Assessing space weather modeling capability is a key element in improving existing models and developing new ones. In order to track improvement of the models and investigate impacts of forcing, from the lower atmosphere below and from the magnetosphere above, on the performance of ionosphere-thermosphere models, we expand our previous assessment for 2013 March storm event [Shim et al., 2018]. In this study, we evaluate new simulations from upgraded models (Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model version 4.1 and Global Ionosphere Thermosphere Model (GITM) version 21.11) and from NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) version 2.2 including 8 simulations in the previous study. A simulation of NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model version 2 (TIE-GCM 2) is also included for comparison with WACCM-X. TEC and foF2 changes from quiet-time background are considered to evaluate the model performance on the storm impacts. For evaluation, we employ 4 skill scores: Correlation coefficient (CC), root-mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (Yield), and timing error(TE). It is found that the models tend to underestimate the storm-time enhancements of foF2 (F2-layer critical frequency) and TEC (Total Electron Content) and to predict foF2 and/or TEC better in the North America but worse in the Southern Hemisphere. The ensemble simulation for TEC is comparable to results from a data assimilation model (Utah State University-Global Assimilation of Ionospheric Measurement (USU-GAIM)) with differences in skill score less than 3% and 6% for CC and RMSE, respectively.

This study reports two 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 eight hours 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 volcanic eruption using the EPB occurrence climatology derived from Swarm satellite data.

In our previous study (Moon et al., 2020), we developed a Long Short Term Memory (LSTM) deep-learning model for geomagnetic quiet days to perform effective long-term predictions for the regional ionosphere. However, their model could not predict geomagnetic storm days effectively at all. This study developed an LSTM model suitable for geomagnetic storms using the new training data set and re-designing input parameters and hyper-parameters. We collected 131 days of geomagnetic storm cases from 1 January 2009 to 31 December 2019, and obtained the IMF Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date. These indices and F2 parameters of Jeju ionosonde (33.43˚N, 126.30˚E) were used as input parameters for the LSTM model. To test and verify the predictive performance and the usability of the LSTM model for geomagnetic storms developed in this manner, we created and diagnosed the 0.5, 1, 2, 3, 6, 12, and 24-hour predictive LSTM models. According to the results of this study, the LSTM storm model for 24-hour developed in this study achieved a predictive performance during the geomagnetic storms about 32% (10%), 34% (17%), and 37% (5%) better in RMSE of foF2 (hmF2) than the LSTM quiet model (Moon et al., 2020), SAMI2, and IRI-2016 models. We propose that the short-term predictions of less than 3 hours are sufficiently competitive compared to other traditional ionospheric models. Thus, this study suggests that our model can be used for short-term prediction and monitoring of the regional mid-latitude ionosphere.

Electromagnetic energy carried by magnetohydrodynamic modes is an important mechanism in the energy transfer between the magnetosphere and the ionosphere. Through wave reflection in the ionosphere, Alfvén waves are known to carry field-aligned currents, and thus play an important role in the dynamics of the ionosphere-magnetosphere coupling. The role of Hall conductance in this interplay has been explored in magneto-hydrodynamic models of the ionosphere, but has hitherto not been observed in-situ. We use five years of observations from the Swarm mission to shed light on this interplay. We present the high-latitude climatology of both the measured Poynting flux and the measured Alfvén wave reflection coefficient. Our results indicate that high-energy deeply penetrating precipitation, which directly leads to strongly enhanced Hall conductance, is an important cause of positively interfering Alfvén wave reflection. We present such observational evidence, and with that, suggest that Hall conductance is substantially more important in the ionospheric wave reflection climatology than hitherto believed.

This paper presents a study on the possibility of predicting the regional ionosphere at mid-latitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the SAMI2 model. The NN model was constructed from the dataset of Jeju ionosonde (33.43˚N, 126.30˚E) for the period of 1 Jan 2011 to 31 Dec 2015 by using the long-short term memory (LSTM) algorithm. The NN model provides 24-hour prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2-LSTM model estimates the ionospheric conditions over the mid-latitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2-LSTM by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root mean square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 31%, 22%, and 29% (35%, 24%, and 17%) than those of the SAMI2, TIEGCM, and IRI-2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of SAMI2-LSTM, like all other models, is significantly worse than during the quiet periods. We discuss the advantage and possible improving strategy on the predictability of the regional mid-latitude ionosphere by utilizing data-assimilated physics models and deep-learning techniques together.

Broad plasma depletions (BPDs) are bubble-like plasma depletions in the equatorial F region whose longitudinal widths (> 4 degree) are greater than those of regular bubbles. Their occurrence in satellite observations is understood in terms of the uplift of the ionosphere; BPDs are observed when satellites pass through the bottomside of bubbles. However, a merger of bubbles is also suggested as the cause of BPDs. We investigate the origin of BPDs by examining the occurrence climatology of BPDs and its association with vertical plasma motion. Our preliminary results derived from the C/NOFS observations in 2008–2012 show that BPDs occur more frequently during lower solar activity, during higher magnetic activity, and at lower altitudes. BPDs during solar maximum and minimum periods show different behavior. BPDs during solar maximum period occur frequently at premidnight and during the equinoxes and December solstices (for highly geomagnetically disturbed periods). On the contrary, BPDs during the solar minimum period occur predominantly at postmidnight and during the June solstices. The occurrence rates of postmidnight BPDs are positively correlated with AE index and are inversely correlated with 10.7 cm solar radio flux. Low solar activity creates favorable conditions for generating BPDs by thinning the F region. At the solar minimum, the density of the F region’s bottomside changes significantly even with slight altitude shifts, which can be recognized as BPDs. When a geomagnetic disturbance occurs, the eastward electric field can be enhanced at the equatorial F region, and the entire F layer can move upward.