Reprocessing Multiple GNSS Constellations and a Global Station Network from 1994 to 2020 with the Raw Observation Approach

An accurate and stable global geodetic reference frame, such as the International Terrestrial Reference Frame (ITRF), is fundamental for quantifying geophysical changes in the Earth system. Global navigation satellite systems (GNSS) are one of the four space-geodetic techniques contributing to the construction of the ITRF. In support of the upcoming ITRF2020 release, the International GNSS Service (IGS) conducted its third reprocessing campaign (repro3), covering the years 1994 to 2020. Graz University of Technology (TUG) participated for the first time as an analysis center in such a reprocessing.

This thesis thoroughly describes the methodologies behind TUG's repro3 contribution. The reprocessing included the three satellite constellations GPS, GLONASS, and Galileo as well as a total of 1182 ground stations. In the latter third of the time series, more than 800 stations were processed per day, resulting in equation systems with hundreds of millions of observations and millions of parameters. The thesis delineates how the raw observation approach was implemented to facilitate a GNSS processing of this scale. This includes methods and strategies developed to handle the extraordinary computational challenges of this reprocessing. All applied parametrizations, models, and corrections are elaborated in detail. For example, generalized methods for the parametrization of code and phase biases in a multi-signal environment are presented.

The evaluation of the resulting GNSS products showed that they are of very high quality. TUG’s repro3 contribution gained the highest weight on average in the IGS reference frame combination. The consistency of the obtained satellite orbits is also very high, which was confirmed by preliminary orbit combination results. This means that the methods delineated in this thesis lead to GNSS products that are very competitive with those of other IGS analysis centers. Therefore, the targets of reaching this quality level and providing a valuable contribution to ITRF2020 were successfully achieved.