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

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.

Response to reviews of paper “Wave-optics uncertainty propagation ..."

“…From experience with estimated biases of ECMWF analysis fields in other studies (e.g., Li et al, 2013Li et al, , 2015Scherllin-Pirscher et al, 2017;Li et al, 2017), we formulate the model for the ECMWF-reference field-induced systematic uncertainty profile u s refEC (z) as a fractional model (f us refEC (z)) with a linear increase downward over the lower troposphere towards the surface,…”

“…The bending angle (BA) and atmospheric profiles retrieval chain for Global Navigation Satellite System (GNSS) radio occultation (RO) data includes many steps involving linear and (moderately) nonlinear transformations, starting from excess phase and amplitude measurements (Gorbunov et al, 2006). Error or uncertainty propagation through the geometric optical part of the retrieval chain has been investigated in a series of theoretical and empirical studies (Kursinski et al, 1997;Syndergaard, 1999;Palmer et al, 2000;Rieder and Kirchengast, 2001;Kuo et al, 2004;Steiner and Kirchengast, 2005;Schreiner et al, 2007;Scherllin-Pirscher et al, 2011b;Scherllin-Pirscher et al, 2011a;Scherllin-Pirscher et al, 2017;Innerkofler et al, 2016;Schwarz et al, 2016;Schwarz et al, 2017a;Schwarz et al, 2017b;Li et al, 2016;Li et al, 2017).…”

Response to Referee 1

“…Finally, related work and manuscript preparation on a new moist-air retrieval algorithm (L2b) and corresponding L2b uncertainty propagation are ongoing (Li et al, 2018;Kirchengast et al, 2017a).…”

Tropical cyclones vertical structure from GNSS radio occultation: an archive covering the period 2001–2018

“…Huang et al (2005), for the first time, assimilated RO refractivity profiles to forecast the typhoons Nari in 2001 and Nakri in 2002, which developed in the northwestern Pacific Ocean. This study was followed by many others, focusing on cyclone events: Hurricane Ernesto in 2006 (Liu et al, 2012), Typhoon Usagi in 2007 (Kunii et al, 2012), typhoons Jangmi in 2008, Hagupit in 2008and Sinlaku in 2008(Hsiao et al, 2012, Super-cyclone Gonu in 2007 (Anisetty et al, 2014), and Tropical Cyclone Phailin in 2013 (Hima Bindu et al, 2016). Huang et al (2010) were the first who analysed a complete TC season followed by Chen et al (2015).…”

“…A similar agreement between RO and dropsonde observations was presented by Anthes (2011) for the Typhoon Jangmi in 2008. Anthes et al (2008) demonstrated the importance of RO temperature and WV assimilation to forecast Hurricane Ernesto in 2006 (Chen et al, 2014;Liu et al, 2012). Winterbottom and Xiao (2010) showed that the quality and the horizontal resolution of RO was high enough to study TCs, even before the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) six-satellite mission was launched.…”