Ionospheric sporadic-E (Es) activity and global morphology were studied using the 50 Hz signal-to-noise ratio amplitude and excess phase measurements from the FormoSat-3/Constellation Observing System for Meteorology, Ionosphere and Climate (FS3/COSMIC) GPS radio occultation (RO) observations. The results are presented for data collected during the last sunspot cycle from mid-2006 to the end of 2017. The FS3/COSMIC generally performed more than 1000 complete E-region GPS RO observations per day, which were used to retrieve normalized L1-band amplitude standard deviation (SDL1) and relative electron density (N_e) profiles successfully. More or less 31% of those observations were identified as Es events based on SDL1 and peak SDL1 altitude criteria. We found that the peak Es-event i values are approximately proportional to the logarithms of the corresponding peak N_e differences. Five major geographical zones were identified, in which the seasonal and diurnal Es occurrence patterns are markedly different. These five zones include the geomagnetic equatorial zone (− 5° < magnetic latitude (ML) < 5°), two extended geomagnetic mid-latitude zones (15° < ML < 55°, and − 55° < ML < − 15°), and two auroral zones (70° < ML, and ML < − 70°). The Es climatology, namely its variations with each identified zone, altitude, season, and local time has been documented.

The Global Positioning System – Radio Occultation (GPS-RO) observations from FormoSat-3 ∕ COSMIC are used to comprehend the global distribution of equatorial plasma bubbles which are characterized by depletion regions of plasma in the F region of the ionosphere. Plasma bubbles that cause intense scintillation of the radio signals are identified based on the S4 index derived from the 1 Hz raw signal-to-noise ratio measurements between 2007 and 2017. The analyses revealed that bubbles influenced by background plasma density occurred along the geomagnetic equator and had an occurrence peak around the dip equator during high solar activity. The peak shifted between the African and American sectors, depending on different solar conditions. Plasma bubbles usually developed around 19:00 local time (LT), with maximum occurrence around 21:00 LT during solar maximum and ∼22:00 LT during solar minimum. The occurrence of bubbles showed a strong dependence on longitudes, seasons, and solar cycle with the peak occurrence rate in the African sector around the March equinox during high solar activity, which is consistent with previous studies. The GPS-RO technique allows an extended analysis of the altitudinal distribution of global equatorial plasma bubbles obtained from high vertical resolution profiles, thus making it a convenient tool which could be further used with other techniques to provide a comprehensive view of such ionospheric irregularities.

Irregularities in electron density usually correlate with ionospheric plasma perturbations. These variations causing radio signal fluctuations, in response, generate ionospheric scintillations that frequently occur in low-latitude regions. In this research, the combination of an artificial neural network (ANN) with the genetic algorithm (GA) was implemented to predict ionospheric scintillations. The GA method was considered for obtaining the ANN model’s initial weights. This procedure was applied to GNSS observations at GUAM (13.58°E, 144.86°N, 201.922H) station for the daily prediction of ionospheric amplitude scintillations via predicting the signal-to-noise ratio (S4) or via prediction of the rate of TEC index (ROTI). Thirty-day modeling was carried out for three months in January, March, and July, representing different seasons of the winter solstice, equinox, and summer solstice during three different years, 2015, 2017, and 2020, with different solar activities. The models, along with ionospheric physical data, were used for the daily prediction of ionospheric scintillations for the consequent day after the modeling. The prediction results were evaluated using S4 derived from GNSS observations at GUAM station. The designed model has the ability to predict daily ionospheric scintillations with an accuracy of about 81% for the S4 and about 80% for the ROTI.