Empirical stochastic modeling of observation noise in global GNSS network processing

Global navigation satellite systems (GNSS) are integral to a wide array of scientific and commercial applications. Precise orbit determination of satellites in low Earth orbit relies on high-quality GNSS products. Examples of such satellites are those of the Copernicus Earth observation program of the European Union or the satellite gravimetry missions GRACE/GRACE-FO and GOCE. Numerous ground-based applications also require these products, for example: estimation of terrestrial water storage variations, earthquake monitoring, GNSS reflectometry, tropospheric and ionospheric research, surveying, or civil engineering. Furthermore, GNSS-derived station coordinates play an important role in the determination of the International Terrestrial Reference Frame. The analysis centres of the International GNSS Service (IGS) generate such products by processing observations from a global network of ground stations to one or more GNSS constellations.

So far, this kind of processing only incorporates elevation-dependent a priori modelling of observation variances and disregards temporal correlations. Meanwhile, numerous studies have shown the positive impact the incorporation of sophisticated stochastic modelling has on GNSS processing and resulting products. However, there have not been any large-scale investigations regarding the impact of stochastic modelling of observation noise on global GNSS processing.

In this contribution, we discuss a post-fit residuals approach for deriving temporal correlations in global multi-GNSS processing and their limitations. We used several years of observations and a selected IGS network of ground stations. Based on this data we analysed the post-fit residuals and the derived temporal correlations per station with respect to their seasonal effects, specific used receivers, antennas, and different transmitter signal types.