Back to results

Virginia Tech

Quantification of Effect of Solar Storms on TEC over U.S. sector Using Machine Learning

Abstract

dc:description.abstract

A study of large solar storms in the equinox periods of solar cycles 23 and 24 is presented to quantify their effects on the total electron content (TEC) in the ionosphere. We study the dependence of TEC over the contiguous US on various storm parameters, including the onset time of the storm, the duration of the storm, its intensity, and the rate of change of the ring current response. These parameters are inferred autonomously and compared to TEC values obtained from the CORS network of GPS stations. To quantify the effects we examine the difference between the storm-time TEC value and an average from 5 quiet days during the same month. These values are studied over a grid with 1 deg x 1 deg spatial resolution in latitude and longitude over the US sector. Correlations between storm parameters and the quantified delta TEC values are studied using machine learning techniques to identify the most important controlling variables. The weights inferred by the algorithm for each input variable show their importance to the resultant TEC change. The results of this work are compared to recent TEC studies to investigate the effects of large storms on the distribution of ionospheric density over large spatial and temporal scales.

Degree

thesis:*
Name thesis:degree_name
MS
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Electrical Engineering
Department dc:contributor.department
Electrical Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Sardana, Disha
Chair dc:contributor.committeechair
  • Earle, Gregory D.
Committee members dc:contributor.committeemember
  • Ruohoniemi, J. Michael
  • Bailey, Scott M.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:15766
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/91373

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Sardana, Disha. Quantification of Effect of Solar Storms on TEC over U.S. sector Using Machine Learning. masters thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/91373