Tomographic imaging of the equatorial and low-latitude ionosphere over central-eastern Brazil

Abstract

A four-dimensional time-dependent tomographic algorithm, named Multi Instrument Data Analysis System (MIDAS), is used to image the equatorial and low-latitude ionosphere over the central-eastern sides of the Brazilian territory. From differential phase data obtained by a chain of ground-based GPS receiver the total electron content (TEC) is estimated and then, together with a modeled ionosphere from International Reference Ionosphere (IRI) model, the electron density distribution is reconstructed and the parameters of the F 2 -peak layer are accessed from the images. This paper presents the rst study of ionospheric tomography using real dual-frequency data from the Brazilian Network for Continuous GPS Monitoring (RBMC). Ionospheric F 2 -peak electron density (N m F 2 ) accessed from the images are compared to concurrent measurements from three ionosondes installed across Brazil. One year of data during the solar maximum period from March/2001 to February/2002 is used to analyze the seasonal and hourly variation of the F 2 -layer peak density. The accuracy with which MIDAS images the electron density during geomagnetic quiet periods is investigated through its correlation and deviation with the ionosonde and IRI model data, respectively. The main aspects of the reconstruction results at the equatorial ionization anomaly (EIA) region over Brazil are highlighted and discussed.

Analysis of local ionospheric variability based on SVD and MDS at low-latitude GNSS stations

“…During these months (winter and summer), photoelectrons at the equator diminish due to the subsolar points away from the equator and moves to the higher latitudes. Similar types of seasonal and annual variations in EIA crest have been studied in Taiwan and Brazil (Huang and Cheng 1996;Muella et al 2011). During the winter solstice period, we observed the maxima in December, which is slightly higher than September.…”

“…The results represent ionospheric variability based on SVD, MDS and IRI models over the low-latitude regions. There are many reports revealing that GPS-TEC model and IRI model data are highly variable at low latitudes and equatorial regions (Adeniyi et al 2003;Zhang et al 2004;Muella et al 2011). As shown in " Fig.…”

“…In current scenario, the study of ionosphere variability is more important for determining the future time epochs, and it is time-varying characteristics (Kazimirovsky et al 2003;Laštovicka 2006). The time-varying nature of the ionosphere will have different characteristics for different latitudes (Misra and Enge 2006;Muella et al 2011). Therefore, a better precise model is required to explain ionospheric characteristics at low-latitude GNSS stations.…”

Ionospheric slab thickness at the equatorial anomaly region after the deep solar minimum of cycle 23/24

“…As the ionization at the fully developed crests of the anomaly tend to be aligned along the geomagnetic field lines, it results in the presence of strong spatial changes of the ionospheric plasma in the zone adjacent to the equatorial anomaly region. The diurnal pattern of TEC variations at equatorial and low latitudes are known to be directly associated with the variability of the "fountain effect", which causes the latitudinal movements of the crests of the equatorial anomaly (Muella et al, 2011), as well as the changes in the crest-to-trough TEC ratios. The large magnetic declination angle over Brazilian longitudes is also responsible for a seasonal maximum during the December solstice (summer) months in the pre-reversal vertical drift velocity and, an enhancement in the resurgence of the fountain effect during post-sunset hours.…”

Automated identification and tracking of polar-cap plasma patches at solar minimum

“…The patches are identified from the output of an algorithm, the Multi-Instrument Data Assimilation System (MIDAS). MIDAS is a tomographic imaging algorithm, with a ten-year history at the time of writing (Mitchell and Spencer, 2003), that has been applied to many ionospheric studies, for example Alfonsi et al (2011), Muella et al (2011, Rose et al (2009Rose et al ( , 2011 and Yin and Mitchell (2011). It solves an under-determined four-dimensional inverse problem in order to compute a gridbased image of the electron concentration of the ionosphere in a user-selected region.…”