Characterisation of residual ionospheric errors in bending angles using GNSS RO end-to-end simulations

Liu, C.L.; Kirchengast, Gottfried; Zhang, K.F.; Norman, Robert; Li, Y.; Zhang, S.C.; Carter, Brett; Fritzer, Johannes; Schwaerz, M.; Choy, Suelynn; Wu, Suqin; Tan, Zefu

doi:10.1016/j.asr.2013.05.021

Cited by 38 publications

(86 citation statements)

References 26 publications

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“…Recently Liu et al (2013) performed an initial study on quantifying bending angle RIEs based on end-to-end simulations, complemented by Liu et al (2014) who looked at the effects in subsequently retrieved temperature profiles. This work found, based on a limited set of individual occultation events, that the RIEs can significantly affect bending angles and retrieved temperatures, in particular when ionospheric disturbances occur such as during periods of active space weather (bending angle RIEs can exceed 0.5 µrad and temperature errors 1 K in the upper stratosphere).…”

Section: Bending Angle Ries and Simulation Methodsmentioning

confidence: 99%

“…(1) are 10 −4 and 10 −7 , respectively. The second-order geomagnetic term can be generally neglected in GPS RO applications (Syndergaard, 2000;Hoque and Jakowski, 2010;Liu et al, 2013), although it is a main source of the RIE in space-to-ground GPS positioning applications (Hoque and Jakowski, 2007). Due to this, as well as for computational efficiency, this term was ignored in our global ensemble RO data simulation for this study.…”

Section: Ionospheric Correction and Bending Angle Riementioning

confidence: 99%

“…According to the GRAS receiving system's error budgets and characteristics, we set the error parameters similar to those adopted in Steiner and Kirchengast (2005) and Liu et al (2013). The receiver noise was modeled as thermal noise with a 150 K LEO antenna noise and a 10 Hz loop band width.…”

Section: Simulation Of Realistic Observationsmentioning

confidence: 99%

“…A correction for the ionospheric effects in RO is usually implemented using a dual-frequency linear combination of bending angles (Vorob'ev and Krasil'nikova, 1994;Syndergaard, 2000), and the remaining error is the so-called residual ionospheric error (RIE). The RIE is not negligible in the upper stratosphere and mesosphere (USMS) in climate applications (Syndergaard, 2000;Mannucci et al, 2011;Danzer et al, 2013;Liu et al, 2013). What is worse is that the RIE can propagate downwards into the UTLS atmospheric retrievals through the Abel integral and the hydrostatic integral (e.g., Kursinski et al, 1997;Steiner and Kirchengast, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…This is because from the stratosphere to the mesosphere, ionospheric error influences become more and more dominant (e.g., Mannucci et al, 2011;Liu et al, 2013). A correction for the ionospheric effects in RO is usually implemented using a dual-frequency linear combination of bending angles (Vorob'ev and Krasil'nikova, 1994;Syndergaard, 2000), and the remaining error is the so-called residual ionospheric error (RIE).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

et al. 2015

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Abstract. The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is currently widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes, and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionospheric error (RIE) can still be significant so that it needs to be further mitigated for high-accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important for enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day ensemble of RO events. Based on the ensemble simulations we assessed the variation of bending angle RIEs, both biases and standard deviations, with solar activity, latitudinal region and with or without the assumption of ionospheric spherical symmetry and co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low to high latitudes, have a clear negative tendency and a magnitude increasing with solar activity, which is in line with recent empirical studies based on real RO data although we find smaller bias magnitudes, deserving further study in the future. The maximum RIE biases are found at low latitudes during daytime, where they amount to within −0.03 to −0.05 µrad, the smallest at high latitudes (0 to −0.01 µrad; quiet space weather and winter conditions). Ionospheric spherical symmetry or asymmetries about the RO event location have only a minor influence on RIE biases. The RIE standard deviations are markedly increased both by ionospheric asymmetries and increasing solar activity and amount to about 0.3 to 0.7 µrad in the upper stratosphere and mesosphere. Taking also into account the realistic observation errors of a modern RO receiving system, amounting globally to about 0.4 µrad (unbiased; standard deviation), shows that the random RIEs are typically comparable to the total observing system error. The results help to inform future RIE mitigation schemes that will improve upon the use of the linear ionospheric correction of bending angles and also provide explicit uncertainty estimates.

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Section: Bending Angle Ries and Simulation Methodsmentioning

confidence: 99%

Section: Ionospheric Correction and Bending Angle Riementioning

confidence: 99%

Section: Simulation Of Realistic Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

et al. 2015

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A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

Kirchengast

Scherllin‐Pirscher

et al. 2013

JGR Atmospheres

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Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

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Integrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry‐air atmospheric profiles

Schwarz

Kirchengast

Schwaerz

2017

Earth and Space Science

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Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) observations, globally available as a continuous record since 2001, are highly accurate and long-term stable data records. Essential climate variables for the thermodynamic state of the free atmosphere, such as temperature and tropospheric water vapor profiles (involving background information), can be derived from these records, which consequentially have the potential to serve as climate benchmark data. In order to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties need to be quantified and traced throughout the retrieval chain. The new Reference Occultation Processing System at the Wegener Center aims to deliver such an accurate retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented for uncertainty propagation from RO bending angle profiles to dry-air variables (pressure and temperature), for estimated random and systematic uncertainties, and for coestimates of observation-to-background weighting ratio profiles. We estimated systematic uncertainty profiles with the same operators as used for the basic profiles retrieval. The random uncertainty propagation was integrated by a covariance propagation approach and validated using Monte-Carlo ensemble methods. We present the results of the validation and demonstrate how the algorithm performs for individual simulated RO events and for ensembles of real RO events. We also compare the new results from the integrated uncertainty propagation to previous ones from empirical error analyses for RO-retrieved atmospheric profiles. We find that the new uncertainty estimation chain shows robust performance and is in good agreement with previous comparable results.

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Characterisation of residual ionospheric errors in bending angles using GNSS RO end-to-end simulations

Cited by 38 publications

References 26 publications

Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

Integrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry‐air atmospheric profiles

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