ITRF2014, Earth Figure Changes, and Geocenter Velocity: Implications for GIA and Recent Ice Melting

Métivier, Laurent; Rouby, Hélène; Rebischung, Paul; Altamimi, Z.

doi:10.1029/2019jb018333

Cited by 12 publications

(8 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The values are shown in Tables 1 , 2 and 3 in Appendix G. Overall, the solutions agree with the results published by Perosanz ( 2019 ) and Métivier et al. ( 2020 ). The good agreement between Hector and GMWMX-1 can be seen visually for the East, North, and Up components.…”

Section: Resultssupporting

confidence: 89%

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series

Cucci

Voirol

Kermarrec

et al. 2023

J Geod

View full text Add to dashboard Cite

The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order $$\mathcal {O}\{\log (n) n\}$$ O { log ( n ) n } for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10–1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

show abstract

Section: Resultssupporting

confidence: 89%

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series

Cucci

Voirol

Kermarrec

et al. 2023

J Geod

View full text Add to dashboard Cite

show abstract

“…Overall, the solutions agree with the results published by Perosanz (2019) and Métivier et al (2020). The good agreement between Hector and GMWMX-1 can be seen visually for the East, North and Up components.…”

Section: Case Studysupporting

confidence: 87%

The Generalized Method of Wavelet Moments with Exogenous Inputs: a Fast Approach for the Analysis of GNSS Position Time Series

Cucci¹,

Voirol²,

Kermarrec³

et al. 2022

Preprint

View full text Add to dashboard Cite

The Global Navigation Satellite System (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow studying global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogeneous variables: it is semiparametric, computationally efficient and scalable. Unlike standard methods such as the widely used Maximum Likelihood Estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard methods, the processing time of the GMWMX is over 1000 times faster considering time series recorded over 20 years and allows the estimation of large scale problems within minutes on a standard computer. We validate the performances of our method via Monte-Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power-law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

show abstract

“…We also note that other estimates based on analysis of Gravity Recovery and Climate Experiment (data over the period January 2003 to August 2016 (Sun et al, 2019) suggest a slightly higher value of ±0.5 mm/year and that this is an area of ongoing investigation. A recent study by Métivier et al (2020) has obtained a geocenter velocity reaching 0.9 ± 0.5 mm/year in 2013 with a Z component of 0.8 ± 0.4 mm/year from spherical harmonic coefficients estimated from ITRF2014 Global Navigation Satellite System (GNSS) velocities, over the period 1994.0-2015.1, which is larger than previous estimates and attributed to accelerations of recent global ice melting as a purely elastic response. We consider this further below and note that accurately determining small rates of change from GPS coordinate time series is complicated by reference frame errors and inconsistencies.…”

Section: Introductionmentioning

confidence: 73%

Present‐Day Vertical Land Motion of Australia From GPS Observations and Geophysical Models

2020

View full text Add to dashboard Cite

The secular rate of Australia's vertical surface deformation due to past ice‐ocean loading changes is not consistent with present vertical velocities observed by a previously sparse network of Global Positioning System (GPS) sites. Current understanding of the Earth's rheology suggests that the expected vertical motion of the crust should be close to zero given that Australia is located in the far field of past ice sheet loading. Recent GPS measurements suggest that the vertical motion of the Australian continent at permanent sites is between 0 and −2 mm/year. Here we investigate if vertical deformation due to previous ice sheet loading can be recovered in the time series of Australian GPS sites through enlarging the number of sites compared to previous studies from ~20 to more than 100 and through the application of improved data filtering. We apply forward geophysical models of elastic surface displacement induced by atmospheric, hydrologic, nontidal ocean, and ice loading and use independent component analysis as a spatiotemporal filter that includes multivariate regression to consider temporally correlated noise in GPS. Using this approach, the common mode error is identified, and subsequent multivariate regression leads to an average reduction in trend uncertainty of ~35%. The average vertical subsidence of the Australian continent is substantially different to vertical motion predicted by glacial isostatic adjustment and surface mass transport models.

show abstract

ITRF2014, Earth Figure Changes, and Geocenter Velocity: Implications for GIA and Recent Ice Melting

Cited by 12 publications

References 72 publications

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series

The Generalized Method of Wavelet Moments with Exogenous Inputs: a Fast Approach for the Analysis of GNSS Position Time Series

Present‐Day Vertical Land Motion of Australia From GPS Observations and Geophysical Models

Contact Info

Product

Resources

About