dependent on the accuracy of models and also on the sensitivity of individual GPS stations. We present a new way to identify the GPS sensitivity, which is based on benchmarking of individual GPS stations using statistical clustering approaches. We focus on regional sets of GPS stations located in Europe, where technique-related signals cover real geophysical changes for many GPS permanent stations and those located in South America and Asia, where hydrological and atmospheric loadings dominate other effects. We prove that combining GPS stations into smaller sets improves our understanding of real and system-related signals and errors.