Remote Sensing, Vol. 11, Pages 2729: A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals (Remote Sensing)

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a key technique for obtaining thermodynamic profiles of temperature, humidity, pressure, and density in the Earth’s troposphere. However, due to refraction effects of both the dry air and water vapor at low altitudes, retrieval of accurate profiles is challenging. Here we introduce a new moist air retrieval algorithm aiming to improve the quality of RO-retrieved profiles in moist air and including uncertainty estimation in a clear sequence of steps. The algorithm first uses RO dry temperature and pressure and background temperature/humidity and their uncertainties to retrieve humidity/temperature and their uncertainties. These temperature and humidity profiles are then combined with their corresponding background profiles by optimal estimation employing inverse-variance weighting. Finally, based on the optimally estimated temperature and humidity profiles, pressure and density profiles are computed using hydrostatic and equation-of-state formulas. The input observation and background uncertainties are dynamically estimated, accounting for spatial and temporal variations. We show results from applying the algorithm on test datasets, deriving insights from both individual profiles and statistical ensembles, and from comparison to independent 1D-Variational (1DVar) algorithm-derived moist air retrieval results from Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers. We find that the new scheme is comparable in its retrieval performance and features advantages in the integrated uncertainty estimation that includes both estimated random and systematic uncertainties and background bias correction. The new algorithm can therefore be used to obtain high-quality tropospheric climate data records including uncertainty estimation.