Analysis of estimating PCO/PCV using raw observationa approach in global GNSS processing

A Global Geodetic Reference Frame (GGRF) is essential for relating measurements taken anywhere on Earth. It provides the basis for a wide range of scientific and industrial applications, and is essential in any field where precise location information is required, such as the monitoring of climate change, agriculture, and changes in groundwater levels. The International Terrestrial Reference Frame (ITRF) is established and maintained by an international community.

The Satellite Geodesy working group at the Institute of Geodesy (IFG) at Graz University of Technology (TUG) provides a wide range of products, which are processed and published for the international community. These include gravity field and mass transport solutions, precise orbit data (POD) for low Earth orbit (LEO) satellites and global navigation satellite systems (GNSS) station networks, among others. These products are utilised by various organisations, including the International Combination Service for Time-variable Gravity Fields (COST-G) of the International Association of Geodesy (IAG), the International GNSS Service (IGS), and the European Copernicus POD Service Quality Working Group (CPOD). A consistent and accurate GGRF forms the basis of all our products and is therefore essential to ensure the quality of our in-house computations.

The latest version, ITRF2020, is based on four space geodetic techniques: GNSS, VLBI, SLR and DORIS. GNSS is used in ITRF2020. In this combination of techniques, the GNSS scale was adjusted by changing the z-offsets of the GNSS satellites, which allowed for better agreement with SLR. However, this resulted in a discrepancy with the calibration values provided for the GNSS satellites.

In this work, we analyse PCO/PCV estimation using the raw observation approach in global GNSS processing, with the aim of achieving an even more consistent solution. We also analyse the relationship between geocentre motion estimation and scale estimation in more detail. We also analyse the relationship with other GNSS parameters and compare simulated data with real-world data over a period of several years. We also demonstrate the problems and pitfalls, as well as the current state of the results and limitations.