Characterizing Ionospheric Effect on GNSS Radio Occultation Atmospheric Bending Angle

2020 ◽  
Vol 125 (6) ◽  
Author(s):  
Mingzhe Li ◽  
Xinan Yue ◽  
Weixing Wan ◽  
William S. Schreiner
2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.


2005 ◽  
Vol 5 (6) ◽  
pp. 1665-1677 ◽  
Author(s):  
A. von Engeln ◽  
G. Nedoluha

Abstract. The Optimal Estimation Method is used to retrieve temperature and water vapor profiles from simulated radio occultation measurements in order to assess how different retrieval schemes may affect the assimilation of this data. High resolution ECMWF global fields are used by a state-of-the-art radio occultation simulator to provide quasi-realistic bending angle and refractivity profiles. Both types of profiles are used in the retrieval process to assess their advantages and disadvantages. The impact of the GPS measurement is expressed as an improvement over the a priori knowledge (taken from a 24h old analysis). Large improvements are found for temperature in the upper troposphere and lower stratosphere. Only very small improvements are found in the lower troposphere, where water vapor is present. Water vapor improvements are only significant between about 1 km to 7 km. No pronounced difference is found between retrievals based upon bending angles or refractivity. Results are compared to idealized retrievals, where the atmosphere is spherically symmetric and instrument noise is not included. Comparing idealized to quasi-realistic calculations shows that the main impact of a ray tracing algorithm can be expected for low latitude water vapor, where the horizontal variability is high. We also address the effect of altitude correlations in the temperature and water vapor. Overall, we find that water vapor and temperature retrievals using bending angle profiles are more CPU intensive than refractivity profiles, but that they do not provide significantly better results.


2018 ◽  
Vol 11 (4) ◽  
pp. 2427-2440 ◽  
Author(s):  
Congliang Liu ◽  
Gottfried Kirchengast ◽  
Yueqiang Sun ◽  
Kefei Zhang ◽  
Robert Norman ◽  
...  

Abstract. The Global Navigation Satellite System (GNSS) radio occultation (RO) technique is widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source to RO at upper stratospheric altitudes, and a linear dual-frequency bending angle correction is commonly used to remove the first-order ionospheric effect. However, the higher-order residual ionospheric error (RIE) can still be significant, so it needs to be further mitigated for high-accuracy applications, especially from 35 km altitude upward, where the RIE is most relevant compared to the decreasing magnitude of the atmospheric bending angle. In a previous study we quantified RIEs using an ensemble of about 700 quasi-realistic end-to-end simulated RO events, finding typical RIEs at the 0.1 to 0.5 µrad noise level, but were left with 26 exceptional events with anomalous RIEs at the 1 to 10 µrad level that remained unexplained. In this study, we focused on investigating the causes of the high RIE of these exceptional events, employing detailed along-ray-path analyses of atmospheric and ionospheric refractivities, impact parameter changes, and bending angles and RIEs under asymmetric and symmetric ionospheric structures. We found that the main causes of the high RIEs are a combination of physics-based effects – where asymmetric ionospheric conditions play the primary role, more than the ionization level driven by solar activity – and technical ray tracer effects due to occasions of imperfect smoothness in ionospheric refractivity model derivatives. We also found that along-ray impact parameter variations of more than 10 to 20 m are possible due to ionospheric asymmetries and, depending on prevailing horizontal refractivity gradients, are positive or negative relative to the initial impact parameter at the GNSS transmitter. Furthermore, mesospheric RIEs are found generally higher than upper-stratospheric ones, likely due to being closer in tangent point heights to the ionospheric E layer peaking near 105 km, which increases RIE vulnerability. In the future we will further improve the along-ray modeling system to fully isolate technical from physics-based effects and to use it beyond this work for additional GNSS RO signal propagation studies.


