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TitlePotential impact of Swarm electric field data on global 2D convection mapping in combination with SuperDARN radar data
AuthorFiori, R A D; Boteler, D H; Knudsen, D; Burchill, J; Koustov, A V; Cousins, E D P; Blais, C
SourceJournal of Atmospheric and Solar-Terrestrial Physics vol. 93, 2013 p. 87-99, https://doi.org/10.1016/j.jastp.2012.11.013
Year2013
Alt SeriesEarth Sciences Sector, Contribution Series 20120182
PublisherElsevier
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing; radar methods; radar imagery; mapping techniques; computer mapping; satellite imagery; SuperDARN
Illustrationshistograms; images
ProgramTargeted Hazard Assessments in Northern Canada, Public Safety Geoscience
AbstractThe Electric Field Instrument (EFI) onboard the Swarm satellites will make continuous measurements of the three-dimensional ion drift in the topside F region providing a convenient data set for mapping the ionospheric convection pattern. In this study, a spherical cap harmonic analysis (SCHA) algorithm has been developed to generate maps of the high-latitude convection pattern in the narrow region surrounding the footprints of the Swarm satellite tracks where the solution will be constrained by measurements. This technique has been tested using input velocity values generated from a statistical model at simulated coordinates of Swarm EFI measurements. To obtain a global context from the Swarm ion drift measurements, the Swarmdata set is mergedwith values of the E x B plasma drift determined using a statistical model at typical locations of measurements for the Super Dual Auroral Radar Network (SuperDARN) radars in the northern hemisphere. It is shown that the addition of Swarm ion drifts to a SuperDARN data set increased the proportion of the calculated convection pattern that is constrained by measurement, by a relative increase of as much as 12% for a period of good SuperDARN coverage and 30% for a period of poor SuperDARN coverage. For a data set comprising two years of past SuperDARN operation and 4 years of future satellite operation, it is shown that a distribution of the relative increase peaks at 12.5%. The magnitude of the improvement depends on the size of the SuperDARN data set, the number of satellites contributing to the Swarm data set, and the extent of the overlap between instruments. Contributions from a Swarm data set also allows for the determination of convection features and properties, such as the location of convection vortices or the value of the cross polar cap potential, that could not be calculated by SuperDARN data alone due to a limited data set.
GEOSCAN ID291790