Potential of TIMED/GUVI limb observations for medium-scale traveling ionospheric disturbances study at mid-latitudes

At mid-latitudes, medium-scale traveling ionospheric disturbances (MSTIDs) are the most recurrent type of ionospheric irregularities. During daytime, the common source of MSTIDs is the propagation of atmospheric gravity waves whose origin is generally found in the lower atmosphere. In the nighttime hours, the Perkins instability induces another type of MSTIDs that is correlated with the appearance of sporadic E layers, sometimes leading to spread-F signatures in ionograms.
MSTIDs climatology and characterization have been extensively described during the last two decades, mainly using GNSS measurements. However, only few studies are devoted to the description of their vertical structure and the monitoring of their propagation into the ionosphere, which is helpful to understand their dissipation processes and their physical origin.
The NASA’s TIMED mission was launched in December 2001 on a 74° inclination low-Earth orbit at an altitude of 625 km, which allowed to cover both low and high-latitude regions. The Global Ultraviolet Imager (GUVI) instrument aimed at remotely sense, among others, the ionospheric ion and electron densities. GUVI performs disk observations and limb scans in five FUV wavelength channels, making it an ideal tool to characterize the vertical structure of the ionosphere as well as to contextualize the study.
The purpose of this work is to use GUVI limb scans to characterize MSTIDs preliminary detected by GNSS in mid-latitudes before December 2007, after which the instrument exclusively supplied disk observations. We first select a few MSTID cases during solar maximum conditions that were observed in the Total Electron Content (TEC) by GNSS ground stations. Then, we combine our dataset with GUVI limb observation of the OI-135.6 nm emission to characterize the vertical structure of the MSTIDs. At last, concurrent observations from ionosondes located in the vicinity of the region where the GNSS and GUVI data were obtained will also provide an interesting cross-comparison dataset.