Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach
Abstract We present a machine‐learning‐based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural net model for Relativistic electrons (ORIENT‐R) uses only solar wind conditions and geomag...
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Wiley
2021-12-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002808 |
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| author | Xiangning Chu Donglai Ma Jacob Bortnik W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Hong Zhao Qianli Ma Kun Zhang Daniel N. Baker Xinlin Li Harlan Spence Geoff Reeves |
| author_facet | Xiangning Chu Donglai Ma Jacob Bortnik W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Hong Zhao Qianli Ma Kun Zhang Daniel N. Baker Xinlin Li Harlan Spence Geoff Reeves |
| author_sort | Xiangning Chu |
| collection | DOAJ |
| description | Abstract We present a machine‐learning‐based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural net model for Relativistic electrons (ORIENT‐R) uses only solar wind conditions and geomagnetic indices as input. For the first time, we show that the state of the outer radiation belt can be determined using only solar wind conditions and geomagnetic indices, without any initial and boundary conditions. The most important features for determining outer radiation belt dynamics are found to be AL, solar wind flow speed and density, and SYM‐H indices. ORIENT‐R reproduces out‐of‐sample relativistic electron fluxes with a correlation coefficient of 0.95 and an uncertainty factor of ∼2. ORIENT‐R reproduces radiation belt dynamics during an out‐of‐sample geomagnetic storm with good agreement to the observations. In addition, ORIENT‐R was run for a completely out‐of‐sample period between March 2018 and October 2019 when the AL index ended and was replaced with the predicted AL index (lasp.colorado.edu/home/personnel/xinlin.li). It reproduces electron fluxes with a correlation coefficient of 0.92 and an out‐of‐sample uncertainty factor of ∼3. Furthermore, ORIENT‐R captured the trend in the electron fluxes from low‐earth‐orbit (LEO) SAMPEX, which is a completely out‐of‐sample data set both temporally and spatially. In sum, the ORIENT‐R model can reproduce transport, acceleration, decay, and dropouts of the outer radiation belt anywhere from short timescales (i.e., geomagnetic storms) and very long timescales (i.e., solar cycle) variations. |
| format | Article |
| id | doaj-art-01d6074376e345e6b156165429bd6acc |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-01d6074376e345e6b156165429bd6acc2025-01-14T16:27:22ZengWileySpace Weather1542-73902021-12-011912n/an/a10.1029/2021SW002808Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network ApproachXiangning Chu0Donglai Ma1Jacob Bortnik2W. Kent Tobiska3Alfredo Cruz4S. Dave Bouwer5Hong Zhao6Qianli Ma7Kun Zhang8Daniel N. Baker9Xinlin Li10Harlan Spence11Geoff Reeves12Laboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USASpace Environment Technologies Pacific Palisades CA USASpace Environment Technologies Pacific Palisades CA USASpace Environment Technologies Pacific Palisades CA USADepartment of Physics Auburn University Auburn AL USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USASpace Science Institute Boulder CO USALaboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USALaboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USAInstitute for the Study of Earth, Oceans and Space University of New Hampshire Durham NH USASpace Science and Applications Group Los Alamos National Lab Los Alamos NM USAAbstract We present a machine‐learning‐based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural net model for Relativistic electrons (ORIENT‐R) uses only solar wind conditions and geomagnetic indices as input. For the first time, we show that the state of the outer radiation belt can be determined using only solar wind conditions and geomagnetic indices, without any initial and boundary conditions. The most important features for determining outer radiation belt dynamics are found to be AL, solar wind flow speed and density, and SYM‐H indices. ORIENT‐R reproduces out‐of‐sample relativistic electron fluxes with a correlation coefficient of 0.95 and an uncertainty factor of ∼2. ORIENT‐R reproduces radiation belt dynamics during an out‐of‐sample geomagnetic storm with good agreement to the observations. In addition, ORIENT‐R was run for a completely out‐of‐sample period between March 2018 and October 2019 when the AL index ended and was replaced with the predicted AL index (lasp.colorado.edu/home/personnel/xinlin.li). It reproduces electron fluxes with a correlation coefficient of 0.92 and an out‐of‐sample uncertainty factor of ∼3. Furthermore, ORIENT‐R captured the trend in the electron fluxes from low‐earth‐orbit (LEO) SAMPEX, which is a completely out‐of‐sample data set both temporally and spatially. In sum, the ORIENT‐R model can reproduce transport, acceleration, decay, and dropouts of the outer radiation belt anywhere from short timescales (i.e., geomagnetic storms) and very long timescales (i.e., solar cycle) variations.https://doi.org/10.1029/2021SW002808machine learningneural networkradiation beltenergetic electron fluxesVan Allen Probesforecast |
| spellingShingle | Xiangning Chu Donglai Ma Jacob Bortnik W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Hong Zhao Qianli Ma Kun Zhang Daniel N. Baker Xinlin Li Harlan Spence Geoff Reeves Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach Space Weather machine learning neural network radiation belt energetic electron fluxes Van Allen Probes forecast |
| title | Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach |
| title_full | Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach |
| title_fullStr | Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach |
| title_full_unstemmed | Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach |
| title_short | Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach |
| title_sort | relativistic electron model in the outer radiation belt using a neural network approach |
| topic | machine learning neural network radiation belt energetic electron fluxes Van Allen Probes forecast |
| url | https://doi.org/10.1029/2021SW002808 |
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