Quality assessment of IDS contribution to ITRF2014 performed by DGFI-TUM

Bloßfeld, M; Seitz, Matthias; Angermann, D; Moreaux, Guilhem

doi:10.1016/j.asr.2015.12.016

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…This period is related to the revolution of the TOPEX/Poseidon, Jason-1 and Jason-2 satellites' orbits (− 3.0679 • /day). Similar results were reported for the DORIS time series by Le Bail (2006), Khelifa (2016), Bloßfeld et al (2016) and Tornatore et al (2016). The nodal period for the remaining satellites is nearly equal to the annual period which causes draconitic periods to tend to infinity .…”

Section: Seasonal Oscillationssupporting

confidence: 74%

“…We aimed at a proper description of the stochastic character of the DORIS data to model the linear velocity together with its most reliable error. It is in some ways a continuation of the studies presented by Moreaux et al (2016), who estimated the velocity along with its statistics, but a pure white noise was employed. Our statistics refer to a covariance matrix of observations being described as in Eq.…”

Section: Discussionmentioning

confidence: 99%

“…Both Bloßfeld et al (2016) and Tornatore et al (2016) identified signals at a frequency of 14 days in the DORIS time series. However, the origin of such a signal can stem from the sampling interval of 7 days of the DORIS coordinate time series or from mismodelling of tidal constituents as, for example, M2 or O1.…”

Section: Seasonal Oscillationsmentioning

confidence: 99%

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Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Kłos

Bogusz

Moreaux

2017

J Geod

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This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.

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Section: Seasonal Oscillationssupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

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Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Kłos

Bogusz

Moreaux

2017

J Geod

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“…The time series of transformation parameters and station residuals can be analyzed in order to check the quality of the solution. This has been done by Bloßfeld et al (2016) for IDS, by Rebischung et al (2015) for IGS and by Seitz et al (2015) for the four techniques realizing the DGFI2014 TRF. The same periodic effects in station coordinates residuals have been observed in the datum parameters and EOP time series.…”

Section: Time Series Data Set and Modelingmentioning

confidence: 99%

“…Orbital effects can impact both the coordinates and the datum; for example GNSS orbits radial error can affect the scale factor, with a correlated impact on the estimated Up. DORIS and GPS draconitic harmonics have been detected both on station coordinates residuals and on datum parameters (Rebischung et al, 2015;Bloßfeld et al, 2016). Due to this correlation, the estimated amplitudes can be biased or scaled, while the frequency and the phase are datum invariant.…”

Section: Time Series Data Set and Modelingmentioning

confidence: 99%

Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS

Tornatore

Kayıkçı

Roggero

2016

Advances in Space Research

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Please cite this article as: Tornatore, V., Kayıkç ı, E.T., Roggero, M., Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS, Advances in Space Research (2016), doi: http://dx.doi.org/10. 1016/ j.asr.2016.07.016 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The analysis method has proven to be particularly suited to obtain quasi-cyclostationary residuals which are an important property to carry out a reliable harmonic analysis. We looked for common signatures among the three techniques. Frequencies and amplitudes of the detected signals have been reported along with their percentage of incidence. Our comparison shows that two of the estimated signals, having one-year and 14 days periods, are common to all the techniques. Different hypotheses on the nature of the signal having a period of 14 days are presented. As a final check we have compared the estimated velocities and their Standard Deviations (STD) for the sites that co-located the VLBI, GNSS and DORIS stations, obtaining a good agreement among the three techniques both in the horizontal (1.0 mm/yr mean STD) and in the vertical (0.7 mm/yr mean STD) component, although some sites show larger STDs, mainly due to lack of data, different data spans or noisy observations. Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS

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Rank defect analysis and the realization of proper singularity in normal equations of geodetic networks

Kotsakis

Chatzinikos

2017

J Geod

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Quality assessment of IDS contribution to ITRF2014 performed by DGFI-TUM

Cited by 13 publications

References 15 publications

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS

Rank defect analysis and the realization of proper singularity in normal equations of geodetic networks

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