Physics
Scientific paper
Jul 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010jgra..11507307f&link_type=abstract
Journal of Geophysical Research, Volume 115, Issue A7, CiteID A07307
Physics
Ionosphere: Instruments And Techniques, Ionosphere: Electric Fields (2712), Radio Science: Ionospheric Physics (1240, 2400), Radio Science: Instruments And Techniques (1241)
Scientific paper
A spherical cap harmonic analysis (SCHA) technique is introduced for mapping the 2-D high-latitude ionospheric convection pattern based on Super Dual Auroral Radar Network (SuperDARN) velocity measurements. The current method for generating such maps is the FIT technique which generates global-scale maps over the entire convection region. This is accomplished by combining observations with a statistical model to prevent unphysical solutions in areas away from the observation points and by forcing the plasma flow to zero at the low-latitude boundary of the convection zone. Both constraints distort the mapped convection and require a preconception of where the plasma flow lines should close. By focusing on mapping the convection over a region well covered by velocity observations, the SCHA technique is freed of these constraints and more accurately reproduces local convection. We generate large-scale convection maps from SuperDARN data for various interplanetary magnetic field (IMF) conditions during periods of widespread radar coverage to show the patterns are consistent with expectations for various IMF configurations. We validate the SCHA maps by comparing them with the 2-D ion drifts measured by the DMSP satellites and with the 2-D convection vectors obtained by merging SuperDARN measurements at beam crossings. The SCHA technique is shown to perform comparably to the FIT technique over regions of good data coverage. For limited data coverage and over regions of highly variable flow, particularly near the equatorward edge of the mapping region, the SCHA technique provides a better solution for mapping ionospheric convection based on SuperDARN radar observations.
Boteler David H.
Fiori A. D. R.
Haines G. V.
Koustov Alexandre V.
Ruohoniemi Michael J.
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