Abstract
Abstract. The standard approach to remove the effects of the ionosphere from neutral atmosphere GPS radio occultation measurements is to estimate a corrected bending angle from a combination of the L1 and L2 bending angles. This approach is known to result in systematic errors and an extension has been proposed to the standard ionospheric correction that is dependent on the squared L1 ∕ L2 bending angle difference and a scaling term (κ). The variation of κ with height, time, season, location and solar activity (i.e. the F10.7 flux) has been investigated by applying a 1-D bending angle operator to electron density profiles provided by a monthly median ionospheric climatology model. As expected, the residual bending angle is well correlated (negatively) with the vertical total electron content (TEC). κ is more strongly dependent on the solar zenith angle, indicating that the TEC-dependent component of the residual error is effectively modelled by the squared L1 ∕ L2 bending angle difference term in the correction. The residual error from the ionospheric correction is likely to be a major contributor to the overall error budget of neutral atmosphere retrievals between 40 and 80 km. Over this height range κ is approximately linear with height. A simple κ model has also been developed. It is independent of ionospheric measurements, but incorporates geophysical dependencies (i.e. solar zenith angle, solar flux, altitude). The global mean error (i.e. bias) and the standard deviation of the residual errors are reduced from -1.3×10-8 and 2.2×10-8 for the uncorrected case to -2.2×10-10 rad and 2.0×10-9 rad, respectively, for the corrections using the κ model. Although a fixed scalar κ also reduces bias for the global average, the selected value of κ (14 rad−1) is only appropriate for a small band of locations around the solar terminator. In the daytime, the scalar κ is consistently too high and this results in an overcorrection of the bending angles and a positive bending angle bias. Similarly, in the nighttime, the scalar κ is too low. However, in this case, the bending angles are already small and the impact of the choice of κ is less pronounced.
Highlights
It has been demonstrated that, by using variational data assimilation techniques, GPS radio occultation (GPS-RO) measurements can be assimilated into operational numerical weather prediction (NWP) systems to improve the accuracy of temperatures in the upper troposphere and lower–middle stratosphere (Healy and Thépaut, 2006; Poli et al, 2009; Rennie, 2010)
VK94 showed that the first-order correction leaves a systematic bending angle bias that increases as a function of the electron density squared, integrated over the vertical profile
It has been demonstrated that such an approach can lead to significant differences in the residual bending angle bias between day and night
Summary
It has been demonstrated that, by using variational data assimilation techniques, GPS radio occultation (GPS-RO) measurements can be assimilated into operational numerical weather prediction (NWP) systems to improve the accuracy of temperatures in the upper troposphere and lower–middle stratosphere (Healy and Thépaut, 2006; Poli et al, 2009; Rennie, 2010). Vorob’ev and Krasil’nikova (1994) (hereafter referred to as VK94) proposed a method of combining the GPS-RO bending angles measured at two frequencies (L1 and L2) to provide a first-order correction for the ionosphere. VK94 showed that the first-order correction leaves a systematic bending angle bias that increases as a function of the electron density squared, integrated over the vertical profile. The relationship between the bias and electron density suggests that the bending angle biases should vary diurnally and as a func-. Simple models of κ will be assessed in order to evaluate their potential to reduce the residual bending angle errors in the VK94 correction. Three models will be considered: Latitude Longitude Time Day of year Year Tangent height Range. A further independent set of 25 000 κ estimates was generated using the same random parameter ranges to act as a test data set
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
