VLBI Scale Systematics

Transcription

1 VLBI Scale Systematics Dan MacMillan NVI Inc. at NASA/GSFC Hana Krasna Technical University Vienna Unified Analysis Workshop Paris July 10, 2017

2 Overview Systematic Errors Contributing to VLBI Scale Scale series and estimation uncertainties Hydrology loading Antenna gravitational deformation Atmospheric delay modeling Mean pole model Relativistic delay model (not discussed here, see J. Gipson presentation)

3 VLBI Scale Series Session-wise scale factor estimates ( ) from NEOS-A, IVS-R1, IVS-R4 and all CONT 24-hr sessions VieVS Software 3.0 IERS Conventions 2010 (Petit & Luzum, 2010) Non-tidal atmosphere loading applied a priori (TU Wien, Wijaya et al., 2013) TRF fixed to ITRF2014 Scale factor estimated session-wise EOP + CRF estimated session-wise Scale factor w.r.t. ITRF2014 weighted mean = 0.70 ppb (= half of the difference between VLBI and SLR scale in ITRF2014)

4 VLBI Scale Series Uncertainties

5 VLBI Scale Series Uncertainties

6 Hydrology Loading Hydrology Loading

7 Hydrology Loading Annual Offset ppb Rate ppb/yr Cosine ppb Sine ppb No Loading ±0.020 Hydro Loading ±0.02 VLBI scale amp (Altamimi et al.(2016) ± ± Estimates are relative to ITRF2014. Data period Most of the observed annual is explained by hydrology loading Scale amplitudes ~ agree with Altamimi et. al

8 VLBI Scale Series and Hydrology Loading Annual with amplitude (~ ppb) CONTs occurred (Aug-Oct) [ except Cont14 (May)] at series ± peaks WRMS: 0.8 ppb -> 0.7 ppb CONT08 and CONT11 biggest reduction of bias Applying a monthly hydrology loading series based on the GLDAS model removes much of annual signal

9 Gravitational Deformation Clark and Thomsen (1988) model for signal path delay depends on variations of 1) focal length 2) vertex position 3) receiver position Coefficients depend on dimensions and structure of antenna The functions F, V and R have to be measured for each antenna

10 Gravitational Deformation Measurements of Noto and Medicina (Sarti and Abondanza, 2009,2010) laser scanner (F)+ terrestrial survey (R)+ finite element model (V) Model of deformation from Clark and Thomsen (1988) XY mount antenna at Fairbanks (26 meter diameter) ΔL(e) = 2.4 (1 sin(e) ) mm

11 Gravitational Deformation Offset ppb Rate ppb/yr Medicina Model 0.80 ± ± Noto Model Fairbanks Model Scaled each model delay (~ Diam 2 ) to the antenna diameter of each antenna in the solution. Estimates are relative to ITRF2014. Data period

12 Troposphere Scale Bias Error Scale bias between CONT11 solutions [Eriksson, MacMillan, and Gipson, 2014] - Computed from the mean length differences for each baseline for all baselines in each solution - Linear rate vs. length = scale difference o Raytrace - VMF1 = ± ppb o Raytrace - NMF = ± ppb Scale offset between TRF solutions ( ) using raytracing versus VMF1 [Hofmeister & Böhm, 2016] Raytrace VMF1 < 0.1 ppb

13 Mean Pole Model Pole Tide Site Position Contributions ~ (x,y) usno_finals (x,y) mean pole Currently we (GSFC) use the IERS2010 mean pole model Ran tests done to compare IERS2010 versus the IERS mean pole table (2016.0) VLBI data Global scale solution: IERS Table (2016.0) Conv ppb ppb/yr Scale estimated for each 24 hour session

14 Mean Pole Model Comparison of mean pole models and the USNO Finals polar motion series X-pole difference => systematic in scale series

15 Error Source Gravitational Deformation Mean pole IERS Table-2010 Scale Error Budget Annual Cos Annual Sin Rate ppb/yr to Bias ppb 0.80 to Hydrology Troposphere < 0.1 VLBI difference in ITRF ± ± ± ± ) Only 0.17 ppb of 0.69 ppb is resolved 2) Gravitational deformation increases the scale difference relative to SLR (based on measurements of only 3 telescopes)