This paper presents observations of postsunset super plasma bubbles over China and adjacent areas during the second main phase of a storm on 8 September 2017. The signatures of the plasma bubbles can be seen or deduced from (1) deep field‐aligned total electron content depletions embedded in regional ionospheric maps derived from dense Global Navigation Satellite System networks, (2) significant equatorial and midlatitudinal plasma bite‐outs in electron density measurements on board Swarm satellites, and (3) enhancements of ionosonde virtual height and scintillation in local evening associated with strong southward interplanetary magnetic field. The bubbles/depletions covered a broad area mainly within 20°–45°N and 80°–110°E with bifurcated structures and persisted for nearly 5 hr (∼13–18 UT). One prominent feature is that the bubbles extended remarkably along the magnetic field lines in the form of depleted flux tubes, reaching up to midlatitude of around 50°N (magnetic latitude: 45.5°N) that maps to an altitude of 6,600 km over the magnetic equator. The maximum upward drift speed of the bubbles over the magnetic equator was about 700 m/s and gradually decreased with altitude and time. The possible triggering mechanism of the plasma bubbles was estimated to be storm time eastward prompt penetration electric field, while the traveling ionospheric disturbance could play a role in facilitating the latitudinal extension of the depletions.

This study presents observations of nighttime spread F/ionospheric irregularities and spread Es at low and middle latitudes in the South East Asia longitude of China sectors during the recovery phase of the 7–9 September 2017 geomagnetic storm. In this study, multiple observations, including a chain of three ionosondes located about the longitude of 100°E, Swarm satellites, and Global Navigation Satellite System (GNSS) ROTI maps, were used to study the development process and evolution characteristics of the nighttime spread F/ionospheric irregularities at low and middle latitudes. Interestingly, spread F and intense spread Es were simultaneously observed by three ionosondes during the recovery phase. Moreover, associated ionospheric irregularities could be observed by Swarm satellites and ground-based GNSS ionospheric TEC. Nighttime spread F and spread Es at low and middle latitudes might be due to multiple off-vertical reflection echoes from the large-scale tilts in the bottom ionosphere. In addition, we found that the periods of the disturbance ionosphere are ~1 h at ZHY station, ~1.5 h at LSH station and ~1 h at PUR station, respectively. It suggested that the large-scale tilts in the bottom ionosphere might be produced by LSTIDs (Large scale Traveling Ionospheric Disturbances), which might be induced by the high-latitude energy inputs during the recovery phase of this storm. Furthermore, the associated ionospheric irregularities observed by satellites and ground-based GNSS receivers might be caused by the local electric field induced by LSTIDs.

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

A data ingestion method in reproducing ionospheric electron density and total electron content (TEC) was developed to incorporate TEC products from the Madrigal Database into the NeQuick 2 model. The method is based on retrieving an appropriate global distribution of effective ionization parameter (Az) to drive the NeQuick 2 model, which can be implemented through minimizing the difference between the measured and modeled TEC at each grid in the local time‐modified dip latitude coordinates. The performance of this Madrigal TEC‐driven‐NeQuick 2 result is validated through the comparison with various International Global Navigation Satellite Systems Services global ionospheric maps and ionosonde data. The validation results show that a general accuracy improvement of 30–50% can be achieved after data ingestion. In addition, the empirical orthogonal function (EOF) analysis technique is used to construct a parameterized time‐varying global Az model. The quick convergence of EOF decomposition makes it possible to use the first six EOF series to represent over 90% of the total variances. The intrinsic diurnal variation and spatial distribution in the original data set can be well reflected by the constructed EOF base functions. The associated EOF coefficients can be expressed as a set of linear functions of F_10.7 and Ap indices, combined with a series of trigonometric functions with annual/seasonal variation components. The NeQuick TEC driven by EOF‐modeled Az shows 10–15% improvement in accuracy over the standard ionosphere correction algorithm in the Galileo navigation system. These preliminary results demonstrate the effectiveness of the combined data ingestion and EOF modeling technique in improving the specifications of ionospheric density variations.

The geomagnetic storm caused numerous disturbances of the ionosphere, particularly, plasma irregularities, which can affect the accuracy of Global Positioning System (GPS) positioning. This study quantifies the impacts of the ionospheric disturbance on GPS precise point positioning (PPP) solutions during extreme space weather events by taking advantage of 260 GPS stations in China. The results show that the average positioning error in China increased significantly during the ionospheric disturbance on 08 Sep 2017. By comparing the kinematic GPS‐PPP (3‐D) error distribution map and the ROTI index map, it is found that the ionospheric irregularities are one of the main reasons for degrading the GPS positioning accuracy. The statistical analysis results show that when the ionospheric scintillation index S4 is greater than 0.4, the average kinematic GPS‐PPP error calculated by the station is higher than 0.8 m.