• Clicking on the title will open a new window with all details of the bibliographic entry.
  • Clicking on the DOI link will open a new window with the original bibliographic entry from the publisher.
  • Clicking on a single author will show all publications by the selected author.
  • Clicking on a single keyword, will show all publications by the selected keyword.

Found 2 entries in the Bibliography.

Showing entries from 1 through 2


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 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 6537 plasmapause crossing events during the period from 2012 to 2017. To avoid the overfitting and optimize the model generalization, 5493 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 1044 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 indicate that our neural network model is capable of predicting the plasmapause location with the lowest RMSE. Our model can also produce a smooth MLT 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. This article is protected by copyright. All rights reserved.

Guo, DeYu; Fu, Song; Xiang, Zheng; Ni, Binbin; Guo, YingJie; Feng, Minghang; Guo, JianGuang; Hu, Zejun; Gu, Xudong; Zhu, Jianan; Cao, Xing; Wang, Qi;

Published by: Space Weather      Published on: 03/2021

YEAR: 2021     DOI:

Plasmapause; neural network; Van Allen Probes; space weather forecast


Evidence of Nonlinear Interactions Between Magnetospheric Electron Cyclotron Harmonic Waves

Electron cyclotron harmonic (ECH) waves play an important role in the magnetosphere-ionosphere coupling. They are usually considered to be generated by the Bernstein-mode instability with electron loss cone distributions. By analyzing the Van Allen Probes wave data, we present the direct evidence of the nonlinear interactions between ECH waves in the magnetosphere. Substorm-injected electrons excite primary ECH waves in a series of structureless bands between multiples of the electron gyrofrequency. Nonlinear interactions between the primary ECH waves produce secondary waves at sum- and difference-frequencies of the primary waves. Our results suggest that the nonlinear wave-wave interactions can redistribute the primary ECH wave energy over a broader frequency range and hence potentially affect the magnetospheric electrons over a broader range of pitch angles and energies.

Gao, Zhonglei; Zuo, Pingbing; Feng, Xueshang; Wang, Yi; Jiang, Chaowei; Wei, Fengsi;

Published by: Geophysical Research Letters      Published on: 08/2020

YEAR: 2020     DOI:

ECH; wave-wave interaction; nonlinear interaction; frequency spectrum broadening; electron Bernstein mode; generalized Bernstein mode; Van Allen Probes