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Complex demodulation in monitoring Earth rotation by VLBI: testing the algorithm by analysis of long periodic EOP components

Wielgosz A., Brzeziński A., Böhm S.

Artificial Satellites : Journal of Planetary Geodesy

2016

Vol. 51, No. 4

135--147

The complex demodulation (CD) algorithm is an efficient tool for extracting the diurnal and subdiurnal components of Earth rotation from the routine VLBI observations (Brzeziński, 2012). This algorithm was implemented by Böhm et al (2012b) into a dedicated version of the VLBI analysis software VieVs. The authors processed around 3700 geodetic 24-hour observing sessions in 1984.0-2010.5 and estimated simultaneously the time series of the long period components as well as diurnal, semidiurnal, terdiurnal and quarterdiurnal components of polar motion (PM) and universal time UT1. This paper describes the tests of the CD algorithm by checking consistency of the low frequency components of PM and UT1 estimated by VieVS CD and those from the IERS and IVS combined solutions. Moreover, the retrograde diurnal component of PM demodulated from VLBI observations has been compared to the celestial pole offsets series included in the IERS and IVS solutions. We found for all three components a good agreement of the results based on the CD approach and those based on the standard parameterization recommended by the IERS Conventions (IERS, 2010) and applied by the IERS and IVS. We conclude that an application of the CD parameterization in VLBI data analysis does not change those components of EOP which are included in the standard adjustment, while enabling simultaneous estimation of the high frequency components from the routine VLBI observations. Moreover, we deem that the CD algorithm can also be implemented in analysis of other space geodetic observations, like GNSS or SLR, enabling retrieval of subdiurnal signals in EOP from the past data.

Extreme learning machine for the predictions of length of day

Lei Y., Zhao D., Cai H.

Artificial Satellites : Journal of Planetary Geodesy

2015

Vol. 50, No. 1

19--33

This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.