Approaching the peak year of the 25th solar activity cycle, the frequency of strong geomagnetic storms is gradually increasing, which seriously affects the navigation and positioning performance of GNSS. Based on the globally distributed GNSS station data and FORMOSAT-7/COSMIC-2 occultation data, this paper explores for the first time the effects of the G4-class geomagnetic storm that occurred on 23–24 April 2023 on the global ionosphere, especially the ionospheric equatorial anomalies and F-layer perturbations. It reveals the precise point positioning (PPP) accuracy degradation during a geomagnetic storm. The results show that the ionospheric rate of total electron content index (ROTI) and near high latitude GNSS phase scintillations index have varying levels of perturbation during geomagnetic storms, with the maximum ROTI and phase scintillations index exceeding 0.5 TECU/min and 0.8, respectively. The equatorial ionization anomaly (EIA) shows an enhanced state (positive ionospheric storms) during geomagnetic storms, and the cause of this phenomenon is most likely the equatorward neutral wind. The variation of the S4 index of the FORMOSAT-7/COSMIC-2 satellite reveals the uplift of the F-layer during geomagnetic storms. During geomagnetic storms, the PPP accuracy degrades most seriously at high latitudes, the maximum MAE exceeds 2.3 m, and the RMS in the three-dimensional (3D) direction exceeds 2.0 m. These investigations can provide case support for space weather and GNSS studies of the impact of geomagnetic storms during peak solar activity years.

This paper investigates ionospheric response characteristics from multiple perspectives based on globally distributed GNSS data and products, ionosonde data, FORMOSAT-7/COSMIC-2 occultation data, and Swarm satellite observations caused by the total solar eclipse of 8 April 2024 across North and Central America. The results show that both GNSS-derived TEC products have detected the ionospheric TEC degradation triggered by the total solar eclipse, with the maximum degradation exceeding 10 TECU. The TEC data from nine GNSS stations in the path of the maximum eclipse reveal that the intensity of ionospheric TEC degradation is related to the spatial location, with the maximum degradation value of the ionospheric TEC being about 14~23 min behind the moment of the maximum eclipse. Additionally, a negative anomaly of foF2 with a maximum of more than 2.7 MHz is detected by ionosonde. In the eclipse region, NmF2 and hmF2 show trends of decrease and increase, with percentages of variation of 40~70% and 4~16%, respectively. The Ne profile of the Swarm-A satellite is significantly lower than the reference value during the eclipse period, with the maximum negative anomaly value reaching 11.2 × 10⁵ el/cm³, and it failed to show the equatorial ionization anomaly.

During the main phase of the May 2024 super geomagnetic storm, we investigated large‐scale traveling ionospheric disturbances (LSTID) using data from densely distributed global GNSS receivers. From both two‐dimensional and three‐dimensional (3D) perspectives, we analyzed LSTID propagation characteristics over North America, European, and Australia. Global detrended total electron content (dTEC) time‐sequence maps revealed multiple alternating positive and negative TEC perturbation bands propagating from high to low latitudes, with wavelengths exceeding 1,000 km—typical signatures of LSTID. The strongest disturbances occurred over North America, approaching ±4.0 TECU at 19:00 UT. Estimated meridional propagation speeds ranged from 550 to 570 m/s (19:40 UT) in the North America, 720–730 m/s (18:30 UT) in the European, and 530–540 m/s (18:40 UT) and 730–770 m/s (23:20 UT) in the Australian sector, aligning with known mid‐ and high‐latitude LSTID characteristics. Using GNSS‐based 3D ionospheric tomography, we identified the vertical propagation of LSTID, which was confirmed by ionosonde and SwarmA, demonstrating the method's ability to capture hmF2 dynamics. Furthermore, the AMPERE field‐aligned currents (FACs) and the associated Joule heating exhibit strong spatiotemporal correlation with the observed LSTID, suggesting that high‐latitude FAC‐driven energy input, particularly in the form of Joule heating, plays a critical role in their generation and equatorward propagation. Our study highlights the importance of capturing the three‐dimensional characteristics of LSTID during the storm's main phase to better understand high‐to‐low latitude energy coupling in the ionosphere.

In this paper, GNSS stations’ observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai on 15 January 2022. (i) We detect the negative total electron content (TEC) anomalies by three GNSS stations on 5 January before the volcanic eruption after excluding the influence of solar and geomagnetic disturbances and lower atmospheric forcing. The GIMs also detect the negative anomaly in the global ionospheric TEC only near the epicenter of the eruption on 5 January, with a maximum outlier exceeding 6 TECU. (ii) From 1 to 3 January (local time), the equatorial ionization anomaly (EIA) peak shifts significantly towards the Antarctic from afternoon to night. The equatorial ionization anomaly double peak decreases from 4 January, and the EIA double peak disappears and merges into a single peak on 7 January. Meanwhile, the diurnal maxima of TEC at TONG station decrease by nearly 10 TECU and only one diurnal maximum occurred on 4 January (i.e., 5 January of UT), but the significant ionospheric diurnal double-maxima (DDM) are observed on other dates. (iii) We find a maximum value exceeding NmF2 at an altitude of 100~130 km above the volcanic eruption on 5 January (i.e., a sporadic E layer), with an electron density of 7.5 × 10⁵ el/cm³.

The ionosphere is one of the important error sources that affect the communication of radio signals. The international reference ionosphere (IRI) model is a commonly used model to describe ionospheric parameters. The driving parameter IG12 of the IRI-2016 model was optimally updated based on GNSS-TEC data from 2015 and 2019. The electron density profiles and NmF2 calculated by the IRI-2016 model (upda-IRI-2016) driven by the updated IG12 value (IG-up) were evaluated for their accuracy using ionosonde observations and COSMIC inversion data. The experiments show that both the electron density profiles and NmF2 calculated by upda-IRI-2016 driven by IG-up show significant optimization effects, compared to the IRI-2016 model driven by IG12. For electron density, the precision improvement (PI) for both MAE and RMSE at the Beijing station exceed 31.2% in January 2015 and 16.0% in January 2019. While the PI of MAE and RMSE at the Wuhan station, which is located at a lower latitude, both exceed 32.5% in January 2015, both exceed 42.1% in January 2019, which is significantly higher than that of the Beijing station. In 2015, the PI of MAE and RMSE compared with COSMIC are both higher than 20%. For NmF2, the PI is greater for low solar activity years and low latitude stations, with the Wuhan station showing a PI of more than 11.7% in January 2019 compared to January 2015. The PI compared to COSMIC was higher than 17.2% in 2015.

This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model’s performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models.