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

Kłos, Anna; Bogusz, Janusz; Moreaux, Guilhem

doi:10.1007/s00190-017-1092-0

Cited by 10 publications

(5 citation statements)

References 57 publications

(90 reference statements)

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“…Different geodetic observations are characterized by different kinds of noise. As an example, zenith total delays are well-approximated by the autoregressive process (Klos et al 2018d), while DORIS observations are represented by pure powerlaw noise (Klos et al 2018a). For all these cases, the AWF constitutes an alternative approach to how to split the original geodetic signal into seasonal signals and noise.…”

Section: Discussionmentioning

confidence: 99%

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

et al. 2018

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Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used in geodesy to denoise gravity records, to model the seasonal signals in the GPS position time series. To model the time-varying part of the signal, a first-order autoregressive process is employed. The WF is then adapted to the noise level of the data to model only those time variabilities which are significant. Synthetic and real GPS data is used to demonstrate that this variation of the WF leaves the underlying noise properties intact and provides optimal modeling of seasonal signals. This methodology is referred to as the adaptive WF (AWF) and is both easy to implement and fast, due to the use of the fast Fourier transform method.

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Section: Discussionmentioning

confidence: 99%

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

et al. 2018

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“…We employ the vertical (Up) position changes and construct the time series model using both deterministic and stochastic parts, similarly to [51]:…”

Section: Methodsmentioning

confidence: 99%

Optimal Strategy of a GPS Position Time Series Analysis for Post-Glacial Rebound Investigation in Europe

2019

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We describe a comprehensive analysis of the 469 European Global Positioning System (GPS) vertical position time series. The assumptions we present should be employed to perform the post-glacial rebound (PGR)-oriented comparison. We prove that the proper treatment of either deterministic or stochastic components of the time series is indispensable to obtain reliable vertical velocities along with their uncertainties. The statistical significance of the vertical velocities is examined; due to their small vertical rates, 172 velocities from central and western Europe are found to fall below their uncertainties and excluded from analyses. The GPS vertical velocities reach the maximum values for Scandinavia with the maximal uplift equal to 11.0 mm/yr. Moreover, a comparison between the GPS-derived rates and the present-day motion predicted by the newest Glacial Isostatic Adjustment (GIA) ICE-6G_C (VM5a) model is provided. We prove that these rates agree at a 0.5 mm/yr level on average; the Sweden area with the most significant uplift observed agrees within 0.2 mm/yr. The largest discrepancies between GIA-predicted uplift and the GPS vertical rates are found for Svalbard; the difference is equal to 6.7 mm/yr and arises mainly from the present-day ice melting. The GPS-derived vertical rates estimated for the southern coast of the Baltic Sea are systematically underestimated by the GIA prediction by up to 2 mm/yr. The northern British Isles vertical rates are overestimated by the GIA model by about 0.5 mm/yr. The area of the Netherlands and the coastal area of Belgium are both subsiding faster than it is predicted by the GIA model of around 1 mm/yr. The inland part of Belgium, Luxemburg and the western part of Germany show strong positive velocities when compared to the GIA model. Most of these stations uplift of more than 1 mm/yr. It may be caused by present-day elastic deformation due to terrestrial hydrology, especially for Rhein basin, or non-tidal atmospheric loading, for Belgium and Luxembourg.

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“…We apply the same approach to the DORIS data from Klos et al. (2017). For each tide‐gauge location, we check all available GNSS stations within a 50 km radius for which at least 4 years of data is available, and for which the estimated uncertainty in the vertical trend is smaller than 2 mm year −1 .…”

Section: Methodsmentioning

confidence: 99%

“…However, these estimates are based on GNSS records that come with a substantial uncertainty. Next to the GNSS stations that we use to estimate local VLM, the DORIS station DAKA also shows a small VLM signal (Klos et al., 2017), but again, there is no overlap between the DORIS and the Gravity Recovery and Climate Experiment records to account for present‐day mass redistribution effects. Also Le Cozannet et al.…”

Section: An Observation‐based Estimate Of Sea‐level Rise In the Southmentioning

confidence: 99%

Constraining 20th‐Century Sea‐Level Rise in the South Atlantic Ocean

et al. 2021

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Sea level in the South Atlantic Ocean has only been measured at a small number of tide‐gauge locations, which causes considerable uncertainty in 20th‐century sea‐level trend estimates in this basin. To obtain a better‐constrained sea‐level trend in the South Atlantic Ocean, this study aims to answer two questions. The first question is: can we combine new observations, vertical land motion estimates, and information on spatial sampling biases to obtain a likely range of 20th‐century sea‐level rise in the South Atlantic? We combine existing observations with recovered observations from Dakar and a high‐resolution sea‐level reconstruction based on salt‐marsh sediments from the Falkland Islands and find that the rate of sea‐level rise in the South Atlantic has likely been between 1.1 and 2.2 mm year−1 (5%–95% confidence intervals), with a central estimate of 1.6 mm year−1. This rate is on the high side, but not statistically different compared to global‐mean trends from recent reconstructions. The second question is: are there any physical processes that could explain a large deviation from the global‐mean sea‐level trend in the South Atlantic? Sterodynamic (changes in ocean dynamics and steric effects) and gravitation, rotation, and deformation effects related to ice mass loss and land water storage have probably led to a 20th‐century sea‐level trend in the South Atlantic above the global mean. Both observations and physical processes thus suggest that 20th‐century sea‐level rise in the South Atlantic has been about 0.3 mm year−1 above the rate of global‐mean sea‐level rise, although even with the additional observations, the uncertainties are still too large to distinguish a statistically significant difference.

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

Cited by 10 publications

References 57 publications

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Optimal Strategy of a GPS Position Time Series Analysis for Post-Glacial Rebound Investigation in Europe

Constraining 20th‐Century Sea‐Level Rise in the South Atlantic Ocean

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