Prediction of Dynamic Plasmapause Location Using a Neural Network. Issue 5 (11th May 2021)

• Record Type: Journal Article
• Authors: Guo, Deyu; Fu, Song; Xiang, Zheng; Ni, Binbin; Guo, Yingjie; Feng, Minghang; Guo, Jianguang; Hu, Zejun; Gu, Xudong; Zhu, Jianan; Cao, Xing; Wang, Qi
• Is Part Of: Space weather. Volume 19:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-11
• Subjects: plasmapause -- neural network -- Van Allen Probes -- space weather forecast; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2020SW002622
• Languages: English
• ISSNs: 1542-7390

Abstract

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. Key Points: A neural network model is constructed based on Van Allen Probes observations to predict the dynamic plasmapause location The model parameterized by AE or Kp without inclusion of other parameters shows good accuracy to predict the plasmapause location Our neural network model is capable of predicting the global plasmapause location with low RMSE