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

2017 ◽  
Author(s):  
Jakob Schwarz ◽  
Gottfried Kirchengast ◽  
Marc Schwaerz
Keyword(s):  
Monte Carlo ◽  
Radio Occultation ◽  
Uncertainty Propagation ◽  
Processing System ◽  
Background Information ◽  
Climate Variables ◽  
Free Atmosphere ◽  
Bending Angle ◽  
Covariance Propagation ◽  
Excess Phase

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 since 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 CHAMP and COSMIC. 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

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

2018 ◽  
Vol 11 (5) ◽  
pp. 2601-2631 ◽  
Author(s):  
Jakob Schwarz ◽  
Gottfried Kirchengast ◽  
Marc Schwaerz
Keyword(s):  
Monte Carlo ◽  
Radio Occultation ◽  
Uncertainty Propagation ◽  
Processing System ◽  
Background Information ◽  
Climate Variables ◽  
Free Atmosphere ◽  
Bending Angle ◽  
Covariance Propagation ◽  
Excess Phase

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.

scholarly journals Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

2017 ◽  
Author(s):  
Michael Gorbunov ◽  
Gottfried Kirchengast
Keyword(s):  
Regression Analysis ◽  
Systematic Uncertainty ◽  
Radio Occultation ◽  
Uncertainty Propagation ◽  
Lower Troposphere ◽  
Wave Optics ◽  
Bending Angle ◽  
Excess Phase ◽  
Excess Phases ◽  
Random Uncertainties

Abstract. A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle retrieval in the lower troposphere and introduce 1. an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in the lowest about two kilometers of the troposphere, and 2. the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms predictors and adaptive functions (powers and cross-products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform amplitude and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction, capable of reducing bending angle and corresponding refractivity biases by about a factor of five. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower-bounded by the uncertainty from (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging that this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.

scholarly journals Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

2018 ◽  
Vol 11 (1) ◽  
pp. 111-125 ◽  
Author(s):  
Michael E. Gorbunov ◽  
Gottfried Kirchengast
Keyword(s):  
Regression Analysis ◽  
Systematic Uncertainty ◽  
Radio Occultation ◽  
Uncertainty Propagation ◽  
Lower Troposphere ◽  
Wave Optics ◽  
Bending Angle ◽  
Excess Phase ◽  
Excess Phases ◽  
Random Uncertainties

Abstract. A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.

scholarly journals Evaluation of atmospheric profiles derived from single- and zero-difference excess phase processing of BeiDou radio occultation data from the FY-3C GNOS mission

2018 ◽  
Vol 11 (2) ◽  
pp. 819-833 ◽  
Author(s):  
Weihua Bai ◽  
Congliang Liu ◽  
Xiangguang Meng ◽  
Yueqiang Sun ◽  
Gottfried Kirchengast ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Radio Occultation ◽  
Global Climate ◽  
Weather Prediction ◽  
Neutral Atmosphere ◽  
Change Analysis ◽  
Bending Angle ◽  
New Instrument ◽  
Single Difference ◽  
Excess Phase

Abstract. The Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) is one of the new-generation payloads onboard the Chinese FengYun 3 (FY-3) series of operational meteorological satellites for sounding the Earth's neutral atmosphere and ionosphere. The GNOS was designed for acquiring setting and rising radio occultation (RO) data by using GNSS signals from both the Chinese BeiDou System (BDS) and the US Global Positioning System (GPS). An ultra-stable oscillator with 1 s stability (Allan deviation) at the level of 10−12 was installed on the FY-3C GNOS, and thus both zero-difference and single-difference excess phase processing methods should be feasible for FY-3C GNOS observations. In this study we focus on evaluating zero-difference processing of BDS RO data vs. single-difference processing, in order to investigate the zero-difference feasibility for this new instrument, which after its launch in September 2013 started to use BDS signals from five geostationary orbit (GEO) satellites, five inclined geosynchronous orbit (IGSO) satellites and four medium Earth orbit (MEO) satellites. We used a 3-month set of GNOS BDS RO data (October to December 2013) for the evaluation and compared atmospheric bending angle and refractivity profiles, derived from single- and zero-difference excess phase data, against co-located profiles from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. We also compared against co-located refractivity profiles from radiosondes. The statistical evaluation against these reference data shows that the results from single- and zero-difference processing are reasonably consistent in both bias and standard deviation, clearly demonstrating the feasibility of zero differencing for GNOS BDS RO observations. The average bias (and standard deviation) of the bending angle and refractivity profiles were found to be about 0.05 to 0.2 % (and 0.7 to 1.6 %) over the upper troposphere and lower stratosphere. Zero differencing was found to perform slightly better, as may be expected from its lower vulnerability to noise. The validation results indicate that GNOS can provide, on top of GPS RO profiles, accurate and precise BDS RO profiles both from single- and zero-difference processing. The GNOS observations by the series of FY-3 satellites are thus expected to provide important contributions to numerical weather prediction and global climate change analysis.

scholarly journals Comparison study of COSMIC RO dry-air climatologies based on average profile inversion

