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Title: Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

Abstract

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlationsmore » between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current (Dst), AE, and wave activity.« less

Authors:
 [1];  [1];  [2]; ORCiD logo [3];  [4]
  1. Augsburg College, Minneapolis, MN (United States)
  2. Institute of the Physics of the Earth, Moscow (Russia)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. British Antarctic Survey, Cambridge (United Kingdom)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Aeronautic and Space Administration (NASA); USDOE
OSTI Identifier:
1402660
Report Number(s):
LA-UR-17-28047
Journal ID: ISSN 2169-9380; TRN: US1703023
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Space Physics
Additional Journal Information:
Journal Volume: 121; Journal Issue: 4; Journal ID: ISSN 2169-9380
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Heliospheric and Magnetospheric Physics; multiple regression; multivariable analysis; empirical modeling; relativistic electron flux prediction at geosynchronous orbit

Citation Formats

Simms, Laura E., Engebretson, Mark J., Pilipenko, Viacheslav, Reeves, Geoffrey D., and Clilverd, Mark. Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis. United States: N. p., 2016. Web. doi:10.1002/2016JA022414.
Simms, Laura E., Engebretson, Mark J., Pilipenko, Viacheslav, Reeves, Geoffrey D., & Clilverd, Mark. Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis. United States. doi:10.1002/2016JA022414.
Simms, Laura E., Engebretson, Mark J., Pilipenko, Viacheslav, Reeves, Geoffrey D., and Clilverd, Mark. Thu . "Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis". United States. doi:10.1002/2016JA022414. https://www.osti.gov/servlets/purl/1402660.
@article{osti_1402660,
title = {Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis},
author = {Simms, Laura E. and Engebretson, Mark J. and Pilipenko, Viacheslav and Reeves, Geoffrey D. and Clilverd, Mark},
abstractNote = {The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current (Dst), AE, and wave activity.},
doi = {10.1002/2016JA022414},
journal = {Journal of Geophysical Research. Space Physics},
number = 4,
volume = 121,
place = {United States},
year = {2016},
month = {4}
}

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