The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.

Forecasting ionospheric total electron content (TEC) has been of great interest among ionospheric researchers and radio propagation scientists as it critically affects the operation of dynamic technological systems relying on space-based radio propagation. In this context, this article attempts to forecast ionospheric TEC using a bi-long short-term memory (Bi-LSTM) deep learning neural network model at a low-latitude location (KLEF Campus; geographic 16 $^{\circ}$ 26’N, 80 $^{\circ}$ 37’E; dip 22 $^{\circ}$ 11’) in Guntur, India. We employed the TEC observables from a specialized global navigation satellite system (GNSS) ionospheric scintillation and TEC monitoring (GISTM) receiver as input data for the deep learning model. To include external ionospheric characteristics, geomagnetic and solar indices (Kp, ap, F10.7, sunspot number (SSN), and Sym-H) were also considered whose individual effects are realized before incorporating them in the model. The performance of the Bi-LSTM model was compared with the conventional LSTM, time-series autoregressive integrated moving average (ARIMA), the latest edition of global empirical international reference ionosphere (IRI-2020) models, and the TEC extracted from hourly global ionospheric maps (GIMs) using statical metrics like root-mean-square error (RMSE), maximum absolute error (MAE), and R error measurements. The Bi-LSTM model showed a low RMSE value of 7.73 TEC unit (TECU), while the LSTM and ARIMA models had values of 8.16 and 8.39 TECU, respectively. The Bi-LSTM model also had the highest correlation ( R $=$ 0.962), followed by the LSTM ( R $=$ 0.957) and ARIMA ( R $=$ 0.956). The day-to-day performance of the TEC forecast was evaluated for quiet and disturbed days, confirming a substantial performance irrespective of geomagnetic activity. Further assessment of the model performance at other latitudes in a wider range of the Indian longitude sector may provide more insight into its predictability for regional deployment.

The present study analyzes the global ionospheric response to the intense geomagnetic storm of 10–11 October 2024 (SYM—H minimum of −346 nT), using observations from COSMIC—2 and Swarm satellites, GNSS TEC, and Digisondes. Significant uplift of the F-region was observed across both Hemispheres on the dayside, primarily driven by equatorward thermospheric winds and prompt penetration electric fields (PPEFs). However, this uplift did not correspond with increases in foF2 due to enhanced molecular nitrogen-promoting recombination in sunlit regions and the F2 peak rising beyond the COSMIC—2 detection range. In contrast, in the Southern Hemisphere nightside ionosphere exhibited pronounced Ne depletion and low hmF2 values, attributed to G-conditions and thermospheric composition changes caused by storm-time circulation. Strong vertical plasma drifts exceeding 100 m/s were observed during both the main and recovery phases, particularly over Ascension Island, driven initially by southward IMF—Bz-induced PPEFs and later by disturbance dynamo electric fields (DDEFs) as IMF—Bz turned northward. Swarm data revealed a poleward expansion of the Equatorial Ionization Anomaly (EIA), with more pronounced effects in the Southern Hemisphere due to seasonal and longitudinal variations in ionospheric conductivity. Additionally, the storm excited Large-Scale Travelling Ionospheric Disturbances (LSTIDs), triggered by thermospheric perturbations and electrodynamic drivers, including PPEFs and DDEFs. These disturbances, along with enhanced westward thermospheric wind and altered zonal electric fields, modulated ionospheric irregularity intensity and distribution. The emergence of anti-Sq current systems further disrupted quiet-time electrodynamics, promoting global LSTID activity. Furthermore, storm-induced equatorial plasma bubbles (EPBs) were observed over Southeast Asia, initiated by enhanced PPEFs during the main phase and suppressed during recovery, consistent with super EPB development mechanisms.

The present study examines the global ionospheric response to the “Mother’s Day Superstorm” (10–11 May 2024), one of the most intense geomagnetic storms since 1957, with a minimum SYM-H index of −436 nT. Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) Radio Occultation (RO) data indicated an increase in the F2 layer maximum critical frequency (foF2) over midlatitude dayside regions, which was accompanied by a significant F-region uplift (hmF2 increase) on a global scale, even on the nightside during the main and recovery phases. At the same time, a decrease in foF2 was observed on the nightside. High southeastward and vertical drift velocities were observed in the nightside sector of the northern hemisphere with the dayside sector exhibiting upward and southwestward-to-northwestward drifts during the main and recovery phases of the storm. An intense upward drift (~170 m/s) in the southern hemisphere was registered with the poleward expansion of the Equatorial Ionization Anomaly (EIA) during the main phase. Swarm A data highlighted the EIA expansion from ~45°N to 60°S during the dayside main phase and from ~30°N to 40°S on the nightside during recovery.

The present study examines the negative ionospheric response over Europe during two geomagnetic storms on 10–13 May 2024, known as the Mother’s Day geomagnetic superstorm. The first storm, with a peak SYM-H value of −436 nT, occurred in the interval 10–11 May, while the second, less intense storm (SYM-H~−103 nT), followed in the interval 12–13 May. Using data from four European locations, temporal and spatial variations in ionospheric parameters (TEC, foF2, and hmF2) were analyzed to investigate the morphology of the strong negative response. Sharp electron density (Ne) depletion is associated with the equatorward displacement of the Midlatitude Ionospheric Trough (MIT), confirmed by Swarm satellite data. A key finding was the absence of foF2 and hmF2 values over all ionosonde stations during the recovery phase of the storms, likely due to the coupling between the Equatorial Ionization Anomaly (EIA) crests and the auroral ionosphere influenced by the intense uplift of the F layer. Relevant distinct features such as Large-scale Travelling Ionospheric Disturbance (LSTID) signatures and Spread F were also noted, particularly during the initial and main phase of the first storm over high midlatitude regions. Regional effects varied, with high European midlatitudes exhibiting different features compared to lower European latitude areas.