Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

Marta Gruszczynska,Maciej Gruszczynski,Janusz Bogusz,Anna Klos,Severine Rosat

doi:10.1007/s00024-018-1814-0

Abstract

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Highlights

Seasonal changes are a component part of the Global Positioning System (GPS) position time series, especially the vertical direction (Blewitt and Lavallee 2002; Collilieux et al 2007)
We presented the results of research on time-varying seasonal signals estimated with multichannel singular spectrum analysis (MSSA), separately for environmental loadings in each considered section
We aimed to propose MSSA as an alternative method to remove the common environmental effect from the GPS position time series without affecting their stochastic characters

Summary

Introduction

Seasonal changes are a component part of the Global Positioning System (GPS) position time series, especially the vertical direction (Blewitt and Lavallee 2002; Collilieux et al 2007). In most cases, those variations result from real geophysical phenomena which deform the Earth’s surface. Those variations result from real geophysical phenomena which deform the Earth’s surface They are broadly explained and modelled by environmental loading effects (van Dam and Wahr 1998; Jiang et al 2013). The appropriate models can be removed directly from the GPS position time series to reduce the influence they might have on the observed displacements. Klos et al (2017) showed that the direct removal of environmental loading models from the GPS observations causes the evident change in the power spectrum density of noise for frequencies between 4 and 80 cycles per year (cpy)

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Conclusion