Status and Prospects for Combined GPS LOD and VLBI UT1 Measurements

Abstract

ABSTRACT.A Kalman filter was developed to combine VLBI estimates of UT1-TAI with biased length of day (LOD) estimates from GPS. The VLBI results are the analyses of the NASA Goddard Space Flight Center group from 24-hr multi-station observing sessions several times per week and the nearly daily 1-hr single-baseline sessions. Daily GPS LOD estimates from the International GNSS Service (IGS) are combined with the VLBI UT1-TAI by modeling the natural excitation of LOD as the integral of a white noise process (i.e., as a random walk) and the UT1 variations as the integration of LOD, similar to the method described by Morabito et al. (1988). To account for GPS technique errors, which express themselves mostly as temporally correlated biases in the LOD measurements, a Gauss-Markov model has been added to assimilate the IGS data, together with a fortnightly sinusoidal term to capture errors in the IGS treatments of tidal effects. Evaluated against independent atmospheric and oceanic axial angular momentum (AAM + OAM) excitations and compared to other UT1/LOD combinations, ours performs best overall in terms of lowest RMS residual and highest correlation with (AAM + OAM) over sliding intervals down to 3 d. The IERS 05C04 and Bulletin A combinations show strong high-frequency smoothing and other problems. Until modified, the JPL SPACE series suffered in the high frequencies from not including any GPS-based LODs. We find, surprisingly, that further improvements are possible in the Kalman filter combination by selective rejection of some VLBI data. The best combined results are obtained by excluding all the 1-hr single-baseline UT1 data as well as those 24-hr UT1 measurements with formal errors greater than 5 μs (about 18% of the multi-baseline sessions). A rescaling of the VLBI formal errors, rather than rejection, was not an effective strategy. These results suggest that the UT1 errors of the 1-hr and weaker 24-hr VLBI sessions are non-Gaussian and more heterogeneous than expected, possibly due to the diversity of observing geometries used, other neglected 58 systematic effects, or to the much shorter observational averaging interval of the single-baseline sessions. UT1 prediction services could benefit from better handling of VLBI inputs together with proper assimilation of IGS LOD products, including using the Ultra-rapid series that is updated four times daily with 15 hr delay.

Determination of precise Galileo orbits using combined GNSS and SLR observations

“…We also analyze, how the SLR observations influence the satellite position, as well as the impact of SLR data on the empirical orbit parameters. Finally, we analyze the systematic drift of the accumulated Length-of-Day (LoD) parameter, also known as LoD bias (Senior et al 2010), as it is correlated with the drift of the satellite ascending node and may serve as an additional orbit quality indicator. We also investigate RBs as a prerequisite for a reliable SLR solution and analyze the quality of the 2-day orbit predictions.…”

Earth rotation monitoring, UT1 determination and prediction

“…However, on time scales up to about 20 days, these errors are limited so that the high-frequency signal contained in the GPS UT determination can be used for densification of UT1 derived by VLBI [36]. Different approaches can be used.…”

Combination strategy for consistent final, rapid and predicted Earth rotation parameters

“…For GNSS, deficiencies in the modelling of orbit perturbations, for example, the solar radiation pressure, systematically impact the motion of the orbital nodes. This leads to biased GNSS LOD estimates which can distort the combined UT1 and LOD estimates (Ray 1996;Senior et al 2010;Mikschi et al 2019). Depending on the constellation (number of GPS, GLONASS, and Galileo satellites) and the parameterisation (e.g.…”