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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.