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Open AccessArticle

A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

1
State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics (IGG), Chinese Academy of Sciences, 340 Xudong Road, Wuhan 430077, China
2
Wegener Center for Climate and Global Change (WEGC) and Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics, University of Graz, Brandhofgasse 58010, Graz, Austria
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Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Hohe Warte, 381190 Vienna, Austria
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Danish Meteorological Institute (DMI), Lyngbyvej 100, DK-2100 Copenhagen, Denmark
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National Oceanic and Atmospheric Administration (NOAA), NESDIS/STAR/SMCD, Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740-3818, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2729; https://doi.org/10.3390/rs11232729
Received: 26 October 2019 / Revised: 14 November 2019 / Accepted: 15 November 2019 / Published: 20 November 2019
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. View Full-Text
Keywords: GNSS atmospheric sounding; radio occultation; moist air retrieval; uncertainty propagation; algorithm validation GNSS atmospheric sounding; radio occultation; moist air retrieval; uncertainty propagation; algorithm validation
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MDPI and ACS Style

Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Schwaerz, M.; Nielsen, J.K.; Ho, S.-P.; Yuan, Y.-B. A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals. Remote Sens. 2019, 11, 2729.

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