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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5 | Copyright

Special issue: Observing Atmosphere and Climate with Occultation Techniques...

Atmos. Meas. Tech., 11, 2601-2631, 2018
https://doi.org/10.5194/amt-11-2601-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 May 2018

Research article | 04 May 2018

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

Jakob Schwarz1,2, Gottfried Kirchengast1,2, and Marc Schwaerz1,2 Jakob Schwarz et al.
  • 1Wegener Center for Climate and Global Change (WEGC), University of Graz, Graz, Austria
  • 2Institute for Geophysics, Astrophysics and Meteorology/Institute of Physics, University of Graz, Graz, Austria

Abstract. Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere – such as pressure, temperature, and tropospheric water vapor profiles (involving background information) – can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

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We process global navigation satellite system radio occultation (RO) observations in a new way with integrated uncertainty propagation; in this study we focus on retrieving atmospheric bending angles from RO excess phase profiles. We find that this new approach within our novel Reference Occultation Processing System (rOPS) exploits the strengths of RO such as its high accuracy and long-term stability in a reliable manner for global climate monitoring and other weather and climate uses.
We process global navigation satellite system radio occultation (RO) observations in a new way...
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