Subseasonal GNSS positioning errors

Abstract

Global Navigation Satellite System (GNSS) station coordinate errors over seasonal and longer time scales are known to be spatially and temporally correlated with flicker noise spectra. Overlaying this are strong annual and semiannual variations that cannot be explained by any single phenomenon. Next most prominent are harmonics of the GPS draconitic year with periods of (351.4/N) days. One explanation is that errors in the standard model for Earth orientation parameter (EOP) tidal variations near 12 and 24 h periods are absorbed into the resonant GPS orbit and daily EOP estimates, resulting mainly in draconitic and fortnightly alias signatures for 24 h product sampling. With the change in International GNSS Service (IGS) station coordinates from weekly to daily resolution in August 2012, it is now possible to study subseasonal performance. All IGS Analysis Centers (ACs) show fortnightly signals, but the resolution will not be sufficient to distinguish direct from aliased subdaily tidal error sources till two more years of data are available. Nevertheless, aliased errors from the subdaily EOP tide model are expected. All but one of the ACs that includes GLONASS data have signals at ~8 day periods, the ground repeat period for GLONASS orbits. This most likely arises from larger geographically correlated orbit errors for GLONASS. Two ACs possess unique short‐period features that appear to be caused by peculiarities of their analysis strategies.

Global Glacial Isostatic Adjustment Constrained by GPS Measurements: Spherical Harmonic Analyses of Uplifts and Geopotential Variations

“…By comparing Figures 1-6, we can see notable differences between the GIA uplift rates derived from model predictions and GPS-based results, and GIA models are good at describing large-scale and especially global GIA patterns while GPS data are better at local details although the GPS rates can also be affected by unrelated local effects [17,18]. Bearing this in mind, we developed the following spherical harmonic analyses to assimilate GPS data into GIA model outputs (predictions).…”

“…However, the agreements between model-based and GPS-based results are usually limited due to a number of factors (some are discussed in the following sections). It is believed that the GIA models are good at describing large-scale and especially global GIA patterns while GPS data are better at local details although the GPS rates can also be affected by unrelated local effects [17,18].Arguing that the GIA-induced crust uplifts are dominated by the Earth's viscosity mode M0 [19], Wahr et al [20,21] proposed an approximate theory to infer changes in Stokes coefficients from the measured GIA uplift rates. Later, Purcell et al [22] refined the theory relying on viscous load Love numbers (also see Section 2.3 for some details).…”

“…However, the agreements between model-based and GPS-based results are usually limited due to a number of factors (some are discussed in the following sections). It is believed that the GIA models are good at describing large-scale and especially global GIA patterns while GPS data are better at local details although the GPS rates can also be affected by unrelated local effects [17,18].…”

CODE’s new solar radiation pressure model for GNSS orbit determination

“…The periods of the spectral lines could be attributed to the so-called draconitic GPS year, which, due to the regression of the satellite nodes on the equator, is about 14 days shorter than the sidereal year. The effects are small: the amplitudes of the spectral lines, which can be reconstructed from the power spectra in Ray et al (2008) and in Ray et al (2013), are only about a factor of 1-3 above the noise level. Griffith and Ray (2012) state that draconitic errors are contained in virtually all IGS products.…”

Noise analysis for environmental loading effect on GPS time series

“…For instance, the Global Geophysical Fluid Center (GGFC, Jiang et al, 2013) and the Quasi-Observation Combination Analysis based loading model (QLM) are widely used to estimate surface displacements and correct nonlinear INTRODUCTION The Global Positioning System (GPS) has seen tremendous advances in measurement precision and accuracy (Bock et al, 2000;Williams et al, 2004;Bos et al, 2008;Ray et al, 2013;Li et al, 2015). To improve the accuracy and stability of the GPS time series, any possible nuisance parameters or errors need to be minimized.…”