Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles
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, temperatur...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-05-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/11/2601/2018/amt-11-2601-2018.pdf |
| Summary: | 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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| ISSN: | 1867-1381 1867-8548 |