We investigate the influence of assumed height for the thin shell ionosphere model on the Total Electron Content (TEC) derived from a small scale Global Positioning System (GPS) network. TEC and instrumental bias are determined by applying a grid-based algorithm to the data on several geomagnetically quiet days covering a 10 month period in 2006. Comparisons of TEC and instrumental bias are made among assumed heights from 250 km to 700 km with an interval of 10 km. While the TEC variations with time follow the same trend, TEC tends to increase with the height of the thin shell. The difference in TEC between heights 250 km and 700 km can be as large as ∼ 8 TECU in both daytime and nighttime. The times at which the TEC reaches its peak or valley do not vary much with the assumed heights. The instrumental biases, especially bias from the satellite, can vary irregularly with assumed height. Several satellites show a large deviation of ∼ 3 ns for heights larger than 550 km. The goodness of fit for different assumed heights is also examined. The data can be generally well-fitted for heights from 350 km to 700 km. A large deviation happens at heights lower than 350 km. Using the grid-based algorithm, there is no consensus on assumed height as related to data fitting. A thin shell height in the range 350 – 500 km can be a reasonable compromise between data fitting and peak height of the ionosphere.

The geomagnetic storms are the disturbances of the Earth's magnetic field, and the impact of solar wind particles enhancement (Buonsanto, 1999;Gonzalez et al., 1994;Kumar & Kumar, 2019). Following geomagnetic storms, the ionosphere also has obvious disturbance. It shows the critical frequency of F2-layer (foF2) or total electron content (TEC) changes obviously. The large decrease of foF2 or TEC is the ionospheric negative storm, and the significant increase of foF2 or TEC is referred to as positive storms (Fagundes et al., 2016;Maruyama et al., 2004). In general, 𝐴𝐴 𝐴𝐴 region ionization is positively correlated to the density ratio of atomic oxygen [O] to the molecular nitrogen 𝐴𝐴 [N2] . Prölss (1987) showed that the negative storm was caused by the change of neutral gas composition. The enhanced ratio of 𝐴𝐴 [N2] /[O] would lead to the chemical loss rate increase, so the ionospheric electron density decreased.There are two main physical mechanisms of ionospheric positive storms. The first is the equatorward wind surge or disturbance wind (Fuller-Rowell et al., 1994). Joule heating raises the temperature of the upper thermosphere and ion drag drives high velocity neutral winds. The heat source drives a global disturbance wind. It propagates to low latitudes and even into the opposite hemisphere. The equatorward wind pushes the ionosphere up to high altitudes where the recombination is ineffective. As a result, the electron density increases under sunlit conditions (Maruyama et al., 2004;Zhao et al., 2008). Because the equatorward wind surge blows from high to low latitudes, the ionospheric disturbance has time delay. During quiet days, the neutral winds (or background winds) are poleward in daytime and turn equatorward after sunset. During storms, the neutral winds are the combination of background and disturbance winds. The daytime neutral wind, which is caused by Joule heating in the auroral region, even turns to equatorward (Fuller-Rowell et al., 1994). Different storms have different intensity, local time, and latitude coverage. de Jesus et al. ( 2016) studied a positive ionospheric storm at equatorial, low and middle latitudes in African sector. The factor was the equatorward disturbance winds and huge wind circulations.

Ground- and space-based Global Navigation Satellite System (GNSS) receivers can provide three-dimensional (3D) information about the occurrence of equatorial plasma bubbles (EPBs). For this study, we selected March 2014 data (during solar maximum of cycle 24) for the analysis. The timing and the latitudinal dependence of the EPBs occurrence rate are derived by means of the rate of the total electron content (TEC) index (ROTI) data from GNSS receivers in China, whereas vertical profiles of the scintillation index S4 are provided by COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate). The GNSS receivers of the low Earth orbit satellites give information about the occurrence of amplitude scintillations in limb sounding geometry where the focus is on magnetic latitudes from 20° S to 20° N. The occurrence rates of the observed EPB-induced scintillations are generally smaller than those of the EPB-induced ROTI variations. The timing and the latitude dependence of the EPBs occurrence rate agree between the ground-based and spaceborne GNSS data. We find that EPBs occur at 19:00 LT and they are mainly situated above the F2 peak layer which descended from 450 km at 20:00 LT to 300 km at 24:00 LT in the equatorial ionosphere. At the same time, the spaceborne GNSS data also show, for the first time, a high occurrence rate of post-sunset scintillations at 100 km altitude, indicating the coexistence of equatorial sporadic E with EPBs.

Ionized by solar radiation, the ionosphere causes a phase rotation or time delay to trans-ionospheric radio waves. Reconstruction of ionospheric electron density profiles with global navigation satellite system (GNSS) observations has become an indispensable technique for various purposes ranging from space physics studies to radio applications. This paper conducts a comprehensive review on the development of voxel-based computerized ionospheric tomography (CIT) in the last 30 years. A brief introduction is given in chronological order starting from the first report of CIT with simulation to the newly proposed voxel-based algorithms for ionospheric event analysis. The statement of the tomographic geometry and voxel models are outlined with the ill-posed and ill-conditioned nature of CIT addressed. With the additional information from other instrumental observations or initial models supplemented to make the coefficient matrix less ill-conditioned, equation constructions are categorized into constraints, virtual data assimilation and multi-source observation fusion. Then, the paper classifies and assesses the voxel-based CIT algorithms of the algebraic method, statistical approach and artificial neural networks for equation solving or electron density estimation. The advantages and limitations of the algorithms are also pointed out. Moreover, the paper illustrates the representative height profiles and two-dimensional images of ionospheric electron densities from CIT. Ionospheric disturbances studied with CIT are presented. It also demonstrates how the CIT benefits ionospheric correction and ionospheric monitoring. Finally, some suggestions are provided for further research about voxel-based CIT.

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In computerized ionospheric tomography (CIT) with ground-based GNSS, the voxels without satellite-receiver ray traversing cannot be reconstructed directly. We present a CIT algorithm based on virtual reference stations (VRSs), called VRS–CIT, to decrease the number of unilluminated voxels and improve the precision of the estimated ionospheric electron density (IED). The VRSs are set at the nodes of grids with a 0.5° × 0.5° resolution in longitude and latitude. We generate the virtual observations with the observations from nearby six or three stations selected according to azimuths and distances. The generation utilizes multi-quadric surface fitting with six stations and triangular linear interpolation with three stations. With the virtual observations added, the IED distribution is reconstructed by the multiplicative algebraic reconstruction technique with the initial values obtained from IRI-2016. The performance of VRS–CIT is examined by using the data from 127 GNSS stations located in 24–46° N and 122–146° E to derive the IED every 30 min. The study focuses on April 29, 2014, with the adaptability of VRS–CIT analyzed by 12 days, evenly distributed around equinoxes and solstices of 2014. The accuracy of the virtual observation is about 1 TECU. Comparing to that derived from CIT with only real observations, the unsolvability of VRS–CIT declined by 4–12% for the whole region, and for the main area, the improvement can be up to 70%. Taking two IED profiles from radio occultation as reference measurements, the mean absolute error (MAE) of IED by VRS–CIT decreases by 6.88% and 8.43%, respectively. Comparing with slant total electron content (STEC) extracted from five additional GNSS stations, the MAE and the root mean square error of the estimated STEC can be reduced up to 17.24% and 33.81%, respectively.

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