Systematic Inconsistencies Between VLBI CRF and TRF Solutions Caused by Different Analysis Options

We assess the systematics between Very Long Baseline Interferometry (VLBI) terrestrial and celestial reference frames (TRF and CRF) solutions caused by different analysis options. Comparisons are achieved by sequential variation of options relative to a reference solution, which fulfills the requirements of the International VLBI Service for Geodesy and Astrometry (IVS) analysis coordination. Neglecting the total NASA/GSFC Data Assimilation Office (DAO) a priori gradients causes the largest effects: Mean source declinations differ up to 0.2mas, station positions are shifted southwards, and heights are systematically larger by up to 3mm, if no a priori gradients are applied. The effect is explained with the application of gradient constraints. Antenna thermal deformations, atmospheric pressure loading, and the atmosphere pressure used for hydrostatic delay modeling still exhibit significant effects on the TRF, but corresponding CRF differences (about 10μas) are insignificant. The application of NMF atmosphere mapping functions can systematically affect source declinations up to 30μas, which is between the estimated axes stability (10μas) and the mean positional accuracy (40μas) specified for the ICRF2. Further significant systematic effects are seasonal variations of the terrestrial network scale (±1mm) neglecting antenna thermal deformations, and seasonal variations of station positions, primarily of the vertical component up to 5mm, neglecting atmospheric loading. The application of NMF instead of VMF1 can result in differences of station heights up to 6mm, but no overall global systematic can be found. Using constant atmosphere pressure values for the determination of hydrostatic zenith delays systematically deforms the TRF: station height differences mostly show the same sign with absolute values exceeding 1mm.

[1]  C. Bizouard,et al.  The Combined Solution C04 for Earth Orientation Parameters Consistent with International Terrestrial Reference Frame 2005 , 2009 .

[3]  Axel Nothnagel,et al.  Conventions on thermal expansion modelling of radio telescopes for geodetic and astrometric VLBI , 2009 .

[4]  Harald Schuh,et al.  Impact of Analysis Options on the TRF, CRF and Position Time Series Estimated from VLBI , 2006 .

[5]  Thomas A. Herring,et al.  Detection of atmospheric pressure loading using very long baseline interferometry measurements , 1994 .

[6]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[7]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[8]  Thomas A. Herring,et al.  Modeling of nutation and precession: New nutation series for nonrigid Earth and insights into the Ea , 2002 .

[9]  A. Niell Global mapping functions for the atmosphere delay at radio wavelengths , 1996 .

[10]  E. F. Arias,et al.  THE SECOND REALIZATION OF THE INTERNATIONAL CELESTIAL REFERENCE FRAME BY VERY LONG BASELINE INTERFEROMETRY , 2015 .

[11]  Xavier Collilieux,et al.  Reference Frames for Applications in Geosciences , 2013 .

[12]  Harald Schuh,et al.  Atmospheric loading corrections at the observation level in VLBI analysis , 2009 .

[13]  Harald Schuh,et al.  Modeling thermal deformation of VLBI antennas with a new temperature model , 2007 .

[14]  D. S. MacMillan,et al.  Using meteorological data assimilation models in computing tropospheric delays at micrwave frequencies , 1998 .

[15]  D. S. MacMillan,et al.  Atmospheric gradients from very long baseline interferometry observations , 1995 .

[16]  Claudio Abbondanza,et al.  Gravity-dependent signal path variation in a large VLBI telescope modelled with a combination of surveying methods , 2009 .

[17]  H. Schuh,et al.  Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data , 2006 .

[18]  P. Sarti,et al.  Effects of illumination functions on the computation of gravity-dependent signal path variation models in primary focus and Cassegrainian VLBI telescopes , 2010 .

[19]  I. Shapiro,et al.  Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length , 1985 .

[20]  Claudio Abbondanza,et al.  Height bias and scale effect induced by antenna gravitational deformations in geodetic VLBI data analysis , 2011 .

[21]  H. Schuh,et al.  Short Note: A global model of pressure and temperature for geodetic applications , 2007 .

[22]  H. Schuh,et al.  Combination of long time-series of troposphere zenith delays observed by VLBI , 2007 .

[23]  Leonid Petrov,et al.  Study of the atmospheric pressure loading signal in very long baseline interferometry observations , 2003, physics/0311096.

[24]  H. Schuh,et al.  Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium‐Range Weather Forecasts operational analysis data , 2006 .

[25]  The impact of mapping functions for the neutral atmosphere based on numerical weather models in GPS data analysis , 2007 .

[26]  Robert Heinkelmann,et al.  Sub-Commission 1.4 Interaction of Celestial and Terrestrial Reference Frames , 2007 .

[27]  Harald Schuh,et al.  Effect of different tropospheric mapping functions on the TRF, CRF and position time-series estimated from VLBI , 2007 .

[28]  D. S. MacMillan,et al.  Atmospheric pressure loading parameters from very long baseline interferometry observations , 1994 .

[29]  H. Schuh,et al.  The Effect of Meteorological Input Data on the VLBI Reference Frames , 2009 .

[30]  O. Francis,et al.  Modelling the global ocean tides: modern insights from FES2004 , 2006 .

[31]  Harald Schuh,et al.  Troposphere gradients from the ECMWF in VLBI analysis , 2007 .