Ionospheric models aim to simulate and predict variations in the ionosphere. In this study, we attempt to develop a regional ionospheric model to predict hourly the foF2s (peak frequencies of the F2 layer) and the hmF2s (peak heights of the F2 layer) over the Jeju station (33.43°N, 126.30°E) for up to 24 hours by using a deep learning method. For forecasting the F2 parameters, we adopt the method of long short-term memory (LSTM), which, unlike traditional neural networks, can shed light on sequential variation in time-series data. The inputs of the LSTM model are sequential data for the past 24 hours, which includes the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, the foF2 and the hmF2. The foF2 and the hmF2 data used to train the model are measured by the Jeju ionosonde, operated by the Korean Space Weather Center (KSWC) during the period from 2011 through 2016. To evaluate our model performance, we utilized the root mean square error (RMSE) and the correlation coefficient (CC) as the performance skill scores between the model prediction and the measurement during the 338 days of 2017–2018. The best foF2 model, having 21 hidden neurons and 3 batch sizes, performed with an RMSE of 0.65 MHz and a CC of 0.92 when compared with the value measured during the next hour. The best hmF2 model, having 41 hidden neurons and 24 batch sizes, preformed with an RMSE of 23.8 km and a CC of 0.80. When the forecasting steps were increased, the performances gradually deteriorated. The LSTM model results were compared with the results of the TIE-GCM and the IRI-2016 model for geomagnetically quiet and active periods. On geomagnetic quiet days, the LSTM model showed the best performance among the three models. When the RMSEs of the foF2 (hmF2) predictions were compared, the percentage improvements of the LSTM model were 45% (30%) or more than those of the other models during geomagnetically quiet days. On geomagnetic storm days, however, the LSTM model did not improve predictions any better than the IRI-2016 model, which may be due to the limited amount of training data under the storm condition.

中文翻译：

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使用长短期记忆预测济州站（33.43°N，126.30°E）电离层F2参数

电离层模型旨在模拟和预测电离层的变化。在这项研究中，我们尝试开发一个区域电离层模型，以每小时预测济州站（33.43°N，126.30°E）上空的 foF2s（F2 层的峰值频率）和 hmF2s（F2 层的峰值高度）使用深度学习方法长达 24 小时。对于 F2 参数的预测，我们采用了长短期记忆 (LSTM) 的方法，与传统的神经网络不同，它可以揭示时间序列数据中的顺序变化。LSTM 模型的输入是过去 24 小时的序列数据，其中包括太阳黑子数 (SSN)、每日 F10.7 太阳通量、地磁 Ap 和 Kp 指数、foF2 和 hmF2。用于训练模型的 foF2 和 hmF2 数据由济州离子探空仪测量，由韩国空间气象中心 (KSWC) 在 2011 年至 2016 年期间运营。为了评估我们的模型性能，我们利用均方根误差 (RMSE) 和相关系数 (CC) 作为模型预测之间的性能技能分数以及 2017-2018 年 338 天的测量。最好的 foF2 模型具有 21 个隐藏神经元和 3 个批次大小，与下一小时测量的值相比，RMSE 为 0.65 MHz，CC 为 0.92。最好的 hmF2 模型，具有 41 个隐藏神经元和 24 个批次大小，以 23.8 km 的 RMSE 和 0.80 的 CC 进行预制。当预测步骤增加时，性能逐渐恶化。LSTM 模型结果与 TIE-GCM 和 IRI-2016 模型在地磁安静和活跃时期的结果进行了比较。在地磁安静的日子里，LSTM 模型在三个模型中表现最好。当比较 foF2 (hmF2) 预测的 RMSE 时，LSTM 模型的百分比改进在地磁安静的日子里比其他模型的改进百分比提高了 45% (30%) 或更多。然而，在地磁风暴日，LSTM 模型并没有比 IRI-2016 模型更好地改进预测，这可能是由于风暴条件下的训练数据量有限。