Systematic residual ionospheric errors in radio occultation data and a potential way to minimize them

Abstract

Radio occultation (RO) sensing is used to probe the earth's atmosphere in order to obtain information about its physical properties. With a main interest in the parameters of the neutral atmosphere, there is the need to perform a correction of the ionospheric contribution to the bending angle. Since this correction is an approximation to first order, there exists an ionospheric residual, which can be expected to be larger when the ionization is high (day versus night, high versus low solar activity). The ionospheric residual systematically affects the accuracy of the atmospheric parameters at low altitudes, at high altitudes (above 25–30 km) it even is an important error source. In climate applications this could lead to a time dependent bias which induces wrong trends in atmospheric parameters at high altitudes. The first goal of our work was to study and characterize this systematic residual error. In a second step we developed a simple correction method, based purely on observational data, to reduce this residual for large ensembles of RO profiles. In order to tackle this problem, we analyzed the bending angle bias of CHAMP and COSMIC RO data from 2001–2011. We could observe that the nighttime bending angle bias stays constant over the whole period of 11 yr, while the daytime bias increases from low to high solar activity. As a result, the difference between nighttime and daytime bias increases from about −0.05 μrad to −0.4 μrad. This behavior paves the way to correct the solar cycle dependent bias of daytime RO profiles. In order to test the newly developed correction method we performed a simulation study, which allowed to separate the influence of the ionosphere and the neutral atmosphere. Also in the simulated data we observed a similar increase in the bias in times from low to high solar activity. In this simulation we performed the climatological ionospheric correction of the bending angle data, by using the bending angle bias characteristics of a solar cycle as a correction factor. After the climatological ionospheric correction the bias of the simulated data improved significantly, not only in the bending angle but also in the retrieved temperature profiles

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

“…For the large majority of events, u r LF1 lies between about 0.5 and 3 mm in the range between 30 km and 75 km. Note that these results show the random uncertainties after the application of the basic BWS filter (Section 3.1.1), 15 but the input uncertainties u r Lr1 are of similar shape (though larger in magnitude). Figure 10b shows that the correlation length profiles of the CHAMP ensemble (gray) and its ensemble mean (yellow) are of relatively constant magnitude from 35 to 80 km, but then get smaller downward, because the RO event's scan velocity decreases (see Equation A13).…”

“…The residual higher order ionospheric effects are accounted for by a 'conservative best-guess' value (0.05 µrad, reflecting results of Liu et al (2015) and Danzer et al (2013Danzer et al ( , 2015) and added (in root-mean-square form) to the systematic uncertainty 5 profile u s αr , leading to a total estimated systematic uncertainty in this example case of ∼ 0.07 µrad (Figure 8b). Within this uncertainty, the one dominating component from orbit uncertainties (∼ 0.05 µrad, cf.…”

“…The next step is a five-point differentiation operation (Item 1.4 in Figure 2) used to calculate the Doppler shift profile D r (t) 15 from the filtered excess phase profile L F (t). The resulting dual-frequency Doppler shift profiles are plotted along with the model profile D m (t) in Figure 5a for the example case.…”

“…It is interpreted as an apparent systematic uncertainty estimate, since due 25 to the linear fit-based TBAE construction its event-to-event bias character will be essentially random (Scherllin-Pirscher et al, 2011b). Also, the ionospheric correction currently applied in the rOPS is just a first-order correction, which will leave higher-order residual ionospheric errors in α r (e.g., Syndergaard, 2000;Danzer et al, 2013;Liu et al, 2013Liu et al, , 2015Healy and Culverwell, 28 Atmos. Meas.…”

“…Depending on their nature, components of the systematic uncertainty that we need to estimate can be fundamentally sys- 15 tematic across different RO events, a subtype we term estimated basic systematic uncertainties, or appear systematic just for individual RO events, a second subtype that we term estimated apparent systematic uncertainties. It is important to distinguish these two subtypes, since the apparent systematic uncertainties will essentially behave as random uncertainties in ensembleaveraging over many RO events, such as when generating climatologies, while the basic systematic ones will not average out and therefore fundamentally limit the (absolute) accuracy of ensemble averages such as climatologies.…”