A technique for routinely updating the ITU-R database using radio occultation electron density profiles

Abstract

Well credited and widely used ionospheric models, such as the International Reference Ionosphere or NeQuick, describe the variation of the electron density with height by means of a piecewise profile tied to the F2-peak parameters: the electron density,N m F2, and the height, h m F2. Accurate values of these parameters are crucial for retrieving reliable electron density estimations from those models. When direct measurements of these parameters are not available, the models compute the parameters using the socalled ITU-R database, which was established in the early 1960s. This paper presents a technique aimed at routinely updating the ITU-R database using radio occultation electron density profiles derived from GPS measurements gathered from low Earth orbit satellites. Before being used, these radio occultation profiles are validated by fitting to them an electron density model. A re-weighted Least Squares algorithm is used for down-weighting unreliable measurements (occasionally, entire profiles) and to retrieve N m F2 and h m F2 values-together with their error estimates-from the profiles. These values are used to monthly update the database, which consists of two sets of ITU-R-like coefficients that could easily be implemented in the IRI or NeQuick

A comparison of the LPIM-COSMIC F2 peak parameters determinations against the IRI(CCIR)

“…As was already mentioned in Section 1, in this work we have used a modified version of the LPIM (Brunini et al, 2013) with two main objectives: (i) to identify and eliminate those RO-EDP or ED punctual values that do not fulfill the quality criteria, in other words data cleaning; (ii) transforming the RO-EPD data into F2 peak parameters equivalent to those used by the IRI to anchor the vertical profiles. The technique used in this work to retrieve the F2 peak parameters is based on the LPIM model (Brunini et al, 2013).…”

“…The technique used in this work to retrieve the F2 peak parameters is based on the LPIM model (Brunini et al, 2013). LPIM represents the vertical distribution of the electron density by two different functions depending on the height.…”

“…In this work we estimated the parameters of the F2 layer peak using the C/F3 RO-EDP as the input data. And for doing this we applied a modified version of the LPIM (Brunini et al, 2013). The processing with LPIM seeks two main objectives: (i) data cleaning, to identify and eliminate those RO-EDP or ED punctual values that do not fulfill the quality criteria; (ii) transforming the RO-EPD data into F2 peak parameters equivalent to those used by the IRI to anchor the profiles.…”

Ionospheric electron density profiling and modeling of COSMIC follow-on simulations

“…Figure 1 shows 12 For each simulated RO observation, limb-viewing path TECs are calculated by integrating N e values from the corresponding GNSS to LEO satellites under a straight-line propagation assumption. Two independent empirical N e models used separately in this study are the TWIM and NeQuick (Leitinger et al 2002;Brunini et al 2013), also referred to as "true" (or background) ionospheres. The TWIM was constructed from near-vertical N e profiles retrieved from FS3/COSMIC RO measurements and worldwide ionosonde foF2 and foE data (Tsai et al 2009).…”

An attempt to establish a statistical model of the day-to-day variability of the NmF2 and hmF2 parameters computed from IRI

“…In order to automatically scan the CDAAC database and mitigate the impact of unreliable ROP in the results of this research we applied a processing strategy based on fitting every ROP with the La Plata Ionospheric Model (LPIM; Brunini et al, 2013), and carefully checking the statistical significance of the differences between the ROP and LPIM. This procedure does not guarantee the accuracy of the retrieved parameters, but performs reasonably well in filtering out automatically many unreliable observations and also unreliable ROP (a task that cannot be done manually due to the huge amount of data).…”