Prediction of Dynamic Plasmapause Location Using a Neural Network
Abstract As a common boundary layer that distinctly separates the regions of high‐density plasmasphere and low‐density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework PyTorch and high‐quality Van A...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002622 |
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| _version_ | 1841536339330203648 |
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| author | Deyu Guo Song Fu Zheng Xiang Binbin Ni Yingjie Guo Minghang Feng Jianguang Guo Zejun Hu Xudong Gu Jianan Zhu Xing Cao Qi Wang |
| author_facet | Deyu Guo Song Fu Zheng Xiang Binbin Ni Yingjie Guo Minghang Feng Jianguang Guo Zejun Hu Xudong Gu Jianan Zhu Xing Cao Qi Wang |
| author_sort | Deyu Guo |
| collection | DOAJ |
| description | Abstract As a common boundary layer that distinctly separates the regions of high‐density plasmasphere and low‐density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework PyTorch and high‐quality Van Allen Probes data set, we develop a neural network model to predict the global dynamic variation of the plasmapause location, along with the identification of 6,537 plasmapause crossing events during the period from 2012 to 2017. To avoid the overfitting and optimize the model generalization, 5,493 events during the period from September 2012 to December 2015 are adopted for division into the training set and validation set in terms of the 10‐fold cross‐validation method, and the remaining 1,044 events are used as the test set. The model parameterized by only AE or Kp index can reproduce the plasmapause locations similar to those modeled using all five considered solar wind and geomagnetic parameters. Model evaluation on the test set indicates that our neural network model is capable of predicting the plasmapause location with the lowest RMSE. Our model can also produce a smooth magnetic local time variation of the plasmapause location with good accuracy, which can be incorporated into global radiation belt simulations and space weather forecasts under a variety of geomagnetic conditions. |
| format | Article |
| id | doaj-art-86d1ef9ee4e14441973489a1cc26a382 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-86d1ef9ee4e14441973489a1cc26a3822025-01-14T16:31:31ZengWileySpace Weather1542-73902021-05-01195n/an/a10.1029/2020SW002622Prediction of Dynamic Plasmapause Location Using a Neural NetworkDeyu Guo0Song Fu1Zheng Xiang2Binbin Ni3Yingjie Guo4Minghang Feng5Jianguang Guo6Zejun Hu7Xudong Gu8Jianan Zhu9Xing Cao10Qi Wang11Department of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaNational Center for Space Weather Key Laboratory of Space Weather China Meteorological Administration Beijing ChinaSOA Key Laboratory for Polar Science Polar Research Institute of China Shanghai ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaDepartment of Space Physics School of Electronic Information Wuhan University Wuhan ChinaAbstract As a common boundary layer that distinctly separates the regions of high‐density plasmasphere and low‐density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework PyTorch and high‐quality Van Allen Probes data set, we develop a neural network model to predict the global dynamic variation of the plasmapause location, along with the identification of 6,537 plasmapause crossing events during the period from 2012 to 2017. To avoid the overfitting and optimize the model generalization, 5,493 events during the period from September 2012 to December 2015 are adopted for division into the training set and validation set in terms of the 10‐fold cross‐validation method, and the remaining 1,044 events are used as the test set. The model parameterized by only AE or Kp index can reproduce the plasmapause locations similar to those modeled using all five considered solar wind and geomagnetic parameters. Model evaluation on the test set indicates that our neural network model is capable of predicting the plasmapause location with the lowest RMSE. Our model can also produce a smooth magnetic local time variation of the plasmapause location with good accuracy, which can be incorporated into global radiation belt simulations and space weather forecasts under a variety of geomagnetic conditions.https://doi.org/10.1029/2020SW002622plasmapauseneural networkVan Allen Probesspace weather forecast |
| spellingShingle | Deyu Guo Song Fu Zheng Xiang Binbin Ni Yingjie Guo Minghang Feng Jianguang Guo Zejun Hu Xudong Gu Jianan Zhu Xing Cao Qi Wang Prediction of Dynamic Plasmapause Location Using a Neural Network Space Weather plasmapause neural network Van Allen Probes space weather forecast |
| title | Prediction of Dynamic Plasmapause Location Using a Neural Network |
| title_full | Prediction of Dynamic Plasmapause Location Using a Neural Network |
| title_fullStr | Prediction of Dynamic Plasmapause Location Using a Neural Network |
| title_full_unstemmed | Prediction of Dynamic Plasmapause Location Using a Neural Network |
| title_short | Prediction of Dynamic Plasmapause Location Using a Neural Network |
| title_sort | prediction of dynamic plasmapause location using a neural network |
| topic | plasmapause neural network Van Allen Probes space weather forecast |
| url | https://doi.org/10.1029/2020SW002622 |
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