The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model
Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neur...
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003506 |
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| _version_ | 1841536309044183040 |
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| author | Xin Cao Xiangning Chu Jacob Bortnik James M. Weygand Jinxing Li Homayon Aryan Donglai Ma |
| author_facet | Xin Cao Xiangning Chu Jacob Bortnik James M. Weygand Jinxing Li Homayon Aryan Donglai Ma |
| author_sort | Xin Cao |
| collection | DOAJ |
| description | Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale. |
| format | Article |
| id | doaj-art-1d8de361e478456994cec3055a554aa4 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-1d8de361e478456994cec3055a554aa42025-01-14T16:31:22ZengWileySpace Weather1542-73902023-09-01219n/an/a10.1029/2023SW003506The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based ModelXin Cao0Xiangning Chu1Jacob Bortnik2James M. Weygand3Jinxing Li4Homayon Aryan5Donglai Ma6Laboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USALaboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles Los Angeles CA USADepartment of Earth, Planetary and Space Sciences University of California Los Angeles Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles Los Angeles CA USAAbstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.https://doi.org/10.1029/2023SW003506machine learningequivalent ionospheric currentsgeomagnetically induced currentsneural network |
| spellingShingle | Xin Cao Xiangning Chu Jacob Bortnik James M. Weygand Jinxing Li Homayon Aryan Donglai Ma The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model Space Weather machine learning equivalent ionospheric currents geomagnetically induced currents neural network |
| title | The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model |
| title_full | The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model |
| title_fullStr | The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model |
| title_full_unstemmed | The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model |
| title_short | The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model |
| title_sort | response of ionospheric currents to external drivers investigated using a neural network based model |
| topic | machine learning equivalent ionospheric currents geomagnetically induced currents neural network |
| url | https://doi.org/10.1029/2023SW003506 |
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