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Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes

We investigate the drivers of 40–150 keV hourly electron flux at geostationary orbit (GOES 13) using autoregressive moving average transfer functions (ARMAX) multiple regression models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently. By t...

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Autores principales: Simms, L. E., Ganushkina, N. Yu., van de Kamp, M., Liemohn, M. W., Dubyagin, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539492/
https://www.ncbi.nlm.nih.gov/pubmed/36245709
http://dx.doi.org/10.1029/2022JA030538
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author Simms, L. E.
Ganushkina, N. Yu.
van de Kamp, M.
Liemohn, M. W.
Dubyagin, S.
author_facet Simms, L. E.
Ganushkina, N. Yu.
van de Kamp, M.
Liemohn, M. W.
Dubyagin, S.
author_sort Simms, L. E.
collection PubMed
description We investigate the drivers of 40–150 keV hourly electron flux at geostationary orbit (GOES 13) using autoregressive moving average transfer functions (ARMAX) multiple regression models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently. By taking logs of the variables, we create nonlinear models. While many factors show high correlation with flux in single variable analysis (substorms, ULF waves, solar wind velocity (V), pressure (P), number density (N) and electric field (E ( y )), IMF Bz, Kp, and SymH), ARMAX models show substorms are the dominant influence at 40–75 keV and over 20–12 MLT, with little difference seen between disturbed and quiet periods. The Ey influence is positive post‐midnight, negative post‐noon. Pressure shows a negative influence, strongest at 150 keV. ULF waves are a more modest influence than suggested by single variable correlation. Kp and SymH show little effect when other variables are included. Using path analysis, we calculate the summed direct and indirect influences through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect. N, V, and B ( z ), as indirect drivers, are equally influential. While simple correlation or neural networks can be used for flux prediction, neither can effectively identify drivers. Instead, consideration of physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters gives a clearer picture of which are most influential in this system.
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spelling pubmed-95394922022-10-14 Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes Simms, L. E. Ganushkina, N. Yu. van de Kamp, M. Liemohn, M. W. Dubyagin, S. J Geophys Res Space Phys Research Article We investigate the drivers of 40–150 keV hourly electron flux at geostationary orbit (GOES 13) using autoregressive moving average transfer functions (ARMAX) multiple regression models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently. By taking logs of the variables, we create nonlinear models. While many factors show high correlation with flux in single variable analysis (substorms, ULF waves, solar wind velocity (V), pressure (P), number density (N) and electric field (E ( y )), IMF Bz, Kp, and SymH), ARMAX models show substorms are the dominant influence at 40–75 keV and over 20–12 MLT, with little difference seen between disturbed and quiet periods. The Ey influence is positive post‐midnight, negative post‐noon. Pressure shows a negative influence, strongest at 150 keV. ULF waves are a more modest influence than suggested by single variable correlation. Kp and SymH show little effect when other variables are included. Using path analysis, we calculate the summed direct and indirect influences through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect. N, V, and B ( z ), as indirect drivers, are equally influential. While simple correlation or neural networks can be used for flux prediction, neither can effectively identify drivers. Instead, consideration of physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters gives a clearer picture of which are most influential in this system. John Wiley and Sons Inc. 2022-09-15 2022-09 /pmc/articles/PMC9539492/ /pubmed/36245709 http://dx.doi.org/10.1029/2022JA030538 Text en © 2022. The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Article
Simms, L. E.
Ganushkina, N. Yu.
van de Kamp, M.
Liemohn, M. W.
Dubyagin, S.
Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title_full Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title_fullStr Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title_full_unstemmed Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title_short Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes
title_sort using armax models to determine the drivers of 40–150 kev goes electron fluxes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539492/
https://www.ncbi.nlm.nih.gov/pubmed/36245709
http://dx.doi.org/10.1029/2022JA030538
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