Estimates of Vertical Velocity Errors for IGS ITRF2014 Stations by Applying the Improved Singular Spectrum Analysis Method and Environmental Loading Models

Kłos, Anna; Gruszczyńska, M; Bos, M. S.; Boy, Jean‐Paul; Bogusz, Janusz

doi:10.1007/s00024-017-1494-1

Cited by 30 publications

(24 citation statements)

References 68 publications

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“…That SSA is a good estimator of the varying seasonal signal has been demonstrated by several people (Chen et al 2013;Klos et al 2017). Here, we confirm these findings, but also show its performance for various noise levels in the time series.…”

Section: Discussionsupporting

confidence: 90%

Detecting time-varying seasonal signal in GPS position time series with different noise levels

2017

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The coordinate time series determined with the Global Positioning System (GPS) contain annual and semi-annual periods that are routinely modeled by two periodic signals with constant amplitude and phase-lag. However, the amplitude and phaselag of the seasonal signals vary slightly over time. Various methods have been proposed to model these variations such as Wavelet Decomposition (WD), writing the amplitude of the seasonal signal as a Chebyshev polynomial that is a function of time (CP), Singular Spectrum Analysis (SSA), and using a Kalman Filter (KF). Using synthetic time series, we investigate the ability of each method to capture the time-varying seasonal signal in time series with different noise levels. We demonstrate that the precision by which the varying seasonal signal can be estimated depends on the ratio of the variations in the seasonal signal to the noise level. For most GPS time series, this ratio is between 0.05 and 0.1. Within this range, the WD and CP have the most trouble in separating the seasonal signal from the noise. The most precise estimates of the variations are given by the SSA and KF methods. For real GPS data, SSA and KF can model 49-84 and 77-90% of the variance of the true varying seasonal signal, respectively.

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Section: Discussionsupporting

confidence: 90%

Detecting time-varying seasonal signal in GPS position time series with different noise levels

2017

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“…Regarding the stacked power spectra of the ATML, a sudden drop at frequencies higher than 40 cpy can be seen, indicating an autoregressive behavior. This result confirms the findings of earlier studies [19,26,35]. Likewise, the frequency characteristics of NTOL's stacked power spectra also follow an autoregressive process.…”

Section: Stacked Power Spectra Analysissupporting

confidence: 81%

“…With the environmental loading signals subtracted, the stacked power spectrum of the H after time series is closer to horizontal at high frequencies and an evident power reduction can be noticed at the frequency band between 10 cpy and 50 cpy. A similar result is also found by Klos et al [35], in which the phenomenon of power loss is considered as an artifact. We also notice that the phenomenon of power loss changes the noise property of the GPS height time series, which could influence the estimates of velocity uncertainty.…”

Section: Stacked Power Spectra Analysissupporting

confidence: 76%

“…Santamaría-Gómez and Mémin [34] reported that environmental loading displacements are characterized by a power-law process which results in an overestimation of GPS velocity uncertainties if loading effects are corrected directly. Recently, Klos et al [35] examined 376 GPS stations and found that the overestimation of GPS velocity uncertainties can be avoided if environmental loading displacements are modeled with Improved Singular Spectrum Analysis (ISSA). However, the ISSA needs a relatively long data span, such as the threshold of 10 years adopted in that paper.…”

Section: Introductionmentioning

confidence: 99%

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Influences of Environmental Loading Corrections on the Nonlinear Variations and Velocity Uncertainties for the Reprocessed Global Positioning System Height Time Series of the Crustal Movement Observation Network of China

Peng

Jiang

et al. 2018

Remote Sensing

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Mass redistribution of the atmosphere, oceans, and terrestrial water storage generates crustal displacements which can be predicted by environmental loading models and observed by the Global Positioning System (GPS). In this paper, daily height time series of 235 GPS stations derived from a homogeneously reprocessed Crustal Movement Observation Network of China (CMONOC) and corresponding loading displacements predicted by the Deutsche GeoForschungsZentrum (GFZ) are compared to assess the effects of loading corrections on the nonlinear variations of GPS time series. Results show that the average root mean square (RMS) of vertical displacements due to atmospheric, nontidal oceanic, hydrological, and their combined effects are 3.2, 0.6, 2.7, and 4.0 mm, respectively. Vertical annual signals of loading and GPS are consistent in amplitude but different in phase systematically. The average correlation coefficient between loading and GPS height time series is 0.6. RMS of the GPS height time series are reduced by 20% on average. Moreover, an investigation of 208 CMONOC stations with observing time spans of~4.6 years shows that environmental loading corrections lead to an overestimation of the GPS velocity uncertainty by about 1.4 times on average. Nevertheless, by using a common mode component filter through principal component analysis, the dilution of velocity precision due to environmental loading corrections can be compensated.

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“…Dong et al (2002) showed that approximately 40 % of the observed annual oscillations in the Up component may be explained by the joint contribution of pole tide effects and various loadings (ocean tidal and nontidal, atmospheric or ground water). Klos et al (2017) proved that environmental loading models (atmospheric, hydrological and ocean non-tidal) are characterized by annual signal of amplitudes of up to 12 mm in vertical direction.…”

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confidence: 99%

Deriving common seasonal signals in GPS position time series: by using multichannel singular spectrum analysis

Gruszczyńska¹

2017

Acta Geodynamica Et Geomaterialia

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We estimated the common seasonal signal (annual oscillation) included in the Global Positioning System (GPS) vertical position time series by using Multichannel Singular Spectrum Analysis (MSSA). We employed time series from 24 International GNSS Service (IGS) stations located in Europe which contributed to the newest ITRF2014 (International Terrestrial Reference Frame). The MSSA method has an advantage over the traditional modelling of seasonal signals by the Least-Squares Estimation (LSE) and Singular Spectrum Analysis (SSA) approaches because it can extract time-varying and common seasonal oscillations for stations located in the considered area. Having estimated the annual curve with LSE, we may make a misfit of 3 mm when a peakto-peak variations of seasonal signals are to be estimated due to the time-variability of seasonal signal. A variance of data modelled as annual signal with SSA and MSSA differs of 3 % at average what proves that the MSSA-curves contain only time-varying and common seasonal signal and leave the station-specific part, local phenomena and power-law noise intact. In contrast to MSSA, these effects are modelled by SSA. The differences in spectral indices of power-law noise between MSSA and LSE estimated with Maximum Likelihood Estimation (MLE) are closer to zero than the ones between SSA and LSE, which means that MSSA curves do not contain site-specific noise as much as the SSA curves do. ARTICLE INFO

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Estimates of Vertical Velocity Errors for IGS ITRF2014 Stations by Applying the Improved Singular Spectrum Analysis Method and Environmental Loading Models

Cited by 30 publications

References 68 publications

Detecting time-varying seasonal signal in GPS position time series with different noise levels

Detecting time-varying seasonal signal in GPS position time series with different noise levels

Influences of Environmental Loading Corrections on the Nonlinear Variations and Velocity Uncertainties for the Reprocessed Global Positioning System Height Time Series of the Crustal Movement Observation Network of China

Deriving common seasonal signals in GPS position time series: by using multichannel singular spectrum analysis

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