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Empirically modeled global distribution of magnetospheric chorus amplitude using an artificial neural network

Kim K., Y. Shprits, J. Lee, J. Hwang, (2013), Empirically modeled global distribution of magnetospheric chorus amplitude using an artificial neural network, J. of Geophys. Res. [Space Physics], 118, 6243-6253, doi:10.1002/jgra.50595

Abstract

AbstractAccurate knowledge of the global distribution of magnetospheric chorus waves is essential for radiation belt modeling because it provides a direct link to understanding radiation belt losses and acceleration processes. In this paper, we report on newly developed models of the global distribution of chorus amplitudes based on in situ measurements of interplanetary magnetic field (IMF) and solar wind parameters as well as geomagnetic indices using an artificial neural network technique. We find that solar wind speed and IMF BZ are the most influential parameters that affect the evolution of the magnetospheric chorus. The variations of chorus amplitudes in the outer (L ≥ 7) and in the inner (5 ≤ L < 7) regions, respectively, are well correlated with the variations of solar wind speed and IMF BZ. In addition, the solar wind parameter-based chorus model generally results in a slightly higher correlation between measured and modeled chorus amplitudes than any other models including geomagnetic indices AE, Kp, and Dst. The developed model shows that the chorus is amplified near the prenoon sector during the geomagnetically disturbed conditions. With increasing southward IMF BZ the location of peak chorus amplitude moves from the prenoon sector to the midnight sector, which is due to the enhanced electron injection near midnight. We also present a comparison of diffusive transport simulations for radiation belt electrons interacting with two newly developed chorus models, solar wind parameter-based and geomagnetic index-based chorus models. The comparison between two models shows that the modeling outside the plasmapause can affect the dynamic even inside the plasmasphere because the populations outside the plasmapause can act as seed population to radiation belt particles inside the plasmapause. One weakness of our chorus modeling is that it is trained during the early phase of solar cycle 24 where very few strong storms occurred. Therefore, our model might not be valid in reproducing the chorus activity under extremely disturbed conditions, which should be updated in the future once chorus measurements for such conditions become available.

Authors (sorted by name)

Kim Lee Shprits

Journal / Conference

Journal Of Geophysical Research (Space Physics)

Acknowledgments

This research was supported by the Study of Near‐Earth Effects by CME/HSS Project and basic research funding from KASI. We would like to thank Binbin Ni for calculating the diffusion coefficients, Dmitriy Subbotin for useful discussion in evaluating the VERB code, Wen Li and Drew Turner for their help with THEMIS data. We acknowledge NASA contract NAS5‐02099 and V. Angelopoulos for use of data from the THEMIS Mission. We also would like to thank the two anonymous referees for providing us with constructive comments and suggestions.

Grants

NAS5‐02099

Bibtex

@article{doi:10.1002/jgra.50595,
author = {Kim, Kyung-Chan and Shprits, Yuri and Lee, Jaejin and Hwang, Junga},
title = {Empirically modeled global distribution of magnetospheric chorus amplitude using an artificial neural network},
year = {2013},
journal = {Journal of Geophysical Research: Space Physics},
volume = {118},
number = {10},
pages = {6243-6253},
keywords = {magnetospheric chorus, empirical modeling, artificial neural network, radiation belts, diffusion simulation},
doi = {10.1002/jgra.50595},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/jgra.50595},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgra.50595},
abstract = {AbstractAccurate knowledge of the global distribution of magnetospheric chorus waves is essential for radiation belt modeling because it provides a direct link to understanding radiation belt losses and acceleration processes. In this paper, we report on newly developed models of the global distribution of chorus amplitudes based on in situ measurements of interplanetary magnetic field (IMF) and solar wind parameters as well as geomagnetic indices using an artificial neural network technique. We find that solar wind speed and IMF BZ are the most influential parameters that affect the evolution of the magnetospheric chorus. The variations of chorus amplitudes in the outer (L ≥ 7) and in the inner (5 ≤ L < 7) regions, respectively, are well correlated with the variations of solar wind speed and IMF BZ. In addition, the solar wind parameter-based chorus model generally results in a slightly higher correlation between measured and modeled chorus amplitudes than any other models including geomagnetic indices AE, Kp, and Dst. The developed model shows that the chorus is amplified near the prenoon sector during the geomagnetically disturbed conditions. With increasing southward IMF BZ the location of peak chorus amplitude moves from the prenoon sector to the midnight sector, which is due to the enhanced electron injection near midnight. We also present a comparison of diffusive transport simulations for radiation belt electrons interacting with two newly developed chorus models, solar wind parameter-based and geomagnetic index-based chorus models. The comparison between two models shows that the modeling outside the plasmapause can affect the dynamic even inside the plasmasphere because the populations outside the plasmapause can act as seed population to radiation belt particles inside the plasmapause. One weakness of our chorus modeling is that it is trained during the early phase of solar cycle 24 where very few strong storms occurred. Therefore, our model might not be valid in reproducing the chorus activity under extremely disturbed conditions, which should be updated in the future once chorus measurements for such conditions become available.}
}