Luca Ostini

TL;DR: In this paper, the authors focus on the generation of the combined global products at CODE, where they put emphasis not only on accuracy, but also on completeness, and study the impact of GLONASS on the CODE products, and the benefit of using them.

TL;DR: In this article, the authors designed and managed the Detection of Offsets in GPS Experiment, which simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model.

TL;DR: The Bernese GNSS Software introductory course in Bern provides ready-to-use BPE examples for precise point positioning, baseline processing, network processing, clock estimation, LEO orbit determination and SLR validation of GNSS or LEO orbits.

TL;DR: Examples of analysed time series of station coordinates and differential code biases are presented.

Abstract: Nowadays long continuous time series of products obtained by GNSS (Global Navigation Satellite Systems) measurements are available. Due to reprocessing efforts these time series have achieved a very high consistency level. In this context the analysis, reassessment, and the interpretation of these time series become more and more important. In particular station coordinate time series from GNSS observations may be affected by discontinuities, e.g., because of equipment changes, earthquakes, other geophysical processes, data problems, and environmental effects. In the Bernese GPS Software the program ADDNEQ2 is responsible to combine individual (e.g., daily or weekly) solutions over a long time interval of even ten years and more. Regarding the fact that today usually between 100 and 200 (or even more) stations are included in a continuous processing schema over years, an automated analysis of the time series is very appreciated. FODITS (f ̄ ind o ̄ utliers and d ̄ iscontinuities i ̄ n t ̄ ime s ̄ eries) has been developed as a new tool of the Bernese GPS Software. A functional model including outliers, discontinuities, one or more linear velocities per station, and a set of periodic functions is adapted by the program to the time series of the data according to the significance of the components. The used algorithm is bases on the ATI (adaptation, test, and identification) method–developed for preprocessing of GNSS data–and is optimized for the analysis of long time series of coordinates. Nevertheless, it is also possible to analyse other time series using FODITS. In this paper we present examples of analysed time series of station coordinates and differential code biases.