scholarly journals Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

2018 ◽  
Vol 10 (10) ◽  
pp. 1584 ◽  
Author(s):  
Guo Chen ◽  
Qile Zhao ◽  
Na Wei ◽  
Jingnan Liu
Keyword(s):  
Time Series ◽  
Colored Noise ◽  
Deterministic Model ◽  
Simulated Data ◽  
Weight Matrix ◽  
Helmert Transformation ◽  
Seasonal Signals ◽  
Position Time Series ◽  
Power Law Noise ◽  
Transformation Parameters

The noise characteristics of the Global Navigation Satellite System (GNSS) position time series can be biased by many factors, which in turn affect the estimates of parameters in the deterministic model using a least squares method. The authors assess the effects of seasonal signals, weight matrix, intermittent offsets, and Helmert transformation parameters on the noise analyses. Different solutions are obtained using the simulated and real position time series of 647 global stations and power law noise derived from the residuals of stacking solutions are compared. Since the true noise in the position time series is not available except for the simulated data, the authors paid most attention to the noise difference caused by the variable factors. First, parameterization of seasonal signals in the time series can reduce the colored noise and cause the spectral indexes to be closer to zero (much “whiter”). Meanwhile, the additional offset parameters can also change the colored noise to be much “whiter” and more offsets parameters in the deterministic model leading to spectral indexes closer to zero. Second, the weight matrices derived from the covariance information can induce more colored noise than the unit weight matrix for both real and simulated data, and larger biases of annual amplitude of simulated data are attributed to the covariance information. Third, the Helmert transformation parameters (three translation, three rotation, and one scale) considered in the model show the largest impacts on the power law noise (medians of 0.4 mm−k/4 and 0.06 for the amplitude and spectral index, respectively). Finally, the transformation parameters and full-weight matrix used together in the stacking model can induce different patterns for the horizontal and vertical components, respectively, which are related to different dominant factors.

Helmert transformation strategies in analysis of GPS position time-series

2020 ◽  
Vol 223 (2) ◽  
pp. 973-992
Author(s):  
Shiwei Guo ◽  
Chuang Shi ◽  
Na Wei ◽  
Min Li ◽  
Lei Fan ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Frame ◽  
Scale Factor ◽  
Network Effect ◽  
Satellite Orbits ◽  
Surface Mass ◽  
Helmert Transformation ◽  
Position Time Series ◽  
Transformation Parameters ◽  
The Impact

SUMMARY Global positioning system (GPS) position time-series generated using inconsistent satellite products should be aligned to a secular Terrestrial Reference Frame by Helmert transformation. However, unmodelled non-linear variations in station positions can alias into transformation parameters. Based on 17 yr of position time-series of 112 stations produced by precise point positioning (PPP), we investigated the impact of network configuration and scale factor on long-term time-series processing. Relative to the uniform network, the uneven network can introduce a discrepancy of 0.7–1.1 mm, 21.3–27.5 μas and 1.3 mm in terms of root mean square (RMS) for the translation, rotation and scale factor (if estimated), respectively, no matter whether the scale factor is estimated. The RMS of vertical annual amplitude differences caused by such network effect reaches 0.5–0.6 mm. Whether estimating the scale factor mostly affects the Z-translation and vertical annual amplitude, leading to a difference of 1.3 mm when the uneven network is used. Meanwhile, the annual amplitude differences caused by the scale factor present different geographic location dependences over the north, east and up components. The seasonal signals derived from the transformation using the uniform network and without estimating scale factor have better consistency with surface mass loadings with more than 41 per cent of the vertical annual variations explained. Simulation studies show that 40–50 per cent of the annual signals in the scale factor can be explained by the aliasing of surface mass loadings. Another finding is that GPS draconitic errors in station positions can also alias into transformation parameters, while different transformation strategies have limited influence on identifying the draconitic errors. We suggest that the uniform network should be used and the scale factor should not be estimated in Helmert transformation. It is also suggested to perform frame alignment on PPP time-series, even though the used satellite products belong to a consistent reference frame, as the origin of PPP positions inherited from satellite orbits and clocks is not so stable during a long period. With Helmert transformation, the seasonal variations would better agree with surface mass loadings, and noise level of time-series is reduced.

