The Magnitude of Diurnal/Semidiurnal Atmospheric Tides (S1/S2) and Their Impacts on the Continuous GPS Coordinate Time Series

Abstract

The site displacement due to diurnal and semidiurnal atmospheric tides (S1/S2) is often neglected in the routine precise GPS data processing. We recall the S1/S2 modeling method and show the magnitude of the S1/S2 tides under the Center of Mass (CM) frame. The results show that the annual amplitudes caused by both S1 and S2 tides exceed 1 mm for stations near the equator. The impact of S1/S2 on the 24-h Global Positioning System (GPS) solution is typically at the sub-mm level, and the scatter of the S1/S2 caused displacement difference increases with the decreasing latitude for northern hemisphere stations, among which the maximum Standard Deviation (STD) reaches up to 1.5 mm, 1 mm and 0.7 mm for the Up, East and North components, respectively, at low-latitude stations. We also find that 65% of the stations’ vertical velocity change caused by S1/S2 is larger than 1%, among which the maximum velocity variation rate reaches more than 40% for some coastal/island stations, while stations with the weighted root mean square reduced account for 65%, 39%, and 38% for the up, east, and north components respectively, in particular for most coastal/island stations. Furthermore, the S1/S2 correction could partly reduce the annual and the semi-annual signals of the global stacked vertical component together with the semi-annual amplitude of the east component. The power of some anomalous harmonics of 1.04 cycle per year also decreased a lot. These results further prove the benefits of S1/S2 correction in the precise GPS data processing.

Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series

“…However, in addition to ATML signals and other known geophysical effects, e.g., continental water storage and non-tidal ocean tidal loading induced displacements (van Dam et al, 1997(van Dam et al, , 2001(van Dam et al, , 2012Fritsche et al, 2012;Williams and Penna, 2011;Yuan et al, 2018), GNSS-related systematic errors also contribute to the observed non-linear variations in GNSS time series (Ray et al, 2008(Ray et al, , 2013. Previous results have shown that imperfect GNSS data processing models and strategies could result in spurious non-linear displacements of GNSS stations, which mask real crustal displacements, thus affecting the comparison between GNSS and predicted loading signals (Deng et al, 2017;Li et al, 2014Li et al, , 2018King and Watson, 2010;Penna and Stewart, 2003;Penna et al, 2007;.…”

Selection of noise models for GNSS coordinate time series based on model averaging algorithm

“…used threshold is the Akaike weight 1%. The results are shown in table 5. One can see that the ratio of the number of sites that can be applied to a model averaging algorithm is all over 50% per direction.…”

“…In recent years, an increasing number of newly built GNSS sites have been put into operation as well, these GNSS sites provide valuable basic data for scientific research. Currently, high-precision GNSS time series obtained through GNSS observations processing are widely used by researchers in scientific research in fields such as geophysics, geology, seismology, geodynamics and atmospheric and marine physics [1][2][3][4][5][6][7][8][9][10][11][12], as well as in infrastructure construction fields such as monitoring of dams, bridges, and tunnels [13,14].…”

Impact of Meteorological Uncertainties in the Methane Retrieval Ground Segment of the MERLIN Lidar Mission

“…The differences interpreted as being related to the representation of atmospheric tides may induce regional biases up to 3 ppm on the mole fraction of methane with respect to dry air averaged over the atmospheric column (see Figure 5). Indeed, time interpolation creates systematic errors for insufficiently sampled periodic components such as atmospheric tides, as it is well documented in other contexts [24].…”