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.

中文翻译：

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通过将LSTM训练的结果吸收到SAMI2模型中来估计区域电离层参数

本文介绍了通过将预测的电离层参数从神经网络（NN）模型吸收到Sami2是电离层的另一种模型（SAMI2）中来预测中纬度区域电离层的可能性的研究。通过使用长期记忆（LSTM）算法，从济州离子探空仪（33.43°N，126.30°E）的数据集构建了2011年1月1日至2015年12月31日的NN模型。NN模型提供了济州岛上F 2层的峰值密度（NmF 2）和峰值高度（hmF 2）的24小时预测。预测的NmF 2和hmF2被用来计算两个电离层驱动器（总离子密度和有效中性子午风），它们被吸收到SAMI2模型中。SAMI2-LSTM模型估算了同一地磁子午面上济州岛中纬度地区的电离层状况。我们通过比较地磁平静和暴风雨期间的预测NmF 2和hmF 2值与测量值来评估SAMI2-LSTM的性能。NmF 2（hmF的均方根误差值2）在静磁期间，济州离子探空仪的测量值比SAMI2和IRI-2016模型的测量值低45％和45％（30％和11％）。但是，在地磁风暴期间，SAMI2-LSTM模型的性能不能很好地预测正地磁风暴。比较SAMI2-LSTM模型的静默期和风暴期，计算得出济州（利川）风暴期的均方根误差（RMSE）比静默期高2.76（3.2）倍。从这些结果，我们证明，在这项研究中，至少在地磁安静条件下，将NN-LSTM模型与基于物理学的模型相结合可以改善中纬度地区现有理论和经验模型的电离层预测。我们强烈建议这种尝试（以前没有报道过），