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Abstract The development of an intense total electron content (TEC) depletion band over the United States during the 8 September 2017 geomagnetic storm was understood as the extension of an equatorial plasma bubble (EPB) to midlatitudes in previous studies. However, this study reports non‐EPB aspects within this phenomenon. First, the simultaneous emergence of the TEC depletion band at midlatitudes and EPBs in the equatorial region indicates that the midlatitude TEC depletion band is not initiated by an EPB. Second, the intensification of TEC depletion at midlatitudes during the decay of TEC depletion at intermediate latitudes is anomalous. Third, the location of the TEC depletion band at midlatitudes is inconsistent with the EPB location estimated from zonal plasma motion. Given ionospheric perturbations in North America from the beginning of the storm, it is plausible that the TEC depletion band was locally generated in association with these perturbations. 

Abstract This study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction. 

Abstract Large amplitude plasma density irregularities have occasionally been detected at night in the midlatitudeFregion during geomagnetic storms. They are often interpreted in terms of equatorial plasma bubbles (EPBs) because midlatitude irregularities have the morphology of EPBs. This study assesses whether morphology can be a determining factor in ascribing the origin of such midlatitude ionospheric irregularities. We address this question by analyzing the observations of the First Republic of China satellite (ROCSAT‐1) and Defense Meteorological Satellite Program (DMSP)‐F14 and ‐F15 satellites during the geomagnetic storms on 12 February 2000 and 29 October 2003. On both days, ROCSAT‐1 detects plasma depletions at midlatitudes in broad longitude regions and DMSP satellites detect isolated severe plasma depletions whose widths and depths are much wider and deeper than those of typical EPBs. The distinguishing characteristics during the storms are the detection of midlatitude depletions only in the Southern Hemisphere and the occurrence of some of these depletions before 19 hr local time and at the longitudes where EPBs are absent in the equatorial region. These characteristics are not explained satisfactorily by the characteristics of EPBs. Considering the detection of some of the midlatitude depletions at the equatorward edge of ionospheric perturbations in midlatitudes, midlatitude depletions are likely ionospheric perturbations that originated from higher latitudes. Because midlatitude depletions can originate from different sources, the morphology alone is not a determining factor of their origin. 

Abstract This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN‐PB). Our interest is to rebuild small‐scale ionosphere structures on the TEC map in a local region where pronounced ionospheric structures, such as the equatorial ionization anomaly, are absent. The reconstructed regional TEC maps have a domain of 120°–135.5°E longitude and 25.5°–41°N latitude with 0.5° resolution. To achieve this, we first train a DCGAN model by using the International Reference Ionosphere‐based TEC maps from 2002 to 2019 (except for 2010 and 2014) as a training data set. Next, the trained DCGAN model generates synthetic complete TEC maps from observation‐based incomplete TEC maps. Final TEC maps are produced by blending of synthetic TEC maps with observed TEC data by PB. The performance of the DCGAN‐PB model is evaluated by testing the regeneration of the masked TEC observations in 2010 (solar minimum) and 2014 (solar maximum). Our results show that a good correlation between the masked and model‐generated TEC values is maintained even with a large percentage (∼80%) of masking. The performance of the DCGAN‐PB model is not sensitive to local time, solar activity, and magnetic activity. Thus, the DCGAN‐PB model can reconstruct fine ionospheric structures in regions where observations are sparse and distinguishing ionospheric structures are absent. This model can contribute to near real‐time monitoring of the ionosphere by immediately providing complete TEC maps.