Correlation analysis between the occurrence of ionospheric scintillation at the magnetic equator and at the southern peak of the Equatorial Ionization Anomaly. Issue 6 (13th June 2014)

• Record Type: Journal Article
• Authors: de Lima, G. R. T.; Stephany, S.; de Paula, E. R.; Batista, I. S.; Abdu, M. A.; Rezende, L. F. C.; Aquino, M. G. S.; Dutra, A. P. S.
• Is Part Of: Space weather. Volume 12:Issue 6(2014:Jun.)
• Page Start: 406
• Page End: 416
• Publication Date: 2014-06-13
• Subjects: Space environment -- Periodicals; 551.509992
• DOI: 10.1002/2014SW001041
• Languages: English
• ISSNs: 1542-7390

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Ionospheric scintillation refers to amplitude and phase fluctuations in radio signals due to electron density irregularities associated to structures named ionospheric plasma bubbles. The phenomenon is more pronounced around the magnetic equator where, after sunset, plasma bubbles of varying sizes and density depletions are generated by plasma instability mechanisms. The bubble depletions are aligned along Earth's magnetic field lines, and they develop vertically upward over the magnetic equator so that their extremities extend in latitude to north and south of the dip equator. Over Brazil, developing bubbles can extend to the southern peak of the Equatorial Ionization Anomaly, where high levels of ionospheric scintillation are common. Scintillation may seriously affect satellite navigation systems, such as the Global Navigation Satellite Systems. However, its effects may be mitigated by using a predictive model derived from a collection of extended databases on scintillation and its associated variables. This work proposes the use of a classification and regression decision tree to perform a study on the correlation between the occurrence of scintillation at the magnetic equator and that at the southern peak of the equatorial anomaly. Due to limited size of the original database, a novel resampling heuristic was applied to generate new training instances from the original ones in order to improve the accuracy of the decision tree. The correlation analysis presented in this work may serve as a starting point for the eventual development of a predictive model suitable for operational use.</p> </abstract>