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.

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.