Abstract

In recent years, several studies have demonstrated the sensitivity of Global Navigation Satellite System (GNSS) station time series to displacements caused by atmospheric pressure loading (APL). Different methods to take the APL effect into account are used in these studies: applying the corrections from a geophysical model on weekly mean estimates of station coordinates, using observation-level corrections during data analysis, or solving for regression factors between the station displacement and the local pressure. The Center for Orbit Determination in Europe (CODE) is one of the global analysis centers of the International GNSS Service (IGS). The current quality of the IGS products urgently asks to consider this effect in the regular processing scheme. However, the resulting requirements for an APL model are demanding with respect to quality, latency, and—regarding the reprocessing activities—availability over a long time interval (at least from 1994 onward). The APL model of Petrov and Boy (J Geophys Res 109:B03405, 2004) is widely used within the VLBI community and is evaluated in this study with respect to these criteria. The reprocessing effort of CODE provides the basis for validating the APL model. The data set is used to solve for scaling factors for each station to evaluate the geophysical atmospheric non-tidal loading model. A consistent long-term validation of the model over 15 years, from 1994 to 2008, is thus possible. The time series of 15 years allows to study seasonal variations of the scaling factors using the dense GNSS tracking network of the IGS. By interpreting the scaling factors for the stations of the IGS network, the model by (2004) is shown to meet the expectations concerning the order of magnitude of the effect at individual stations within the uncertainty given by the GNSS data processing and within the limitations due to the model itself. The repeatability of station coordinates improves by 20% when applying the effect directly on the data analysis and by 10% when applying a post-processing correction to the resulting weekly coordinates compared with a solution without taking APL into account.

Highlights

  • The station coordinates established by the space-geodetic techniques are affected by many effects resulting in geometrical site displacements at different time scales and magnitudes

  • In summary over all about 50 stations in Fig. 12 we find only one case where an ignored atmospheric pressure loading (APL) effect gives the best result and only three examples, where the repeatability favors correcting the APL by mean corrections on the estimated weekly coordinates

  • Because the Global Navigation Satellite Systems (GNSS) orbits cannot be corrected on the basis of weekly mean station coordinates these results are another important argument for applying the APL correction directly to the observations during the data processing

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Summary

Introduction

The station coordinates established by the space-geodetic techniques are affected by many effects resulting in geometrical site displacements at different time scales and magnitudes. Because CODE acts as one of the global analysis centers (AC) of the IGS, some additional requirements need to be considered to cover the operational aspects of this service: for validating APL corrections, to compare different methods to consider the APL effect in the GNSS data analysis, and to assess the impact of APL on some selected parameters of the GNSS data processing. 5 by comparing the repeatabilities of the coordinate time series based on the different strategies of applying the APL corrections Because these results are derived from processing a global and dense GNSS network, the impact of correcting or not correcting for the APL effect on global parameters (datum definition and GNSS satellite orbits) is discussed in Sect. The set of GNSS stations included in the CODE reprocessing effort (details are provided in Sect. 2.1) is used here

GNSS solution
Time series of atmospheric pressure loading corrections
Comparisons based on weekly GNSS solutions
Comparisons by direct estimation of scaling factors
Atmospheric pressure loading corrections based on local pressure time series
Comparison of APL corrections with local pressure data
Regression factors from GNSS data analysis
Effects seen in the repeatability of station coordinates
Influence on geodetic datum definition
Influence on GNSS-satellite orbits
Findings
Summary
Full Text
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