We use ground‐based (GNSS, SuperDARN, and ionosondes) and space‐borne (Swarm, CSES, and DMSP) instruments to study ionospheric disturbances due to the 25–26 August 2018 geomagnetic storm. The strongest large‐scale storm‐time enhancements were detected over the Asian and Pacific regions during the main and early recovery phases of the storm. In the American sector, there occurred the most complex effects caused by the action of multiple drivers. At the beginning of the storm, a large positive disturbance occurred over North America at low and high latitudes, driven by the storm‐time reinforcement of the equatorial ionization anomaly (at low latitudes) and by particle precipitation (at high latitudes). During local nighttime hours, we observed numerous medium‐scale positive and negative ionospheric disturbances at middle and high latitudes that were attributed to a storm‐enhanced density (SED)‐plume, mid‐latitude ionospheric trough, and particle precipitation in the auroral zone. In South America, total electron content (TEC) maps clearly showed the presence of the equatorial plasma bubbles, that, however, were not seen in data of Rate‐of‐TEC‐change index (ROTI). Global ROTI maps revealed intensive small‐scale irregularities at high latitudes in both hemispheres within the auroral region. In general, the ROTI disturbance “imaged” quite well the auroral oval boundaries. The most intensive ionospheric fluctuations were observed at low and mid‐latitudes over the Pacific Ocean. The storm also affected the positioning accuracy by GPS receivers: during the main phase of the storm, the precise point positioning error exceeded 0.5 m, which is more than five times greater as compared to quiet days.

On 10 May 2024, a powerful coronal mass ejection arrived at Earth at 17:05UT and caused a major geomagnetic storm. With the minimum SYM‐H excursion of −497 nT (5‐min data), this storm is the largest geomagnetic disturbance since March 1989, and can be categorized as a superstorm. In this work, by using ground‐based and space‐borne instruments, we focus on unusual aspects of the electrodynamic and ionospheric response to the May 2024 storm at middle and low latitudes. Between the storm onset at 17:05UT, and until ∼19:40UT, we observed signatures of strong unshielded prompt penetration electric fields (PPEF), which caused an increase of the equatorial ExB drifts up to 95 m/s. This led to the occurrence of a strong ionospheric super‐fountain effect. The local pre‐noon sector was the first to respond to the PPEF, with a very rapid increase of the ionization and the EIA development in the local morning sector. Whereas, in the afternoon‐evening sector the ionosphere responded with ∼2 hr of delay, and the response continued to intensify even after the equatorial ExB drifts had dropped to undisturbed values. The development of such a powerful super‐fountain effect without or with little electrodynamic forcing is difficult to explain, but it could be due to storm‐time meridional and zonal thermospheric winds. During the early recovery phase of the storm, a second positive ionospheric storm occurred over the Australian‐West Pacific region in the local late afternoon to pre‐midnight sector, driven by another ionospheric uplift associated with the occurrence of smaller‐amplitude equatorial upward ExB drifts.

On 6 February 2023, a series of large earthquakes struck Turkey and Northern Syria. The main earthquake of Mw 7.8 occurred at 01:17:34 UTC and was followed by the three notable (Mw > 5.5) aftershocks within the next 18 min. Then, ∼9 hr later, the biggest aftershock with magnitude Mw 7.5 and a Mw 6.0 earthquake occurred to the north‐east from the first main earthquake. In this work, we use data of ground‐based Global Navigation Satellite Systems (GNSS) receivers in Turkey, Israel and Cyprus to analyze the ionospheric response to this series of earthquakes. We separate these events in two groups: the first sequence of earthquakes (at 01–02 UTC) and the second sequence (at 10–11 UTC). For the first sequence, we observe a clear N‐shaped total electron content (TEC) response after the Mw 7.8 mainshock earthquake and Mw 6.7 aftershock, and a smaller TEC disturbance that is, most likely, caused by the Mw 5.6 earthquake. The latter is now the smallest earthquake detected by using ionospheric GNSS data. The co‐seismic ionospheric disturbances (CSID) propagated from the epicentral area in the south‐west direction with velocities of about 750–830 m/s. For the second sequence, we observed the response to the Mw 7.5 aftershock earthquake and the Mw 6.0 aftershock. The CSID propagated both to the south‐west and the north‐west to the epicentral area, with velocities of about 950–1,100 m/s.

Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper.

The ionosphere is characterized by a large number of disturbances generated in response to a wide range of phenomena, including natural hazards, space weather and man‐made events. Identification of the origin of ionospheric disturbances (ID), especially in real or near‐real‐time (NRT), is an extremely difficult task, and it is one of the most interesting fundamental scientific questions. In this paper we present, for the first time, an approach for an automatic and NRT‐compatible detection and recognition of the source of ionospheric disturbances in time series of total electron content (TEC) measured by the Global Navigation Satellite Systems (GNSS) method. The main idea is (a) to analyze main characteristics (such as spatio‐temporal features and frequency content) of ID generated by known sources, and (b) in NRT, to rapidly examine ID's features, and, based on this information, recognize their source. Currently, our database contains TEC data series with response to earthquakes, volcanic eruptions, tornadoes, explosions, rocket launches, equatorial plasma bubbles and geomagnetic storms. Our developments are important for the future assessment of natural hazards from the ionosphere, and also for NRT Space Weather nowcast and applications. Also, our work presents important information about the physical properties of ID of different origins.