Integrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry‐air atmospheric profiles

Abstract

Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) observations, globally available as a continuous record since 2001, are highly accurate and long‐term stable data records. Essential climate variables for the thermodynamic state of the free atmosphere, such as temperature and tropospheric water vapor profiles (involving background information), can be derived from these records, which consequentially have the potential to serve as climate benchmark data. In order to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties need to be quantified and traced throughout the retrieval chain. The new Reference Occultation Processing System at the Wegener Center aims to deliver such an accurate retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented for uncertainty propagation from RO bending angle profiles to dry‐air variables (pressure and temperature), for estimated random and systematic uncertainties, and for coestimates of observation‐to‐background weighting ratio profiles. We estimated systematic uncertainty profiles with the same operators as used for the basic profiles retrieval. The random uncertainty propagation was integrated by a covariance propagation approach and validated using Monte‐Carlo ensemble methods. We present the results of the validation and demonstrate how the algorithm performs for individual simulated RO events and for ensembles of real RO events. We also compare the new results from the integrated uncertainty propagation to previous ones from empirical error analyses for RO‐retrieved atmospheric profiles. We find that the new uncertainty estimation chain shows robust performance and is in good agreement with previous comparable results.