Chameleonic noise in GPS position series: what is the true color of the GPS error spectra?

2020 ◽  
Author(s):  
Alvaro Santamaría-Gómez ◽  
Jim Ray
Keyword(s):  
Time Series ◽  
Skin Color ◽  
Colored Noise ◽  
Flicker Noise ◽  
Linear Trend ◽  
Pink Noise ◽  
Noise Levels ◽  
Color Noise ◽  
Position Time Series ◽  
True Color

<p><em>Chameleonic: readily changing color or other attributes.</em></p><p><em>Chameleon: a lizard that changes skin color to match what surrounds it so that it cannot be seen.</em></p><p>The error spectrum of decadal long GPS position time series is typically represented by a combination of flicker (pink) noise at long periods and white noise at short periods. It is known that when fitting a linear trend to the series, part of the flicker noise at the longest observed period will be absorbed by the trend. Here, using real and synthetic GPS position series, we show how the error spectrum is even more altered by the position discontinuities that populate the series. The fitted position offsets at the discontinuity epochs absorb a significant portion of the power spectrum at periods longer than the separation between the discontinuity epochs. The resulting error spectrum is flattened at long periods and this implies that:</p><ul><li>the estimated content of colored noise is biased low and can even apparently change its color towards whiter noise, i.e. the true noise color is not observable due to the discontinuities,</li> <li>the red (random walk) noise , most probably present in the series in small quantity, becomes undetectable even if long series are used,</li> <li>the pink (flicker) noise is not the best color noise to represent the error spectrum in long series containing discontinuities,</li> <li>the colored noise content cannot be compared between series with different sets of discontinuities.</li> </ul><p>These findings need to be considered when comparing the noise levels between series from different solutions, networks or monuments. In particular, and contrary to a recently published recommendation, station operators should make every effort to avoid adding new discontinuities into their station time series if reliable velocity estimates are expected.</p>

scholarly journals Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China

2018 ◽  
Vol 10 (9) ◽  
pp. 1472 ◽  
Author(s):  
Peng Yuan ◽  
Weiping Jiang ◽  
Kaihua Wang ◽  
Nico Sneeuw
Keyword(s):  
Time Series ◽  
Velocity Field ◽  
Colored Noise ◽  
Environmental Loading ◽  
Movement Observation ◽  
Observation Network ◽  
Uncertainty Estimates ◽  
Position Time Series ◽  
Periodic Signals ◽  
Gps Velocity Field

Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental loading effects, and the estimation of a realistic and unbiased GPS velocity field for geodynamic applications. In this paper, we homogeneously processed the daily observations of 231 Crustal Movement Observation Network of China (CMONOC) Continuous GPS stations to obtain their position time series. Then, we filtered out the CMC and evaluated its effects on the periodic signals and noise for the CMONOC time series. Results show that, with CMC filtering, peaks in the stacked power spectra can be reduced at draconitic harmonics up to the 14th, supporting the point that the draconitic signal is spatially correlated. With the colored noise suppressed by CMC filtering, the velocity uncertainty estimates for both of the two subnetworks, CMONOC-I (≈16.5 years) and CMONOC-II (≈4.6 years), are reduced significantly. However, the CMONOC-II stations obtain greater reduction ratios in velocity uncertainty estimates with average values of 33%, 38%, and 54% for the north, east, and up components. These results indicate that CMC filtering can suppress the colored noise amplitudes and improve the precision of velocity estimates. Therefore, a unified, realistic, and three-dimensional CMONOC GPS velocity field estimated with the consideration of colored noise is given. Furthermore, contributions of environmental loading to the vertical CMC are also investigated and discussed. We find that the vertical CMC are reduced at 224 of the 231 CMONOC stations and 170 of them are with a root mean square (RMS) reduction ratio of CMC larger than 10%, confirming that environmental loading is one of the sources of CMC for the CMONOC height time series.

