Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period
Abstract 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 pr...
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002741 |
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| author | Jeong‐Heon Kim Young‐Sil Kwak YongHa Kim Su‐In Moon Se‐Heon Jeong JongYeon Yun |
| author_facet | Jeong‐Heon Kim Young‐Sil Kwak YongHa Kim Su‐In Moon Se‐Heon Jeong JongYeon Yun |
| author_sort | Jeong‐Heon Kim |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-cbec25edf04e4791894d5f78a3438335 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-cbec25edf04e4791894d5f78a34383352025-01-14T16:26:53ZengWileySpace Weather1542-73902021-09-01199n/an/a10.1029/2021SW002741Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm PeriodJeong‐Heon Kim0Young‐Sil Kwak1YongHa Kim2Su‐In Moon3Se‐Heon Jeong4JongYeon Yun5Korea Astronomy and Space Science Institute (KASI) Daejeon South KoreaKorea Astronomy and Space Science Institute (KASI) Daejeon South KoreaDepartment of Astronomy, Space Science and Geology Chungnam National University (CNU) Daejeon South KoreaDepartment of Astronomy, Space Science and Geology Chungnam National University (CNU) Daejeon South KoreaKorea Astronomy and Space Science Institute (KASI) Daejeon South KoreaKorea Space Weather Center (KSWC) Jeju South KoreaAbstract 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.https://doi.org/10.1029/2021SW002741ionosphereprediction modelLSTM deep‐learning algorithmgeomagnetic storm period |
| spellingShingle | Jeong‐Heon Kim Young‐Sil Kwak YongHa Kim Su‐In Moon Se‐Heon Jeong JongYeon Yun Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period Space Weather ionosphere prediction model LSTM deep‐learning algorithm geomagnetic storm period |
| title | Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period |
| title_full | Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period |
| title_fullStr | Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period |
| title_full_unstemmed | Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period |
| title_short | Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period |
| title_sort | potential of regional ionosphere prediction using a long short term memory deep learning algorithm specialized for geomagnetic storm period |
| topic | ionosphere prediction model LSTM deep‐learning algorithm geomagnetic storm period |
| url | https://doi.org/10.1029/2021SW002741 |
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