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Title: Modeling the Magnetopause Shadowing Loss During the June 2015 Dropout Event

Abstract

Fast dropout of relativistic and ultrarelativistic electrons at both high and low L* regions were observed during the intense coronal mass ejection driven storm in June 2015. An improved radial diffusion model, using an event-specific Last Closed Drift Shell (LCDS) and newly-available radial diffusion coefficients (DDLL), is implemented to simulate the magnetopause shadowing loss of electrons. The model captures the fast shadowing loss of electrons well at high L* regions after both interplanetary shocks, and reproduces the initial adiabatic loss of the high-energy storage ring at low L* regions after the second strong shock. In this paper, we show for the first time that using the event-specific and K-dependent LCDS and improved DDLL is critical to reproduce the observed dropout features, including the timing, location, and the butterfly electron pitch angle distribution. Future inclusion of the EMIC wave scattering process is needed to model the observed further depletion of the storage ring.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3]
  1. West Virginia Univ., Morgantown, WV (United States)
  2. West Virginia Univ., Morgantown, WV (United States); Univ. of Colorado, Boulder, CO (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); National Aeronautics and Space Administration (NASA)
OSTI Identifier:
1659192
Alternate Identifier(s):
OSTI ID: 1559103
Report Number(s):
LA-UR-19-26679
Journal ID: ISSN 0094-8276
Grant/Contract Number:  
89233218CNA000001; 20150127ER; AGS 1752736; NNX15AW06G; 80NSSC18K1284; 80NSSC19K0908
Resource Type:
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Volume: 46; Journal Issue: 16; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Tu, Weichao, Xiang, Zheng, and Morley, Steven Karl. Modeling the Magnetopause Shadowing Loss During the June 2015 Dropout Event. United States: N. p., 2019. Web. https://doi.org/10.1029/2019gl084419.
Tu, Weichao, Xiang, Zheng, & Morley, Steven Karl. Modeling the Magnetopause Shadowing Loss During the June 2015 Dropout Event. United States. https://doi.org/10.1029/2019gl084419
Tu, Weichao, Xiang, Zheng, and Morley, Steven Karl. Wed . "Modeling the Magnetopause Shadowing Loss During the June 2015 Dropout Event". United States. https://doi.org/10.1029/2019gl084419. https://www.osti.gov/servlets/purl/1659192.
@article{osti_1659192,
title = {Modeling the Magnetopause Shadowing Loss During the June 2015 Dropout Event},
author = {Tu, Weichao and Xiang, Zheng and Morley, Steven Karl},
abstractNote = {Fast dropout of relativistic and ultrarelativistic electrons at both high and low L* regions were observed during the intense coronal mass ejection driven storm in June 2015. An improved radial diffusion model, using an event-specific Last Closed Drift Shell (LCDS) and newly-available radial diffusion coefficients (DDLL), is implemented to simulate the magnetopause shadowing loss of electrons. The model captures the fast shadowing loss of electrons well at high L* regions after both interplanetary shocks, and reproduces the initial adiabatic loss of the high-energy storage ring at low L* regions after the second strong shock. In this paper, we show for the first time that using the event-specific and K-dependent LCDS and improved DDLL is critical to reproduce the observed dropout features, including the timing, location, and the butterfly electron pitch angle distribution. Future inclusion of the EMIC wave scattering process is needed to model the observed further depletion of the storage ring.},
doi = {10.1029/2019gl084419},
journal = {Geophysical Research Letters},
number = 16,
volume = 46,
place = {United States},
year = {2019},
month = {8}
}

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