Abstract
Thanks to the increasing number of permanent GNSS stations in Europe and their long records, we computed position solutions for more than 1000 stations over the last two decades using the REPRO3 orbit and clock products from the IGS CNES-CLS (GRGS) Analysis Center. The velocities, which are mainly due to tectonics and glacial isostatic adjustment (GIA), and the annual solar cycle have been estimated using weighted least squares. The interannual variations have been accounted for in the stochastic model or in the deterministic model. We demonstrated that the velocity and annual cycle, in addition to their uncertainties, depend on the estimation method we used and that the estimation of GPS draconitic oscillations minimises biases in the estimation of annual solar cycle displacements. The annual solar cycle extracted from GPS has been compared with that from loading estimates of several hydrological models. If the annual amplitudes between GPS and hydrological models match, the phases of the loading models were typically in advance of about 1 month compared to GPS. Predictions of displacements modelled from GRACE observations did not show this phase shift. We also found important discrepancies at the interannual frequency band between GNSS, loading estimates derived from GRACE, and hydrological models using principal component analysis (PCA) decomposition. These discrepancies revealed that GNSS position variations in the interannual band cannot be systematically interpreted as a geophysical signal and should instead be interpreted in terms of autocorrelated noise.
Highlights
Even if our statistical method using the dispersion of instantaneous velocity provides more realistic uncertainties than that directly given by the weighted least squares method (WLS) method, we observe in Figure 6 that the uncertainties for tiasd are three to four times smaller than those for the WSL + MLE method, which contain a complete stochastic part
These small uncertainties are accompanied by important differences in the velocity values compared to that estimated by CATS: the differences are nested in an interval of ±1.5 mm/y, which is quite important considering the precision requirement on the terrestrial reference frame of 0.1 mm/y [12]
Interannual signals in global navigation satellite systems (GNSS) time series are due to geophysical deformations in only few special cases: regions with melting ice caps or tectonic activity such as slow slip events [4,74,75,76]
Summary
All components are used to determine reference frames, such as the International Terrestrial Reference Frame (ITRF) [12]. Aside from these signals, GNSS position time series contain large broadband variations of unknown origin, typically represented by the combination of power-law (PL) and white noise (WH) models [13], which impacts the determination and interpretation of other parameters, especially the velocity and its uncertainty [14,15,16], and possibly the seasonal signal [17]. [23] shows that the assessment
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