In our previous study (Moon et al., 2020, https://doi.org/10.3938/jkps.77.1265), we developed a long short-term memory (LSTM) deep-learning model for geomagnetic quiet days (LSTM-quiet) 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 redesigning input parameters and hyper-parameters. We collected 131 days of geomagnetic storm cases from January 1, 2009 to December 31, 2019, provided by the Japan Meteorological Agency's Kakioka Magnetic Observatory, and obtained the interplanetary magnetic field Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date from the OMNI database. These indices and F2 parameters (foF2 and hmF2) 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-h predictive LSTM models. According to the results of this study, the LSTM storm model for 24-h developed in this study achieved a predictive performance during the three geomagnetic storms about 32% (10%), 34% (17%), and 37% (5%) better in root mean square error of foF2 (hmF2) than the LSTM quiet model (Moon et al., 2020, https://doi.org/10.3938/jkps.77.1265), SAMI2, and IRI-2016 models. We propose that the short-term predictions of less than 3 h are sufficiently competitive than 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.

中文翻译：

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使用专门针对地磁风暴期的长短期记忆深度学习算法进行区域电离层预测的潜力

在我们之前的研究（Moon et al., 2020, https://doi.org/10.3938/jkps.77.1265）中，我们开发了一个用于地磁安静日（LSTM-quiet）的长短期记忆（LSTM）深度学习模型) 对区域电离层进行有效的长期预测。然而，他们的模型根本无法有效预测地磁风暴日。本研究使用新的训练数据集并重新设计输入参数和超参数，开发了适用于地磁暴的 LSTM 模型。我们收集了日本气象厅柿冈地磁天文台提供的2009年1月1日至2019年12月31日131天的地磁暴案例，获得了与地磁暴对应的行星际磁场Bz、Dst、Kp和AE指数。 OMNI 数据库中的每个风暴日期。济州离子探空仪 (33.43°N, 126.30°E) 的这些指数和 F2 参数（foF2 和 hmF2）被用作 LSTM 模型的输入参数。为了测试和验证以这种方式开发的地磁暴 LSTM 模型的预测性能和可用性，我们创建并诊断了 0.5、1、2、3、6、12 和 24 小时预测 LSTM 模型。根据本研究结果，本研究开发的 24 小时 LSTM 风暴模型在三个地磁风暴期间实现了约 32% (10%)、34% (17%) 和 37% (5%) 的预测性能) foF2 (hmF2) 的均方根误差优于 LSTM 安静模型（Moon 等人，2020，https://doi.org/10.3938/jkps.77.1265）、SAMI2 和 IRI-2016 模型。我们认为，与其他传统电离层模型相比，少于 3 小时的短期预测具有足够的竞争力。因此，