High-precision VTEC derivation with GEONET

Abstract

This paper proposes a new technique, namely Phase bias-based Small Grid Model (PSGM), to derive absolute ionospheric vertical total electron content (VTEC) with observations of Global Navigation Satellite System Earth Observation Network of Japan (GEONET). The proposed technique deals with the phase observations alone without handling the pseudoranges, which reduces the noise in VTEC estimation. A new parameter, the arc bias (B arc), is introduced to combine the phase ambiguities and differential phase biases. To solve B arc , equations are constructed under the assumption that the VTEC is identical in the same 0.1° × 0.1° grid. The performance of PSGM is evaluated with the observations in solar maximum year 2014. The root mean square error (RMSE) of PSGM is 0.40 TECU in average, the maximum RMSE is 0.73 TECU and the minimum RMSE is 0.26 TECU. The fitting accuracy of the VTEC results is improved compared with most of the existing methods.

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

“…A review of the techniques for modeling the total electron content of the ionosphere can be found in Bust & Mitchell (2008). New developments are still ongoing (see for example Bidaine & Warnant (2010); Ansari et al (2017); Li et al (2020b); Yasyukevich et al (2015Yasyukevich et al ( , 2020Yasyukevich et al ( , 2022; Li et al (2021); Pudlovskiy (2021)); notably machine learning techniques have developed in this field like many others (Orus Perez, 2019;Mallika I et al, 2020;Ferreira et al, 2017). Typical examples of tomography reconstruction algorithm of the ionosphere in the literature, such as Seemala et al (2014), model the TEC over wide timescales (more than an hour, possibly more than a half day) and wide geographical regions (with cells hundreds of kilometers wide).…”