Error Analysis of the IGS repro2 Station Position Time Series

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Paul Rebischung1, Jim Ray2, Clément Benoist1, Laurent Metivier1 and Zuheir Altamimi1, (1)IGN Institut National de l'Information Géographique et Forestière, Paris Cedex 13, France, (2)Retired, Washington, DC, United States
Eight Analysis Centers (ACs) of the International GNSS Service (IGS) have completed a second reanalysis campaign (repro2) of the GNSS data collected by the IGS global tracking network back to 1994, using the latest available models and methodology. The AC repro2 contributions include in particular daily terrestrial frame solutions, the first time with sub-weekly resolution for the full IGS history. The AC solutions, comprising positions for 1848 stations with daily polar motion coordinates, were combined to form the IGS contribution to the next release of the International Terrestrial Reference Frame (ITRF2014). Inter-AC position consistency is excellent, about 1.5 mm horizontal and 4 mm vertical. The resulting daily combined frames were then stacked into a long-term cumulative frame assuming generally linear motions, which constitutes the GNSS input to the ITRF2014 inter-technique combination. A special challenge involved identifying the many position discontinuities, averaging about 1.8 per station.

A stacked periodogram of the station position residual time series from this long-term solution reveals a number of unexpected spectral lines (harmonics of the GPS draconitic year, fortnightly tidal lines) on top of a white+flicker background noise and strong seasonal variations. In this study, we will present results from station- and AC-specific analyses of the noise and periodic errors present in the IGS repro2 station position time series. So as to better understand their sources, and in view of developing a spatio-temporal error model, we will focus in particular on the spatial distribution of the noise characteristics and of the periodic errors. By computing AC-specific long-term frames and analyzing the respective residual time series, we will additionally study how the characteristics of the noise and of the periodic errors depend on the adopted analysis strategy and reduction software.