2018 ◽  
Vol 11 (8) ◽  
pp. 4867-4882 ◽  
Author(s):  
Julia Danzer ◽  
Marc Schwärz ◽  
Veronika Proschek ◽  
Ulrich Foelsche ◽  
Hans Gleisner
Keyword(s):  
High Altitude ◽  
Radio Occultation ◽  
Background Information ◽  
Bending Angle ◽  
Background Data ◽  
Average Profile ◽  
The Mean ◽  
Profile Inversion ◽  
The Impact ◽  
Bending Angles

Abstract. Global Navigation Satellite System (GNSS) radio occultation (RO) data enable the retrieval of near-vertical profiles of atmospheric parameters like bending angle, refractivity, pressure, and temperature. The retrieval step from bending angle to refractivity, however, involves an Abel integral with an upper limit of infinity. RO data are practically limited to altitudes below about 80 km and the observed bending angle profiles show decreasing signal-to-noise ratio with increasing altitude. Some kind of high-altitude background data are therefore needed in order to perform this retrieval step (this approach is known as high-altitude initialization). Any bias in the background data will affect all RO data products beyond bending angle. A reduction of the influence of the background is therefore desirable – in particular for climate applications. Recently a new approach for the production of GNSS radio occultation climatologies has been proposed. The idea is to perform the averaging of individual profiles in bending angle space and then propagate the mean bending angle profiles through the Abel transform. Climatological products of refractivity, density, pressure, and temperature are directly retrieved from the mean bending angles. The averaging of a large number of profiles suppresses noise in the data, enabling observed bending angle data to be used up to 80 km without the need for a priori information. Some background information for the Abel integral is still necessary above 80 km. This work is a follow-up study, having the focus on the comparison of the average profile inversion climatologies (API) from the two processing centers WEGC and DMI, which study monthly COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) data from January to March 2011. The impact of different backgrounds above 80 km is tested, and different implementations of the Abel integral are investigated. Results are compared for the climatological products with ECMWF analyses, MIPAS, and SABER data. It is shown that different implementations of the Abel integral have little impact on the API climatologies. On the other hand, different extrapolations of the bending angle profile above 80 km play a key role in the resulting monthly mean refractivities above 35 km in altitude. Below that respective altitude the API climatologies show a good agreement between the two processing centers WEGC and DMI. Due to the downward propagation within the retrieval, effects of the high-altitude initialization lead to differences in dry-temperature climatologies down to 20 km in altitude. When applying an exponential extrapolation to the bending angles above 80 km at both centers, the dry-temperature climatologies agree among WEGC, DMI, ECMWF analysis, and MIPAS up to 35 km in altitude within ±0.5 K and up to 40 km in altitude within ±1 K. We conclude that the API retrieval is a valid approach up to the lower stratosphere. It is a computationally efficient alternative method for producing dry atmospheric RO climatologies.

scholarly journals Evaluation of atmospheric profiles derived from single- and zero-difference excess phase processing of BeiDou System radio occultation data of the FY-3C GNOS mission

2017 ◽  
Author(s):  
Weihua Bai ◽  
Congliang Liu ◽  
Xiangguang Meng ◽  
Yueqiang Sun ◽  
Gottfried Kirchengast ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Neutral Atmosphere ◽  
Change Analysis ◽  
Bending Angle ◽  
New Instrument ◽  
Beidou System ◽  
Single Difference ◽  
Excess Phase

Abstract. The Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) is one of the new generation payloads onboard the Chinese FengYun 3 (FY-3) series of operational meteorological satellites for sounding the Earth’s neutral atmosphere and ionosphere. GNOS was designed for acquiring setting and rising radio occultation (RO) data by using GNSS signals from both the Chinese BeiDou System (BDS) and the U.S. Global Positioning System (GPS). An ultra-stable oscillator with 1-sec stability (Allan deviation) at the level of 10−12 was installed on FY-3C GNOS, thus both zero-difference and single-difference excess phase processing methods should be feasible for FY-3C GNOS observations. In this study we focus on evaluating zero-difference processing of BDS RO data vs. single-difference processing, in order to investigate the zero-difference feasibility for this new instrument, which after its launch in September 2013 started to use BDS signals from 5 geostationary orbit (GEO) satellites, 5 inclined geosynchronous orbit (IGSO) satellites and 4 medium earth orbit (MEO) satellites. We used a 3-month set of GNOS BDS RO data (October to December 2013) for the evaluation and compared atmospheric bending angle and refractivity profiles, derived from single- and zero-difference excess phase data, against co-located profiles from ECMWF (European Centre for Medium-Range Weather Forecasts) analyses. We also compared against co-located refractivity profiles from radiosondes. The statistical evaluation against these reference data shows that the results from single- and zero-difference processing are consistent in both bias and standard deviation, clearly demonstrating the feasibility of zero-differencing for GNOS BDS RO observations. The average bias (and standard deviation) of the bending angle and refractivity profiles were found to be as small as about 0.05 %–0.2 % (and 0.7 %–1.6 %) over the upper troposphere and lower stratosphere, including for the GEO, IGSO, and MEO subsets. Zero-differencing was found to perform slightly better, as may be expected from its lower vulnerability to noise. The validation results establish that GNOS can provide, on top of GPS RO profiles, accurate and precise BDS RO profiles both from single- and zero-difference processing. The GNOS observations by the series of FY-3 satellites will thus provide important contributions to numerical weather prediction and global climate change analysis.

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