Investigation of time_changeable seasonal components in the GPS height time series: A case study for Central Europe

Gruszczyńska, M

doi:10.13168/agg.2016.0010

Cited by 19 publications

(7 citation statements)

References 43 publications

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“…In this section we only discuss the amplitudes and phases of annual and semi-annual tropical and draconitic periods, as these contribute the most to GPS data. As was shown by Gruszczynska et al (2016), the tropical annual signal (365.25 days) may obvious power-law behaviour close to flicker noise between low frequencies up to approximately 100 cpy. Moving towards the end of the spectrum, the PSD becomes flat, revealing a pure white noise in the highest frequencies.…”

Section: Seasonal Signalsmentioning

confidence: 54%

An evaluation of velocity estimates with a correlated noise: case study of IGS ITRF2014 European stations

Kłos¹

2017

Acta Geodynamica Et Geomaterialia

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The velocities of the Global Positioning System (GPS) stations are widely employed for numerous geodynamical studies. The aim of this paper is to investigate the reliability of station velocities and to draw reader's attention that for proper estimates of velocity, we need to consider the optimal character of noise. We focus on a set of 115 European GPS stations which contributed to the newest release of the International Terrestrial Reference Frame (ITRF), i.e. ITRF2014. Based on stacked Power Spectral Densities (PSDs), we show that amplitudes of seasonal signals are significant for nine harmonics of tropical year (365.25 days) and two harmonics of draconitic year (351.60 days). The amplitudes of tropical annual signal fall between 0.1-8.4 mm and are much higher for vertical component than for horizontal. Draconitic annual signal reaches the maximum amplitudes of 1.2 and 0.9 mm for North and East, respectively, whereas is slightly higher for the Up component with a maximum of 3.1 mm. We performed a noise analysis with Maximum Likelihood Estimation (MLE) and found that stations in Central and Northern Europe are characterized by spectral index between flicker and randomwalk noise, while stations in Southern and Western Europe: between white and flicker noise. Both amplitudes and spectral indices of power-law noise show a spatial correlation for Up component. We compared the uncertainties of velocities derived in this study with a combination of power-law and white noises to the ones officially released in the ITRF2014 with a pure white noise. A ratio of the two estimates is larger than 10 for 13 % and 30 % of stations in horizontal and vertical direction, respectively with medians of 6 and 7. The large differences support the fact that at the velocity determination the proper noise characteristic should be taken into account to avoid any mislead interpretation.

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Section: Seasonal Signalsmentioning

confidence: 54%

An evaluation of velocity estimates with a correlated noise: case study of IGS ITRF2014 European stations

Kłos¹

2017

Acta Geodynamica Et Geomaterialia

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“…These residuals were then subjected to CME estimates. In this research, we intentionally focused on annual period, as the percentage of total variance of time series explained by modes of annual signal is much higher than the variance explained by any other pair of modes (Gruszczynska et al 2016).…”

Section: Common Seasonal Signals Estimated From Environmental Loadingsmentioning

confidence: 99%

“…Few methods such as Singular Spectrum Analysis (SSA), Wavelet Decomposition (WD) and Kalman Filter (KF) have been already used to retrieve stationdependent time-varying curves from the GPS position time series (Chen et al 2013;Gruszczynska et al 2016;Didova et al 2016;Klos et al 2018a). However, neither of them is able to separate real and spurious effects from the GPS data.…”

Section: Introductionmentioning

confidence: 99%

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

Gruszczyńska

Rosat

Kłos

et al. 2018

Pure Appl. Geophys.

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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.

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“…Klos et al (2018c) demonstrated that the seasonal signals in loading models do not remain constant over time. This means that the seasonal changes in the GPS position time series may be also time-variable (Freymueller 2009;Chen et al 2013;Bogusz et al 2015a;Gruszczynska et al 2016). Therefore, amplitudes given as constants over time, typically derived with the weighted least-squares method, do not provide the most accurate description of them.…”

Section: Introductionmentioning

confidence: 99%

“…They noted that the effect of the remaining peaks was insignificant to the noise analysis. Xu and Yeu (2015) and Gruszczynska et al (2016) proposed using the singular spectrum analysis (SSA) approach to model the time-varying curves. Chen et al (2013) compared the estimates obtained with the SSA and Kalman filter (KF) techniques, and emphasized that the SSA is much faster than the KF and features a lower computational cost.…”

Section: Introductionmentioning

confidence: 99%

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

et al. 2018

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Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used in geodesy to denoise gravity records, to model the seasonal signals in the GPS position time series. To model the time-varying part of the signal, a first-order autoregressive process is employed. The WF is then adapted to the noise level of the data to model only those time variabilities which are significant. Synthetic and real GPS data is used to demonstrate that this variation of the WF leaves the underlying noise properties intact and provides optimal modeling of seasonal signals. This methodology is referred to as the adaptive WF (AWF) and is both easy to implement and fast, due to the use of the fast Fourier transform method.

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Investigation of time_changeable seasonal components in the GPS height time series: A case study for Central Europe

Cited by 19 publications

References 43 publications

An evaluation of velocity estimates with a correlated noise: case study of IGS ITRF2014 European stations

An evaluation of velocity estimates with a correlated noise: case study of IGS ITRF2014 European stations

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

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

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