2016 ◽  
Vol 31 (1) ◽  
pp. 129-150 ◽  
Author(s):  
Ching-Yuang Huang ◽  
Shu-Ya Chen ◽  
S. K. A. V. Prasad Rao Anisetty ◽  
Shu-Chih Yang ◽  
Ling-Feng Hsiao

Abstract The impact of global positioning system (GPS) radio occultation (RO) soundings on the prediction of severe mei-yu frontal rainfall near Taiwan in June 2012 was investigated in this study using a developed local bending angle (LBA) operator. Two operators for local refractivity (REF) and nonlocal refractivity [excess phase (EPH)] were also used for comparisons. The devised LBA simplifies the calculation of the Abel transform in inverting model local refractivity without a loss of accuracy. These operators have been implemented into the three-dimensional variational data assimilation system of the Weather Research and Forecasting (WRF) Model to assimilate GPS RO soundings available from the Formosa Satellite Mission 3/Constellation Observing Systems for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC). The RO data are found to be beneficial to the WRF forecast of local severe rainfall in Taiwan. Characteristics of assimilation performance and innovation for the three operators are discussed. Both of the local operators performing assimilation at observation levels appear to produce mostly larger positive moisture increments than do the current nonlocal operators performing assimilation on the mean height of each model vertical level. As the information of the initial increments has propagated farther south with the frontal flow, the simulation for LBA shows better prediction of rainfall peaks in Taiwan on the second day than both REF and EPH, with a maximum improvement of about 25%. The positive impact of the RO data results partially from several RO observations near Mongolia and north China. This study provides an intercomparison among the three RO operators, and shows the feasibility of regional assimilation with LBA.


2014 ◽  
Vol 7 (7) ◽  
pp. 7811-7835
Author(s):  
J. Danzer ◽  
H. Gleisner ◽  
S. B. Healy

Abstract. GNSS Radio Occultation (RO) refractivity climatologies for the stratosphere can be obtained from the Abel inversion of monthly average bending-angle profiles. The averaging of large numbers of profiles suppresses random noise and this, in combination with simple exponential extrapolation above an altitude of 80 km, circumvents the need for a "statistical optimization" step in the processing. Using data from the US-Taiwanese COSMIC mission, which provides ~ 1500–2000 occultations per day, it has been shown that this Average-Profile Inversion (API) technique provides a robust method for generating stratospheric refractivity climatologies. Prior to the launch of COSMIC in mid-2006, the data records rely on data from the CHAMP mission. In order to exploit the full range of available RO data, the usage of CHAMP data is also required. CHAMP only provided ~ 200 profiles per day, and the measurements were noisier than COSMIC. As a consequence, the main research question in this study was to see if the average bending angle approach is also applicable to CHAMP data. Different methods for suppression of random noise – statistical and through data quality pre-screening – were tested. The API retrievals were compared with the more conventional approach of averaging individual refractivity profiles, produced with the implementation of statistical optimization used in the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) operational processing. In this study it is demonstrated that the API retrieval technique works well for CHAMP data, enabling the generation of long-term stratospheric RO climate data records from August 2001 and onward. The resulting CHAMP refractivity climatologies are found to be practically identical to the standard retrieval at the DMI below altitudes of 35 km. Between 35 km to 50 km the differences between the two retrieval methods started to increase, showing largest differences at high latitudes and high altitudes. Furthermore, in the winter hemisphere high latitude region, the biases relative to ECMWF were generally smaller for the new approach than for the standard retrieval.


2018 ◽  
Vol 35 (10) ◽  
pp. 2117-2131 ◽  
Author(s):  
Hui Liu ◽  
Ying-Hwa Kuo ◽  
Sergey Sokolovskiy ◽  
Xiaolei Zou ◽  
Zhen Zeng ◽  
...  

AbstractThe fluctuation of radio occultation (RO) signals in the presence of refractivity irregularities in the moist lower troposphere results in uncertainties of retrieved bending angle and refractivity profiles. In this study the local spectral width (LSW) of RO signals, transformed to impact parameter representation, is used for the characterization of the uncertainty (random error) of retrieved bending angle and refractivity profiles. A large LSW has some correlation with the large mean difference (bias) of retrieved refractivity and bending angle from radiosondes and European Centre for Medium-Range Weather Forecasts analyses based on data from 2008 to 2014. An LSW-based quality control (QC) procedure is developed to eliminate low-quality (large random errors and biases) profiles from data assimilation. The LSW-based QC procedure is tested and evaluated in the assimilation of Constellation Observing System for Meteorology, Ionosphere and Climate RO data using the NCAR Data Assimilation Research Testbed and the Weather Research and Forecasting Model. Preliminary results, based on a 2-week data assimilation cycle, show that the LSW-based QC procedure improves water vapor analyses in the moist lower troposphere.


