Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

Abstract

This paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long-short term memory (LSTM) algorithm. The NN model provides 24-hr 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 midlatitude 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 45% and 45% (30% and 11%) than those of the SAMI2 and IRI-2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2-LSTM model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2-LSTM model, the root-mean-square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN-LSTM model and physics-based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics-based model further.

Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period

“…Previous studies (Kim et al., 2020; Moon et al., 2020) performed good predictions during geomagnetically quiet periods but did not predict well during geomagnetic storms. We pointed out that most of the long‐term training data included geomagnetically quiet days.…”

“…The test period (first prediction target) in this study is 3 days from September 6 to September 8, 2017, the geomagnetic storm case used in the previous study (Kim et al., 2020; Moon et al., 2020). Figure 2 shows the space environment changes during the geomagnetic storm of test event #1 (see Figure 2 in Kim et al., 2020). Two more test events were analyzed to evaluate the performance of the LSTM model in this study.…”

“…Especially, the long‐range high frequency (HF) communication is dominantly affected by reflection from the ionosphere F2 layer. Although numerous models have been developed to simulate and predict changes in the ionosphere of the F2 layer, it is relatively recent that the deep‐learning technology began to be utilized in the ionospheric models (e.g., Altinay et al., 1997; Athieno et al., 2017; Fan et al., 2019; Hu & Zhang, 2018; Kim et al., 2020; McKinnell & Poole, 2000; Moon et al., 2020; Nakamura et al., 2007; Oyeyemi et al., 2005; Poole & Poole, 2002; Sai Gowtam & Tulasi Ram, 2017; Tulasi Ram et al., 2018; Wichaipanich et al., 2017; Williscroft & Poole, 1996; Wintoft & Cander, 2000; Yue et al., 2006).…”

“…Kim et al. (2020) assimilated the F2 parameter predicted from Moon et al. (2020) into a physics‐based model to predict the midlatitude ionosphere for up to 24 h. Although the LSTM based model showed reasonably good predictive performance on geomagnetically quiet days, the model prediction was poor for geomagnetic storm days.…”

“…Our previous work (Moon et al, 2020) predicted both foF2 and hmF2 parameters using the LSTM model. Kim et al (2020) assimilated the F2 parameter predicted from Moon et al (2020) into a physics-based model to predict the midlatitude ionosphere for up to 24 h. Although the LSTM based model showed reasonably good predictive performance on geomagnetically quiet days, the model prediction was poor for geomagnetic storm days. The reason for poor prediction for geomagnetic storm days may be due to the fact that the training data for the LSTM model were obtained mostly for geomagnetic quiet days, so that the model was biased to the quiet condition.…”