This study investigates the level of influence of geophysical and solar parameters in ionospheric responses during the main phases of two intense geomagnetic storms that occurred on June 23, 2015 and December 20, 2015 using GNSS observations from four African stations: YKRO (7.54° N, 5.54° W), BJCO (6.37° N, 2.45° E), ADIS (8.98° N, 38.75° E), and DAKR (14.71° N, 17.46° W). Slant TEC (STEC) data in Receiver Independent Exchange (RINEX) format from these stations were processed to VTEC. Pearson and partial correlation analyses were employed to examine the relationships between TEC and key interplanetary and geomagnetic parameters. During the June storm, YKRO and BJCO exhibited strong responses primarily associated with solar wind density and temperature, whereas ADIS was mainly controlled by solar wind speed and geomagnetic activity, while DAKR showed weak coupling with external drivers, indicating dominant local ionospheric control. However, during the December storm, YKRO and BJCO displayed strong associations with Bz, Ey, and Dst. ADIS showed its strongest statistical associations with solar-wind speed and geomagnetic activity, while DAKR showed significant associations with solar-wind density and speed. The results reveal substantial spatial and event-to-event variability in storm-time TEC behavior and also that TEC variability during geomagnetic storms is governed by a combination of direct solar wind forcing, geomagnetic modulation, and regional ionospheric processes, with the relative importance of each mechanism varying with storm characteristics and geographic location. These findings improve understanding of storm-time ionospheric dynamics over Africa and provide valuable information for the development of more accurate ionospheric and space weather forecasting models.
Total Electron Content (TEC) is utilized for the estimation of Ionospheric delay. Accurate prediction of TEC helps correct errors in position estimation using range measurements. TEC is influenced by several factors, such as location, time, solar radiation, geomagnetic index, and season. In this research, machine learning algorithms such as DeepAR, DLinear, and FFNN are used to forecast the Vertical TEC. The developed algorithms are evaluated using L5- and S-band pseudorange measurements from an operational NavIC receiver at ACS College of Engineering (ACSCE), Bangalore, India. In order to assess the algorithms, NavIC TEC for the ACSCE station is predicted and analyzed. The NRMSE of DeepAR is 0.0.225, MASE is 1.468 and SMAPE is 18
The 21 August 2017 Great American solar eclipse provided a unique opportunity to examine ionospheric disturbances under rapid and controlled solar radiation reduction. In this study, GNSS-derived Total Electron Content (TEC) observations from three representative stations located at low latitude (BOGT), mid latitude (P398), and high latitude (NYAL) were analyzed to investigate the spatial and temporal characteristics of eclipse-induced plasma variability. Distinct TEC depletion was recorded at all stations during the eclipse interval, with the largest response over the low-latitude sector and comparatively weaker but more irregular changes at high latitudes. To identify short-term disturbances, detrended TEC residuals and continuous wavelet transform analyses were performed. The results revealed enhanced oscillatory activity near and after the maximum eclipse phase, with dominant periodicities in the range of 40–65 min. These signatures indicate the presence of medium-scale traveling ionospheric disturbances (TIDs), most likely generated by eclipse-driven atmospheric gravity waves (AGWs) and subsequent ionosphere-thermosphere coupling. The comparative multi-latitudinal analysis demonstrates that the eclipse response was strongly modulated by regional background ionization and latitude-dependent electrodynamic processes. This study highlights the capability of GNSS observations to capture both large-scale electron density depletion and mesoscale wave perturbations, providing valuable insight into transient ionospheric dynamics during sudden solar forcing events.
At 2324:52 UTC (local time = UTC + 1200) on July 29, 2025, an earthquake (Mw = 8.8) as the most powerful earthquake worldwide since the 2011 magnitude 9.1 Tohoku event in Japan and ranking among the ten largest quakes recorded globally since 1900, struck east of Petropavlovsk-Kamchatsky, Russia (52.512° N, 160.324° E, depth 35 km). Also following the earthquake of the Kamchatka Peninsula, at least seven volcanoes including Klyuchevskoy and Krasheninnikov on July 30 and August 3, respectively erupted. This study analyzed time series variations of 47 parameters derived from 14 independent precursors including surface and air temperature, Outgoing Longwave Radiation (OLR), carbon monoxide (CO), Sulfur dioxide (SO2), ozone, Methane, GPS-Total Electron Content (TEC), electron density (Ne), electron temperature (Te), and magnetic scalar and vector components (B, Bx, By, Bz), collected from satellite observations between June 1 and August 5, 2025, to identify possible seismic and volcanic anomalies in the Lithosphere Atmosphere Ionosphere (LAI) system around the location of the mentioned event. Next, an attempt was made to estimate the earthquake magnitude by integrating the detected LAI anomalies using two theories: Fuzzy Inference System (FIS) and Dempster-Shaffer (DS). Clear LAI anomalies were observed in the period of about 30 days before to 7 days after the earthquake, and the possible LAIC (LAI Coupling) mechanism was discussed. Also, the earthquake magnitude was estimated with reasonable accuracy in the days before the event. The coincidence of the main earthquake, foreshocks, and aftershocks with volcanic activities in the region acknowledges the occurrence of some LAI anomalies. The results of this study, in continuation of previous studies, emphasize that in a very large percentage of powerful earthquakes (Mw > 7.5), the observation of LAI anomalies is inevitable.
This study investigates ionospheric Total Electron Content (TEC) anomalies before, during, and after two geomagnetic storm periods in 2026 over a low-latitude region. Apart from the TEC anomaly analysis, this paper investigates the performance of a hardware-accelerated multi-layer perceptron (MLP) based Field Programmable Gate Array (FPGA) model coupled with an AdamW optimizer for predicting TEC during storm periods January 19, .2026 to January 21, 2026 and March 22, 2026, using TEC data obtained from the BAKO station, along with solar and geomagnetic indices such as SSN, F10.7, Dst, Ap, and KP obtained from the Omniweb which capture the primary physical drivers of ionospheric variability. Multiple statistical parameters, including RMSE, MAE, R2, MAPE, SMAPE, and NRMSE, were used to assess the performance of the MLP-based FPGA model. Performance evaluation parameters indicate that the developed model achieves an RMSE of 3.43 TECU, an MAE of 2.69 TECU, a MAPE of 20.03
