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2014

Prediction of relativistic electron flux at geostationary orbit following storms: Multiple regression analysis

Many solar wind and magnetosphere parameters correlate with relativistic electron flux following storms. These include relativistic electron flux before the storm; seed electron flux; solar wind velocity and number density (and their variation); interplanetary magnetic field Bz, AE and Kp indices; and ultra low frequency (ULF) and very low frequency (VLF) wave power. However, as all these variables are intercorrelated, we use multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Using 219 storms (1992\textendash2002), we obtained hourly averaged electron fluxes for outer radiation belt relativistic electrons (>1.5 MeV) and seed electrons (100 keV) from Los Alamos National Laboratory spacecraft (geosynchronous orbit). For each storm, we found the log10 maximum relativistic electron flux 48\textendash120 h after the end of the main phase of each storm. Each predictor variable was averaged over the 12 h before the storm, the main phase, and the 48 h following minimum Dst. High levels of flux following storms are best modeled by a set of variables. In decreasing influence, ULF, seed electron flux, Vsw and its variation, and after-storm Bz were the most significant explanatory variables. Kp can be added to the model, but it adds no further explanatory power. Although we included ground-based VLF power from Halley, Antarctica, it shows little predictive ability. We produced predictive models using the coefficients from the regression models and assessed their effectiveness in predicting novel observations. The correlation between observed values and those predicted by these empirical models ranged from 0.645 to 0.795.

Simms, Laura; Pilipenko, Viacheslav; Engebretson, Mark; Reeves, Geoffrey; Smith, A.; Clilverd, Mark;

Published by: Journal of Geophysical Research: Space Physics      Published on: 09/2014

YEAR: 2014     DOI: 10.1002/jgra.v119.910.1002/2014JA019955

empirical modeling; multiple regression; multivariable analysis



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