2017 ◽  
Author(s):  
Michael Gorbunov ◽  
Gottfried Kirchengast

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.


2017 ◽  
Author(s):  
Feiqin Xie ◽  
Loknath Adhikari ◽  
Jennifer S. Haase ◽  
Brian Murphy ◽  
Kuo-Nung Wang ◽  
...  

Abstract. Airborne radio occultation (ARO) measurements collected during a ferry flight at the end of the PRE-Depression Investigation of Cloud-systems in the Tropics (PREDICT) field campaign from the Virgin Islands to Colorado are analyzed. This long flight at ~ 13 km altitude provided intercomparisons of bending angle retrieval techniques over a range of environments that may have different levels of atmospheric multipath propagation interference. Two especially well-adapted radio-holographic bending angle retrieval methods, full-spectrum-inversion (FSI), and phase-matching (PM), were compared with the standard geometric-optics (GO) retrieval method. Comparison of the ARO retrievals with the near-coincident ECMWF reanalysis-interim (ERA-I) profiles shows only a small root-mean-square (RMS) refractivity difference of ~ 0.3 % in the drier upper troposphere from ~ 5 km to 13 km over both land and ocean. Both the FSI and PM methods improve the ARO retrievals in the moist lower troposphere and reduce the negative bias found in the GO retrieval due to the multipath problem. In the lowest layer of the troposphere, the ARO refractivity using FSI shows a negative bias of about –2 %. The increase of the refractivity bias occurs below 5 km over the ocean and below 3.5 km over land, corresponding to the approximate altitude of large vertical moisture gradients above the ocean and land surface, respectively. In comparisons with radiosondes, the FSI ARO soundings capture well the height of layers with sharp refractivity gradients but display a negative refractivity bias inside the boundary layer. Three spaceborne radio occultation profiles within 300 km of the flight track shows a slightly larger RMS refractivity difference of ~ 2 %. Analysis of the 12 ARO events that were simultaneously recorded from both the top and side-looking antennas, indicates that high precision of the ARO measurements can be achieved corresponding to an RMS difference better than 0.2 % in refractivity (or ~ 0.4 K). The surprisingly good quality of recordings from a very simple antenna on top of an aircraft increases the feasibility of developing an operational tropospheric sounding system.


2017 ◽  
Author(s):  
Jakob Schwarz ◽  
Gottfried Kirchengast ◽  
Marc Schwaerz

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


2019 ◽  
Author(s):  
Andrea K. Steiner ◽  
Florian Ladstädter ◽  
Chi O. Ao ◽  
Hans Gleisner ◽  
Shu-Peng Ho ◽  
...  

Abstract. Atmospheric climate monitoring requires observations of high-quality conforming to the criteria of the Global Climate Observing System (GCOS). Radio occultation (RO) data based on Global Positioning System (GPS) signals are available since 2001 from several satellite missions with global coverage, high accuracy, and high vertical resolution in the troposphere and lower stratosphere. We assess the consistency and long-term stability of multi-satellite RO observations for use as climate data records. As a measure of long-term stability, we quantify the structural uncertainty of RO data products arising from different processing schemes. We analyze atmospheric variables from bending angle to temperature for four RO missions, CHAMP, Formosat-3/COSMIC, GRACE, and Metop, provided by five data centers. The comparisons are based on profile-to-profile differences, aggregated to monthly means. Structural uncertainty in trends is found lowest from 8 km to 25 km altitude globally for all inspected RO variables and missions. For temperature, it is < 0.05 K per decade in the global mean and < 0.1 K per decade at all latitudes. Above 25 km, the uncertainty increases for CHAMP while data from the other missions are based on advanced receivers and are usable to higher altitudes for climate trend studies: dry temperature to 35 km, refractivity to 40 km, and bending angle to 50 km. Larger differences in RO data at high altitudes and latitudes are mainly due to different implementation choices in the retrievals. The intercomparison helped to further enhance the maturity of the RO record and confirms the climate quality of multi-satellite RO observations towards establishing a GCOS climate data record.


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