Total Impact of Periodic Terms and Coloured Noise on Velocity Estimates

The uncertainties of velocity estimates for position time series of Global Navigation Satellite System
(GNSS) stations are mainly affected by a misfit of the deterministic model applied to this data.
Insufficiently modelled seasonal signals will propagate into the stochastic model and falsify the results of the noise analysis besides the velocity estimates and their uncertainties. In this presentation we derived the General Dilution of Precision (GDP) of velocity uncertainties. We define this dilution as the ratio between the uncertainties of velocities determined when different deterministic and stochastic models are applied. In this way we discuss, referring to previously published results, how insufficiently modelled seasonal signals influence station velocity uncertainties with white and coloured noise. Using simulated and real data from selected (115) IGS (International GNSS Service) stations we show that the noise character affects GNSS data more than seasonals for time series longer than 9 years.