During sand and dust storm (SDS) events, atmospheric suspension and transport of sand and dust brings a reasonable amount of electrification in the atmosphere which plays a very important role in the atmosphere-ionosphere coupling. The Godzilla SDS began on 5^th June 2020 in Algeria following a decrease in pressure and spread to other areas across the Sahara between 6^th and 28^th June 2020. Using SDS data from Copernicus Sentinel-5P satellite mission and Vertical Total Electron Content (VTEC) data from four GNSS receiver stations: IFR1 (Ifrane Seismic), MELI (Melilla), TETN (Tetouan) and OUCA (Ouca) over Morocco, we investigate the possible ionospheric TEC variability over the four GNSS receiver stations during the Godzilla SDS event which was tracked using the Sentinel-5P Satellite mission. Solar wind parameters: Horizontal component of Interplanetary Magnetic Field (IMF-Bz), interplanetary Electric Field (IEF-Ey) and solar wind speed (V) and geomagnetic indices: Disturbance Storm Time (Dst) and Planetary K (Kp) indices were examined and showed very minimal geomagnetic influence during the period. We observed major ionospheric disturbances over the four Global Navigation Satellite System (GNSS) receiver stations on 16^th, 17^th, 18^th, 21^st, 22^nd, 23^rd 25^th and 26^th June 2020: the period with the Sentinel-5P Aerosol Index (SAI) of more than 4 as recorded by the Sentinel-5P Satellite engine. The daily VTEC values over the four GNSS receiver stations recorded continuous electron density perturbations during these days. Apart from the ionospheric TEC perturbations, significant enhancements and decreases in daily maximum VTEC values over the four GNSS receiver stations were also noted. These were attributed to the changes in the atmospheric electric fields generated by the SDS event. The VTEC plots for each day exhibited similar trends, hence exhibited the same ionospheric dynamics. VTEC depletions of depths 3 to 6 TECU over all the four GNSS receiver stations were noted on 12^th, 14^th, 17^th, 20^th and 25^th June 2020. Nighttime VTEC enhancements were also noted and majorly occurred between 20:00 and 21:00 UT on 9^th, 13^th, 15^th, 17^th, 19^th, 20^th and 21^st June 2020. This was attributed to the development of the electron avalanche processes including dust and electron absorption or losses and the active conversion to electron dissociative attachment leading to electron excitation. In conclusion, the Godzilla SDS of June 2020 led to the electron density perturbations over Morocco.

Accurate forecasting of high-latitude ionospheric total electron content (TEC) is essential for mitigating space-weather impacts on Global Navigation Satellite System (GNSS) performance. This study benchmarks three machine-learning models—random forest (RF), support vector machine, and long short-term memory networks—against a three-point moving average [MA(3)], the International Reference Ionosphere (IRI-2020), and a Persistence baseline. Using GNSS-derived TEC from four high-latitude stations during the geomagnetically active year 2023, we evaluate model skill using root mean squared error (RMSE), MAE, R2, and error distributions. The results reveal a clear, physically interpretable regime dependence. In the auroral zone, RF reduces RMSE by up to 30% relative to persistence, demonstrating superior skill under storm-driven, intermittent variability. In the polar cap, MA(3) outperforms all machine-learning approaches, achieving R2=0.86–0.87 and reducing RMSE by ∼ 26%, reflecting the smooth, strongly autocorrelated nature of convection-driven TEC. Persistence provides the weakest forecasts but remains an essential lower benchmark. Feature relevance analysis shows that short-term lagged TEC and IRI climatology dominate prediction skill, with geomagnetic drivers exerting stronger influence in auroral regions. By linking forecast performance to plasma regime, this work establishes a physically grounded framework for TEC predictability and clarifies why nonlinear ensemble methods excel in auroral conditions while smoothing-based approaches dominate in the polar cap.

