Premium
Prediction of relativistic electron flux at geostationary orbit following storms: Multiple regression analysis
Author(s) -
Simms Laura E.,
Pilipenko Viacheslav,
Engebretson Mark J.,
Reeves Geoffrey D.,
Smith A. J.,
Clilverd Mark
Publication year - 2014
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/2014ja019955
Subject(s) - physics , flux (metallurgy) , magnetosphere , storm , solar wind , van allen radiation belt , van allen probes , geosynchronous orbit , electron , atmospheric sciences , geomagnetic storm , computational physics , meteorology , plasma , satellite , astronomy , nuclear physics , chemistry , organic chemistry
Abstract 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 B z , 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 (1992–2002), 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 log 10 maximum relativistic electron flux 48–120 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 B z 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.