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

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.

What Causes the Common‐Mode Error in Array GPS Displacement Fields: Case Study for Taiwan in Relation to Atmospheric Mass Loading

“…This estimate agrees with the ~60% of AML contributions evident from the GPS vertical position measurements (for 19 global stations) estimated by van Dam et al, (1994). Our results agree with the recent study of Gruszczynska et al (2019) for the overall reduction of 68% for 229 global stations when compared to the environmental mass loading models.…”

“…The CME can also be reduced via a similarity transformation algorithm (Blewitt et al, 2013). Gruszczynska et al, (2019) used Multichannel Singular Spectrum Analysis for removing the CME from the GPS position time series. Alternatively, Dong et al (2006) proposed the spatio-temporal filtering of GPS data using the combination of Principal Component Analysis (PCA) and the Karhunen-Loeve expansion.…”

“…Dong et al, 2006;Yuan et al, 2008) found these crustal deformations to contribute to the spatially correlated "errors" in the GPS data. Recent studies (Gruszczynska et al, 2019;Gruszczynski et al, 2019;Zhu, Zhou, & Liu, 2017) have further explored the relation of CME with the environmental sources. examined the effects of environmental loading and thermal expansion on the estimated CME for a large network of the Crustal Movement Observation Network of China.…”

“…They found that the environmental loading corrections on the CME reduce the inter-station correlation coefficients of the vertical component up to 20% and that the thermal expansion corrections have negligible effect. Gruszczynska et al, (2019) compared the joint environmental mass loading from atmospheric, hydrological, and non-tidal ocean loading with the CME computed using International GNSS Service (IGS) stations globally. However, used the regional stacking approach for the CME extraction which has limitations for the large region.…”

“…However, used the regional stacking approach for the CME extraction which has limitations for the large region. Although the Multichannel Singular Spectrum Analysis extraction of CME used by Gruszczynska et al, (2019) performed better than the regional stacking methods, it has limitations in the reliable extraction of CME for large regions. Despite some limitations, recent studies did provide insights into CME's relation with the environmental mass loading.…”