scholarly journals Effect of antenna snow intrusion on vertical GPS position time series in Antarctica

2020 ◽  
Vol 94 (10) ◽  
Author(s):  
A. Koulali ◽  
P. J. Clarke
Keyword(s):  
Time Series ◽  
Vertical Surface ◽  
Snow Accumulation ◽  
Surface Mass ◽  
Load Variations ◽  
Mode Decomposition ◽  
Surface Displacements ◽  
Seasonal Signals ◽  
Position Time Series ◽  
Mass Loads

Abstract Vertical surface displacements from continuous Global Positioning System (GPS) stations often show strong seasonal signals, which in some cases may be associated with surface mass loading, including hydrological, and non-tidal oceanic and atmospheric loading. In Antarctica, many GPS stations show vertical motions in phase with seasonal snow accumulation changes, but these variations cannot be fully explained with snow load variations between seasons. Instead we show, for many sites in Antarctica, that a significant component of the annual cycle in vertical GPS coordinates time series may be related to snow/ice inside antennas causing as an artefact apparent seasonal variation, with amplitudes of up to 4 mm. We present a method based on the Empirical Mode Decomposition (EMD) algorithm to remove this artefact signal. The corrected GPS time series show an improvement in the agreement with displacements predicted by elastic modelling using GRACE-derived surface mass loads.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

scholarly journals GPS measurements on pre-, co- and post-seismic surface deformation at first multi-parametric geophysical observatory, Ghuttu in Garhwal Himalaya, India

2020 ◽  
Vol 10 (1) ◽  
pp. 136-144
Author(s):  
P.K. Gautam ◽  
S. Rajesh ◽  
N. Kumar ◽  
C.P. Dabral
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Translational Motion ◽  
Anomalous Behavior ◽  
Deformation Pattern ◽  
Local Earthquakes ◽  
Position Time Series ◽  
Continuous Gps ◽  
Gps Time Series ◽  
Gps Station

Abstract We investigate the surface deformation pattern of GPS station at MPGO Ghuttu (GHUT) to find out the cause of anomalous behavior in the continuous GPS time series. Seven years (2007-2013) of GPS data has been analyzed using GAMIT/GLOBK software and generated the daily position time series. The horizontal translational motion at GHUT is 43.7 ± 1 mm/yr at an angle of 41°± 3° towards NE, while for the IGS station at LHAZ, the motion is 49.4 ±1 mm/yr at 18 ± 2.5° towards NEE. The estimated velocity at GHUT station with respect to IISC is 12 ± 1 mm/yr towards SW. Besides, we have also examined anomalous changes in the time series of GHUT before, after and during the occurrences of local earthquakes by considering the empirical strain radius; such that, a possible relationship between the strain radius and the occurrences of earthquakes have been explored. We considered seven local earthquakes on the basis of Dobrovolsky strain radius condition having magnitude from 4.5 to 5.7, which occurred from 2007 to 2011. Results show irrespective of the station strain radius, pre-seismic surface deformational anomalies are observed roughly 70 to 80 days before the occurrence of a Moderate or higher magnitude events. This has been observed for the cases of those events originated from the Uttarakashi and the Chamoli seismic zones in the Garhwal and Kumaun Himalaya. Occurrences of short (< 100 days) and long (two years) inter-seismic events in the Garhwal region plausibly regulating and diffusing the regional strain accumulation.

scholarly journals Benchmarking homogenization algorithms for monthly data

2012 ◽  
Vol 8 (1) ◽  
pp. 89-115 ◽  
Author(s):  
V. K. C. Venema ◽  
O. Mestre ◽  
E. Aguilar ◽  
I. Auer ◽  
J. A. Guijarro ◽  
...  
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Linear Trend ◽  
Simulated Data ◽  
Integrated Approach ◽  
Global Network ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Simulated Network ◽  
The Individual

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

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