Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements
Abstract Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current...
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003257 |
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| author | Jinxing Li Jacob Bortnik Xiangning Chu Donglai Ma Sheng Tian Chih‐Ping Wang Jerry W. Manweiler Louis J. Lanzerotti |
| author_facet | Jinxing Li Jacob Bortnik Xiangning Chu Donglai Ma Sheng Tian Chih‐Ping Wang Jerry W. Manweiler Louis J. Lanzerotti |
| author_sort | Jinxing Li |
| collection | DOAJ |
| description | Abstract Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory. |
| format | Article |
| id | doaj-art-6d5a7f898e5949afb21945a423a134cf |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-6d5a7f898e5949afb21945a423a134cf2025-01-14T16:26:43ZengWileySpace Weather1542-73902023-05-01215n/an/a10.1029/2022SW003257Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe MeasurementsJinxing Li0Jacob Bortnik1Xiangning Chu2Donglai Ma3Sheng Tian4Chih‐Ping Wang5Jerry W. Manweiler6Louis J. Lanzerotti7Department 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 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 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 USAFundamental Technologies LLC Lawrence KS USACenter for Solar Terrestrial Research Department of Physics New Jersey Institute of Technology Newark NJ USAAbstract Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory.https://doi.org/10.1029/2022SW003257ring currentneural networkmachine learningprotongeomagnetic stormVan Allen Probe |
| spellingShingle | Jinxing Li Jacob Bortnik Xiangning Chu Donglai Ma Sheng Tian Chih‐Ping Wang Jerry W. Manweiler Louis J. Lanzerotti Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements Space Weather ring current neural network machine learning proton geomagnetic storm Van Allen Probe |
| title | Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements |
| title_full | Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements |
| title_fullStr | Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements |
| title_full_unstemmed | Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements |
| title_short | Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements |
| title_sort | modeling ring current proton fluxes using artificial neural network and van allen probe measurements |
| topic | ring current neural network machine learning proton geomagnetic storm Van Allen Probe |
| url | https://doi.org/10.1029/2022SW003257 |
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