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 investigates the responses of the equatorial and low-latitude ionosphere in the American–Atlantic longitude sector during the super geomagnetic storm that occurred on 10–11 May 2024. The investigation utilizes multi-instrument datasets, including ground-based observations (GNSS TEC, ionosonde, and Fabry–Perot interferometer) as well as space-borne satellite measurements (GOLD, Swarm, DMSP, and TIMED). Our findings reveal significant day-to-day variations in the storm-time equatorial ionization anomaly (EIA), summarized as follows: (1) During the main phase of the storm, the low- and mid-latitude ionosphere experienced a positive storm, with TEC drastically enhanced by 50–100% within a few hours. The EIA crests exhibited a substantial poleward expansion, reaching as high as ±35° MLAT. This expansion was caused by the enhanced fountain effect driven by penetration electric fields, along with increased ambipolar diffusion due to transient meridional wind surges. (2) During the recovery phase of the storm, the global ionosphere was characterized by a substantial negative storm with a 50–80% depletion in TEC. The EIA crests were notably suppressed and merged into a single equatorial band, which can be attributed to the composition change effect and the influence of disturbance dynamo electric fields. These results illustrate the complex processes of magnetosphere–ionosphere–thermosphere coupling during a superstorm, highlighting the significant impacts of space weather on the global ionosphere.

Using new observations from the Chinese Meridian Project (CMP), this study examines the characteristics of neutral winds in the East Asian sector during the Mother's Day super‐intense storm in May 2024, primarily focusing on its effects on the disturbances over China and adjacent areas. It is the first time that new measurements from three Dual‐Channel Optical Interferometers (DCOIs) are utilized to analyze storm‐time neutral winds at an altitude of 250 km in northern China. By developing all northern‐hemisphere Super Dual Auroral Radar Network radars including six Chinese Dual Auroral Radar Network (CN‐DARN) radars, the newly derived ionospheric convection pattern and its impacts on neutral winds can be well analyzed. The results show that a strong equatorward wind with a maximum amplitude of ∼400 m/s in the meridional component was observed for the first time during the storm main phase. In the East Asian sector, a negative ionospheric storm over China and adjacent areas was accompanied by this enhancement in equatorward wind in the night of May 10. Additionally, ionospheric convection expanded to 43° magnetic latitude (MLAT) with eastward ion velocities exceeding 800 m/s around 50° MLAT. This can strengthen zonal wind in northern China, producing a notable eastward surge of ∼230 m/s measured by new DCOIs in the dawnside sub‐auroral region. Wave‐like oscillations in neutral winds were observed by multi‐DCOI stations, which were associated with the storm‐time Traveling Atmospheric Disturbances (TADs). During the storm recovery phase, a high‐level total electron content cluster shifted from eastern China to the central regions, which may be attributed to enhanced ∑[O]/[N₂].

This paper presents an epoch analysis of global ionosphere responses to recurrent geomagnetic activity during 79 corotating interaction region (CIR) events from 2004 to 2009. The data used were GPS total electron content (TEC) data from the Madrigal Database at the Massachusetts Institute of Technology Haystack Observatory and the electron density (Ne) data obtained from CHAllenging Minisatellite Payload (CHAMP) observations. The results show that global ionosphere responses to CIR events have some common features. In high and middle latitudes, the total electron content (TEC) showed a significant positive response (increased electron densities) in the first epoch day. A negative TEC response occurred at high latitudes of the American sector following the positive response. The CHAMP Ne showed a daytime positive response in all latitudes and a nighttime negative response in the subauroral region. These negative TEC and Ne responses were found to be related to thermospheric composition (O/N₂) changes during the storms. At all latitudes, the maximum of the TEC positive effect always occurred at 2–6 h after the CIR starting during local daytime and 10–18 h later for the CIR onset during local nighttime. Case studies indicate that the TEC and Ne positive response had a strong dependence on the southward component (Bz) of the interplanetary magnetic field and solar wind speed. This suggests that penetration electric fields that were associated with changes in solar winds might play a significant role in the positive ionospheric response to storms. During the recovery time of the CIR‐produced geomagnetic activity, the TEC positive disturbance at low latitudes sometimes could last for 2–4 days, whereas at middle to high latitudes the disturbance lasted only for 1 day in most cases. A comparison of the ionospheric responses between the American, European and Asian sectors shows that the ionosphere response in the North American sector was stronger than that in the other two regions. The response of f_oF₂ to the CIR events in middle to high latitudes showed a negative response for 2–3 days after the first epoch day. This is different from the response of TEC, which was mostly positive during the same period of time.

As more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two‐dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root‐mean‐square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed.

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.