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Abstract Meteor radar observations provide wind data ranging from 80 to 100 km altitude, while the Michaelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) onboard the Ionospheric Connection Explorer satellite offers wind data above 90 km altitude. This study aims to generate wind profiles in the mesosphere and lower thermosphere by combining the winds derived from meteor radar and MIGHTI observations over the Korean Peninsula from January 2020 to December 2021. The wind profiles derived from the two instruments are continuous at night, but they show discrepancies during the day. The atomic oxygen 557.7 nm (green line) emission intensity measured by MIGHTI peaks at approximately 100 km during the day and 94 km at night. The vertical gradient of the airglow volume emission rate is more pronounced during the day. These differences can cause day‐night differences in the MIGHTI wind retrieval accuracy, potentially leading to discrepancies during the day. 

Abstract This study investigates the distribution and formation mechanisms of ionization troughs inside an auroral oval (referred to as high‐latitude troughs) by analyzing Swarm observations from May–August 2014. Simultaneous measurements of plasma density, 3‐dimensional ion velocity, ionospheric radial current (IRC), and electron temperature are available during this period. Because high‐latitude troughs appear within an auroral oval while mid‐latitude troughs appear at the equatorward edge of the auroral oval, the positioning of troughs relative to the equatorward auroral boundary becomes critical for distinguishing between the two types of troughs. We ascertain the auroral boundary and the orientation of field‐aligned currents using IRC data derived from magnetic field measurements. The principal features of high‐latitude troughs identified from Swarm data include: (a) enhancements in ion velocity and electron temperature, (b) the presence of downward or absent field‐aligned current (FAC), and (c) a more frequent occurrence in the Northern (summer) Hemisphere than in the Southern (winter) Hemisphere and in the dawn and dusk sectors than in the noon and midnight sectors. The alignment of the density minimum with the velocity maximum underscores the role of high‐speed plasma convection in the formation of high‐latitude troughs; atmospheric frictional heating promotes the O⁺loss through dissociative recombination. The prevailing appearance of high‐latitude troughs at dawn and dusk sectors, coupled with downward field‐aligned currents, indicates the involvement of outward electron evacuation in trough formation. 

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 This study reports different properties of ionospheric perturbations detected to the west and south of the Korean Peninsula after the Hunga‐Tonga volcanic eruption on 15 January 2022. Transient wave‐like total electron content (TEC) modulations and intense irregular TEC perturbations are detected in the west and south of the Korean Peninsula, respectively, about 8 hr after the eruption. The TEC modulations in the west propagate away from the epicenter with a speed of 302 m/s. Their occurrence time, propagation direction and velocity, and alignment with the surface air pressure perturbations indicate the generation of the TEC modulations by Lamb waves generated by the eruption. The strong TEC perturbations and L band scintillations in the south are interpreted in terms of the poleward extension of equatorial plasma bubbles (EPBs). We demonstrate the association of the EPBs with the volcanic eruption using the EPB occurrence climatology derived from Swarm satellite data. 

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.