The ionospheric equivalent slab thickness (τ) is defined as the ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2), and it is a significant parameter representative of the ionosphere. In this paper, a comprehensive statistical analysis of the diurnal, seasonal, solar, and magnetic activity variations in the τ at Guam (144.86°E, 13.62°N, 5.54°N dip lat), which is located near the magnetic equator, is presented using the GPS-TEC and ionosonde NmF2 data during the years 2012–2017. It is found that, for geomagnetically quiet days, the τ reaches its maximum value in the noontime, and the peak value in winter and at the equinox are larger than that in summer. Moreover, there is a post-sunset peak observed in the winter and equinox, and the τ during the post-midnight period is smallest in equinox. The mainly diurnal and seasonal variation of τ can be explained within the framework of relative variation of TEC and NmF2 during different seasonal local time. The dependence of τ on the solar activity shows positive correlation during the daytime, and the opposite situation applies for the nighttime. Specifically, the disturbance index (DI), which can visually assess the relationship between instantaneous τ values and the median, is introduced in the paper to quantitatively describe the overall pattern of the geomagnetic storm effect on the τ variation. The results show that the geomagnetic storm seems to have positive effect on the τ during most of the storm-time period at Guam. An example, on the 1 June 2013, is also presented to analyze the physical mechanism. During the positive storms, the penetration electric field, along with storm time equator-ward neutral wind, tends to increase upward drift and uplift F region, causing the large increase in TEC, accompanied by a relatively small increase in NmF2. On the other hand, an enhanced equatorward wind tends to push more plasma, at low latitudes, into the topside ionosphere in the equatorial region, resulting in the TEC not undergoing severe depletion, as with NmF2, during the negative storms. The results would complement the analysis of τ behavior during quiet and disturbed conditions at equatorial latitudes in East Asia.

The temporal variations and spatial variations in the ionosphere during geomagnetic storms are exceptionally complex and drastic, significantly complicating ionospheric model construction. In this study, we present a multi-site, high-precision ionospheric vertical total electron content ($VTEC$) estimation method by constraining the $VTEC$ when the locations of ionospheric pierce points (IPPs), determined by multiple sites, are nearby. The root mean square error (RMSE) relative to the global ionospheric map (GIM) $VTEC$ is 3.22 TEC units (TECU), with a correlation coefficient of 0.98. This method enables the high-precision estimation of $VTEC$ at IPPs. Utilizing the Gauss–Markov Kalman filter data assimilation algorithm, we consider the relationship between various Dst indices and the ionospheric temporal scales, achieving a regional ionospheric total electron content $\left(TEC\right)$ Map during geomagnetic storms. This approach effectively monitors the impact of geomagnetic storms on the ionospheric total electron content $\left(TEC\right)$ and provides a more accurate representation of ionospheric changes during geomagnetic storms compared to the GIM $TEC$ Map and the International Reference Ionosphere (IRI)-2020 model.

The ionospheric equivalent slab thickness is a key parameter for understanding the plasma distribution in the ionosphere, with direct relevance to satellite navigation, communication, and skywave over‐the‐horizon radar. However, traditional prediction methods often suffer from regional biases, limiting their global applicability. To address this, we develop a novel global prediction model for ionospheric slab thickness by integrating the Extreme Gradient Boosting (XGBoost) algorithm with Ensemble Learning (EL) techniques (XGBoost + EL). This hybrid approach leverages the strong nonlinear modeling capability of XGBoost and the robustness of EL to enhance prediction accuracy and generalization. The model was trained and tested on global occultation data from 2007 to 2020, incorporating key parameters such as vertical total electron content (vTEC), the F2‐layer critical frequency, and solar activity indices. Evaluation results demonstrate the model's high predictive accuracy, achieving a root mean square error (RMSE) of 62.3 km, a mean absolute error (MAE) of 41.5 km, a mean absolute percentage error (MAPE) of 13.1%, and a correlation coefficient ( R ) of 0.904. Notably, our model significantly outperforms the Neustrelitz equivalent Slab Thickness Model (NSTM), reducing RMSE, MAE, and MAPE by 54.8 km, 47.2 km, and 11.8%, respectively. It also shows superior performance over the International Reference Ionosphere (IRI) model, with corresponding reductions of 72.1 km, 57.1 km, and 14.7%. These findings underscore the considerable potential of machine learning for advancing global ionospheric modeling. Future work will focus on model optimization and assessing its applicability across diverse regions and temporal scales to further improve reliability.

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations.

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models.

During geomagnetic storms, ionospheric disturbances can undergo substantial spatiotemporal restructuring and affect high-precision GNSS applications. This study investigates the multiscale ionospheric response over Brazil during the intense geomagnetic storm of 12 November 2025 and examines the associated changes in precise point positioning (PPP) convergence. Multi-source observations, including GNSS TEC/dSTEC, ROTI, JPL Global Ionospheric Maps, ionosonde parameters, and three-dimensional ionospheric tomography, were jointly analyzed. The results show that the storm produced pronounced and nonuniform global TEC anomalies, with the Brazilian sector embedded in a disturbed background. Over Brazil, clear traveling ionospheric disturbance (TID) propagation and ROTI enhancement were observed during the main response phase. The TID developed after approximately 02:05 UT and reached its maximum intensity during 02:25–03:00 UT. Ionosonde observations indicated decreased foF2 and increased h′F2, suggesting electron density depletion and an apparent uplift of the F-region reflection height. The GNSS dSTEC-constrained tomographic reconstruction suggested that the relative perturbation structures were more evident at 150–400 km, especially near 250–350 km. PPP analysis further revealed longer convergence times on the storm day, particularly in the vertical component. These results indicate that the Brazilian ionosphere experienced a multiscale response from global anomalies to regional propagation and vertical restructuring, which was associated with delayed PPP convergence performance.