GNSS radio occultation (RO) observations have the potential to provide climate data records of benchmark quality, due to the unique properties of the RO technique. RO records are highly accurate, long-term stable, globally available and provide Essential Climate Variables (ECVs) for the thermodynamic state of the free atmosphere. These ECVs, pressure, temperature and tropospheric water vapor profiles, can be derived from the raw RO observations through an atmospheric profiles retrieval chain. To realize the climate benchmark potential, these RO retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to the ECVs. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate chain with integrated uncertaintypropagation.This thesis contributed to the development and implementation of algorithms in the rOPS to propagate uncertainty, first, from RO excess phase profiles to atmospheric bending angle profiles, then further through refractivity to dry-air profiles, and finally, using also background profiles, to moist-air thermodynamic profiles (the ECVs). In the context of this thesis, propagation of estimated systematic and random uncertainties, vertical error correlations and resolution estimates, and also observation-to-background weighting ratio profiles was implemented into the rOPS retrieval chain.Results from the implemented covariance propagation were validated using Monte Carlo ensemble methods. The algorithm performance was demonstrated by test-day ensembles of simulated and real RO event data from the satellite missions CHAMP, COSMIC, and MetOp, which show that the new uncertainty estimation chain performs robustly and delivers reliable results. This thesis thus contributes to establishing the benchmark capability of the rOPS for the benefit of climate change monitoring and other applications.