Quantification of Effect of Solar Storms on TEC over U.S. sector Using Machine Learning
| dc.contributor.author | Sardana, Disha | en |
| dc.contributor.committeechair | Earle, Gregory D. | en |
| dc.contributor.committeemember | Ruohoniemi, J. Michael | en |
| dc.contributor.committeemember | Bailey, Scott M. | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2019-07-09T06:00:23Z | en |
| dc.date.available | 2019-07-09T06:00:23Z | en |
| dc.date.issued | 2018-06-26 | en |
| 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. | en |
| dc.description.abstractgeneral | This study analyzes the impact of geomagnetic storms on the electrical properties of the upper atmosphere at altitudes where satellites routinely fly. The storms are caused by bursts of charged particles from the sun entering the Earth’s atmosphere at high latitudes, leading to phenomena like the aurora. These fluctuations in the atmospheric electrical properties can potentially have serious consequences for the electrical power grid, the communications infrastructure, and various technological systems. Given the risks solar storms can pose, it is important to predict how strong the impact of a given storm is likely to be. The current study applies machine learning techniques to model one particular parameter that relates to the electrified atmosphere over the contiguous US sector. We quantify the strength of the fluctuations as a function of various storm parameters, including onset time and duration. This enables us to autonomously infer which storm parameters have the most significant influence on the resultant atmospheric changes, and compare our results to other recent studies. | en |
| dc.description.degree | MS | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:15766 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/91373 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Space Weather | en |
| dc.subject | Solar Storms | en |
| dc.subject | Data Analysis | en |
| dc.subject | Machine learning | en |
| dc.title | Quantification of Effect of Solar Storms on TEC over U.S. sector Using Machine Learning | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | MS | en |
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