We investigate the ionospheric response to the intense St. Patrick's Day geomagnetic storm of 17 March 2015 (minimum Dst ≈ −223 nT) using Global Navigation Satellite System (GNSS)–derived Total Electron Content (TEC) observations from 11 East African stations: Addis Ababa‐ADIS (9.04°N, 38.77°E), Arusha‐ARSH (3.38°S, 36.69°E), Dodoma‐DODM (6.19°S, 35.75°E), Eldoret‐MOIU (0.29°N, 35.29°E), Malindi‐MAL2 (2.99°S, 40.19°E), Mbarara‐MBAR (0.61°S, 30.65°E), and Mbeya‐MBEY (9.03°N, 38.74°E, Asab‐ASAB (13.06°N. 43.65°E), Ginr‐GINR (7.15°N, 40.71°E), Bahir Dar‐BDMT (11.60°N, 37.36°E), Debarek‐DEBK (13.15°N, 37.89°E), Assosa‐ASOS (10.05°N, 34.55°E). Instrumental biases were removed, data were screened for satellite elevation angles ≥40°, and analyzed using detrended TEC (dTEC) and Morlet continuous wavelet transform to identify storm‐time ionospheric disturbances across multiple temporal scales. The storm main phase was characterized by prolonged southward z‐component of interplanetary magnetic field (IMF‐Bz) (<−20 nT), enhanced solar wind speeds (>600 kms ⁻¹ ), and elevated y‐component of the interplanetary electric field (IEF‐Ey), indicating strong solar wind–magnetosphere coupling. These conditions drove significant ionospheric variability, with the largest dTEC amplitudes (>±4 TECU) observed near the Equatorial Ionization Anomaly (EIA), particularly at ADIS and MAL2. Wavelet analysis revealed the coexistence of medium‐scale traveling ionospheric disturbances (MSTIDs; ∼0.25–1 hr), large‐scale travelling ionospheric disturbances (LSTIDs; ∼1–4 hr), and short‐period fluctuations (<0.25 hr). Disturbances were most pronounced during the storm main and early recovery phases and exhibited clear latitudinal dependence, with enhanced activity near the EIA crest. Post‐sunset ionospheric irregularities, interpreted as equatorial plasma bubbles (EPBs), were identified through sharp dTEC depletions and enhanced short‐period wavelet power. EPBs occurred on most days but showed increased frequency, earlier onset, and wider latitudinal extent during the storm, consistent with intensified pre‐reversal enhancement and storm‐time electric field penetration. The temporal association between MSTIDs and EPB onset suggests a potential seeding role of atmospheric wave activity. These results demonstrate that ionospheric disturbances over East Africa are strongly controlled by the interplay between storm‐time electrodynamics and equatorial processes. The study highlights the effectiveness of combined dTEC and wavelet analysis for resolving multiscale ionospheric variability and provides important insights into space weather impacts on GNSS performance in equatorial regions.

This study presents a novel multitask deep learning framework that integrates models designed for simultaneous, multi‐horizon forecasting of Hp60 and Disturbance storm time (Dst) indices with lead times up to 3 hr. Using a Long Short‐Term Memory architecture, the models capture both mid‐latitude variability and the ring current dynamics. Input features include interplanetary magnetic field (IMF) parameters, solar wind plasma data, and historical geomagnetic indices. Model interpretability is addressed through permutation‐based feature importance analysis, providing physical context to the machine learning predictions. The models demonstrate strong predictive performance across all forecast horizons. For the Dst index, they achieve a Root Mean Square Error (RMSE) of 3.81 nT and Pearson correlation coefficient of 0.98 at 1‐hr horizon ( t + 1 hr), remaining robust (RMSE = 9.01 nT, = 0.91) at 3‐hr horizon ( t + 3 hr). More importantly, the models perform well under both quiet and disturbed geomagnetic conditions. However, while they accurately capture storm onset and recovery phases, they underestimate extreme storm intensities. Hp60 predictability is primarily associated with related mid‐latitude indices, such as planetary K (Kp) and ap, that are derived from similar observatory networks and respond to the same underlying geomagnetic disturbances. Dst forecasting is largely influenced by its own historical values (persistence). Southward IMF is also significantly influential for forecasting both indices during disturbed periods, showing that the models learn physically consistent storm‐time behavior. The multitask approach offers a scalable and efficient pathway for future space weather models by leveraging shared representations of common solar wind drivers.