Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model
Abstract The terrestrial ring current consists of particles with energy from several keV to 100 s of keV, and its enhancement will result in magnetic field depression, known as geomagnetic storms. The ring current is mainly composed of H+, O+, He+, and electrons, and there has been a longstanding de...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW003947 |
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| _version_ | 1832583575186178048 |
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| author | Yun Yan Zi‐Kang Xie Chao Yue Jiu‐Tong Zhao Fan Yang Lun Xie Qiu‐Gang Zong Xu‐Zhi Zhou Shan Wang |
| author_facet | Yun Yan Zi‐Kang Xie Chao Yue Jiu‐Tong Zhao Fan Yang Lun Xie Qiu‐Gang Zong Xu‐Zhi Zhou Shan Wang |
| author_sort | Yun Yan |
| collection | DOAJ |
| description | Abstract The terrestrial ring current consists of particles with energy from several keV to 100 s of keV, and its enhancement will result in magnetic field depression, known as geomagnetic storms. The ring current is mainly composed of H+, O+, He+, and electrons, and there has been a longstanding debate regarding their relative contributions. In this study, we employed a multi‐output convolutional neural network to predict the storm‐time ring current plasma pressures of these particles. Taking solar wind parameters, interplanetary magnetic field (IMF) data, and geomagnetic indices with a time history of 3 days as input parameters, the model shows good performances for electron plasma pressure, H+ plasma pressure, He+ plasma pressure, and O+ plasma pressure in both quiet‐time and storm‐time periods, with high correlation coefficients and small root mean square errors between the measured and the predicted values. Our model successfully captures ring current enhancement during the storm main phase and species‐dependent decay during the recovery phase. Moreover, the model's ability to predict plasma pressures of different species simultaneously facilitates a comparative analysis of their respective contributions to the ring current. The storm event where our model was applied demonstrates that during the storm, the contribution of H+ decreases but still dominates, while the contribution of O+ increases dramatically, and electrons and He+ also play roles in some localized regions. The application of this model is in line with previous observations and simulations, which can be utilized for quantitative analysis of storm‐time ring current dynamics. |
| format | Article |
| id | doaj-art-1c22d292e2fc45ad82fc3c79f2ce6d18 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-1c22d292e2fc45ad82fc3c79f2ce6d182025-01-28T10:40:44ZengWileySpace Weather1542-73902025-01-01231n/an/a10.1029/2024SW003947Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network ModelYun Yan0Zi‐Kang Xie1Chao Yue2Jiu‐Tong Zhao3Fan Yang4Lun Xie5Qiu‐Gang Zong6Xu‐Zhi Zhou7Shan Wang8Institute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaInstitute of Space Physics and Applied Technology Peking University Beijing ChinaAbstract The terrestrial ring current consists of particles with energy from several keV to 100 s of keV, and its enhancement will result in magnetic field depression, known as geomagnetic storms. The ring current is mainly composed of H+, O+, He+, and electrons, and there has been a longstanding debate regarding their relative contributions. In this study, we employed a multi‐output convolutional neural network to predict the storm‐time ring current plasma pressures of these particles. Taking solar wind parameters, interplanetary magnetic field (IMF) data, and geomagnetic indices with a time history of 3 days as input parameters, the model shows good performances for electron plasma pressure, H+ plasma pressure, He+ plasma pressure, and O+ plasma pressure in both quiet‐time and storm‐time periods, with high correlation coefficients and small root mean square errors between the measured and the predicted values. Our model successfully captures ring current enhancement during the storm main phase and species‐dependent decay during the recovery phase. Moreover, the model's ability to predict plasma pressures of different species simultaneously facilitates a comparative analysis of their respective contributions to the ring current. The storm event where our model was applied demonstrates that during the storm, the contribution of H+ decreases but still dominates, while the contribution of O+ increases dramatically, and electrons and He+ also play roles in some localized regions. The application of this model is in line with previous observations and simulations, which can be utilized for quantitative analysis of storm‐time ring current dynamics.https://doi.org/10.1029/2024SW003947storm‐time ring currentconvolutional neural networkring current plasma pressure |
| spellingShingle | Yun Yan Zi‐Kang Xie Chao Yue Jiu‐Tong Zhao Fan Yang Lun Xie Qiu‐Gang Zong Xu‐Zhi Zhou Shan Wang Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model Space Weather storm‐time ring current convolutional neural network ring current plasma pressure |
| title | Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model |
| title_full | Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model |
| title_fullStr | Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model |
| title_full_unstemmed | Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model |
| title_short | Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model |
| title_sort | storm time ring current plasma pressure prediction based on the multi output convolutional neural network model |
| topic | storm‐time ring current convolutional neural network ring current plasma pressure |
| url | https://doi.org/10.1029/2024SW003947 |
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