Using continuous GPS and absolute gravity to separate vertical land movements and changes in sea-level at tide-gauges in the UK

Teferle, Felix Norman; Bingley, R. M.; Williams, Simon; Baker, T. F.; Dodson, Alan

doi:10.1098/rsta.2006.1746

Cited by 61 publications

(60 citation statements)

References 25 publications

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“…It is here worthwhile mentioning the related work on the developing and applying of the spatial filtering to the geophysical applications. We refer to Wdowinski et al (1997), Nikolaidis (2002), Dong et al (2006) and Teferle et al (2006). The objective of this paper is four-fold.…”

Section: Introductionmentioning

confidence: 99%

Noise in multivariate GPS position time-series

Amiri-Simkooei

2008

J Geod

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A methodology is developed to analyze a multivariate linear model, which occurs in many geodetic and geophysical applications. Proper analysis of multivariate GPS coordinate time-series is considered to be an application. General, special, and more practical stochastic models are adopted to assess the noise characteristics of multivariate time-series. The least-squares variance component estimation (LS-VCE) is applied to estimate full covariance matrices among different series. For the special model, it is shown that the multivariate time-series can be estimated separately, and that the (cross) correlation between series propagates directly into the correlation between the corresponding parameters in the functional model. The time-series of five permanent GPS stations are used to show how the correlation between series propagates into the site velocities. The results subsequently conclude that the general model is close to the more practical model, for which an iterative algorithm is presented. The results also indicate that the correlation between series of different coordinate components per station is not significant. However, the spatial correlation between different stations for individual components is significant (a correlation of 0.9 over short baselines) both for white and for colored noise components.

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Section: Introductionmentioning

confidence: 99%

Noise in multivariate GPS position time-series

Amiri-Simkooei

2008

J Geod

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“…Peltier, 2001) or the measurements by advanced geodetic techniques (e.g. Teferle et al, 2006;Snay et al, 2007). The accuracy of the adjusted rates will depend upon the accuracy of the land movement corrections.…”

Section: Introductionmentioning

confidence: 99%

Evidence for the accelerations of sea level on multi‐decade and century timescales

Woodworth

White

Jevrejeva

et al. 2008

Intl Journal of Climatology

214

180

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ABSTRACT:A modification in the rate of change of sea level (i.e. an 'acceleration' or 'nonlinear trend') is an important climate-related signal, which requires confirmation and explanation. In this study, the evidence for accelerations in regional and global average sea level on timescales of several decades and longer is reviewed by inter-comparison of the recent findings of different researchers and by inspection of original tide gauge records. Most sea-level data originate from Europe and North America, and both the sets display evidence for a positive acceleration, or 'inflexion', around 1920-1930 and a negative one around 1960. These inflexions are the main contributors to reported accelerations since the late 19th century, and to decelerations during the mid-to late 20th century. However, these characteristic features are not always found in records from other parts of the world. Although some aspects of the sea-level time series are consistent with changes in rates of globally averaged temperature changes, volcanic eruptions and natural climate variability, modelling undertaken so far has been unable to describe these features adequately. This emphasizes the need for a major enhancement of the sea-level data set, especially for those parts of the world without long tide gauge records, in order to obtain greater insight into the spatial dependence of accelerations. A number of complementary methods must be employed, of which salt marsh techniques offer the possibility of obtaining time series similar to those that would have been obtained from coastal tide gauges.

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“…This type of analysis was first used to study the Earth's crustal movements caused by seismic deformations [13], as well as filtering of daily GPS solutions [12]. Stacking was also successfully used to subtract the common mode error [14][15][16], through analysis of subnets consisting of a limited number of permanent stations (3 to 10). An improvement of continuous daily GPS solutions was shown in [14] by estimating the weighted root-mean-square (WRMS) of raw and filtered residual coordinate time series twice.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal filtering for determination ofcommon mode error in regional GNSS networks

et al. 2015

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Abstract:The spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually estimated with the regional spatial filtering, such as the "stacking". In this paper, we show the stacking approach for the set of ASG-EUPOS permanent stations, assuming that spatial distribution of the CME is uniform over the whole region of Poland (more than 600 km extent). The ASG-EUPOS is a multifunctional precise positioning system based on the reference network designed for Poland. We used a 5-year span time series (2008)(2009)(2010)(2011)(2012) of daily solutions in the ITRF2008 from Bernese 5.0 processed by the Military University of Technology EPN Local Analysis Centre (MUT LAC). At the beginning of our analyses concerning spatial dependencies, the correlation coefficients between each pair of the stations in the GNSS network were calculated. This analysis shows that spatio-temporal behaviour of the GPS-derived time series is not purely random, but there is the evident uniform spatial response. In order to quantify the influence of filtering using CME, the norms L 1 and L 2 were determined. The values of these norms were calculated for the North, East and Up components twice: before performing the filtration and after stacking. The observed reduction of the L 1 and L 2 norms was up to 30% depending on the dimension of the network. However, the question how to define an optimal size of CME-analysed subnetwork remains unanswered in this research, due to the fact that our network is not extended enough.

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Using continuous GPS and absolute gravity to separate vertical land movements and changes in sea-level at tide-gauges in the UK

Cited by 61 publications

References 25 publications

Noise in multivariate GPS position time-series

Noise in multivariate GPS position time-series

Evidence for the accelerations of sea level on multi‐decade and century timescales

Spatio-temporal filtering for determination ofcommon mode error in regional GNSS networks

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