Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

This paper focuses on the investigation of the deterministic and stochastic parts of the doppler orbitography and radiopositioning integrated by satellite (doris) weekly time series aligned to the newest release of itrf2014. a set of 90 stations was divided into three groups depending on when the data were collected at an individual station. to reliably describe the doris time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. we proved that the values of the parameters delivered for doris data are strictly correlated with the time span of the observations. the quality of the most recent data has significantly improved. not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. among several tested models, the power-law process be chosen as the preferred one for most of the doris data. moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. for the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the north, east and up components. in the best cases, a velocity uncertainty of doris sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.