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

Simms, Laura E.; Pilipenko, Viacheslav A.; Engebretson, Mark J.; Reeves, Geoffrey D.; Smith, A.J.; Clilverd, Mark ORCID: https://orcid.org/0000-0002-7388-1529. 2014 Prediction of relativistic electron flux at geostationary orbit following storms: Multiple regression analysis. Journal of Geophysical Research: Space Physics, 119 (9). 7297-7318. https://doi.org/10.1002/2014JA019955

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Abstract/Summary

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), IMF 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 (1992-2002), we obtained hourly averaged electron fluxes for outer radiation belt relativistic electrons (>1.5 MeV) and seed electrons (100 keV) from LANL spacecraft (geosynchronous orbit). For each storm, we found the log10 maximum relativistic electron flux 48-120 hours after the end of the main phase of each storm. Each predictor variable was averaged over the 12 hours before the storm, main phase, and the 48 hours 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 .645 to .795.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/2014JA019955
Programmes:	BAS Programmes > Polar Science for Planet Earth (2009 - ) > Climate
ISSN:	21699380
Additional Keywords:	multiple regression, multi-variable analysis, empirical modeling
Date made live:	01 Sep 2014 10:42 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/508286

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