Abstract
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.
Highlights
The Global Navigation Satellite System (GNSS) Radio Occultation (RO) technique is an atmospheric sounding technique providing global-coverage, high-accuracy, and high-precision vertical profiles of Earth’s atmosphere [1,2,3,4,5]
The accumulated bending angle can be calculated from precise orbit data and the excess phase measurements acquired by tracking the GNSS signals on a Low Earth Orbit (LEO) satellite
The quality of retrieved profiles from the OPSv5.6 and dynamic approaches was determined by the OPSv5.6 system specifications [56] and the quality of the profiles retrieved at COSMIC Data Analysis and Archiving Center (CDAAC) and Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) by the system they used at their centers [20,26,57]
Summary
The Global Navigation Satellite System (GNSS) Radio Occultation (RO) technique is an atmospheric sounding technique providing global-coverage, high-accuracy, and high-precision vertical profiles of Earth’s atmosphere [1,2,3,4,5]. In each 10◦ × 20◦ grid cell and on the 200-level standard vertical grid, several types of basic variables are pre-calculated and saved for both background temperature and specific humidity These basic variables include (the same notations of some basic variables are used for both temperature and humidity due to the same calculation method): (1) the mean analysis profile of temperature Ta and specific humidity qa; (2) the standard deviations of the ensemble of analysis values against its mean sa; (3) bias of the mean analysis profile ba; (4) the mean forecast profile of temperature Tf and specific humidity qf; (5) the standard deviations of the forecast-minus-analysis difference profiles sf-a; (6) the number of values in the analysis and the forecast ensemble Na,f. The details of how to extract the ensemble of profiles on the grid and how to calculate these profiles were described by Li et al [52,53]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
