GEOMAGNETIC METHOD FOR AUTOMATIC DIAGNOSTICS OF AURORAL OVAL BOUNDARIES IN TWO HEMISPHERES OF EARTH

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GEOMAGNETIC METHOD FOR AUTOMATIC DIAGNOSTICS OF AURORAL OVAL BOUNDARIES IN TWO HEMISPHERES OF EARTH

Journal Title: Solar-Terrestrial Physics - Year 2021, Vol 7, Issue 2

Abstract

The ground-based automatic method for determining auroral oval (AO) boundaries developed by the authors [Lunyushkin, Penskikh, 2019] has been modified and expanded to the Southern Hemisphere. Input data of the method contains large-scale distributions of the equivalent current function and field-aligned current density calculated in the polar ionospheres of two hemispheres in a uniform ionospheric conductance approximation based on the magnetogram inversion technique and the geomagnetic database of the world network of stations of the SuperMAG project. The software implementation of the method processes large volumes of time series of input data and produces coordinates of the main boundaries of AO in both hemispheres: the boundaries of the ionospheric convection reversal, the AO polar and equatorial boundaries, the lines of maximum density of field-aligned currents and auroral electrojets. The automatic method reduces the processing time for a given amount of data by 2–3 orders of magnitude (up to minutes and hours) compared to the manual method, which requires weeks and months of laborious operator work on the same task, while both methods are comparable in accuracy. The automatic geomagnetic method has been tested for diagnostics of AO boundaries during the isolated substorm of August 27, 2001, for which the expected synchronous dynamics of polar caps in two hemispheres has been confirmed. We also show the AO boundaries identified are in qualitative agreement with simultaneous AO images from the IMAGE satellite, as well as with the results of the OVATION and APM models; the boundary of ionospheric convection reversal, determined by the geomagnetic method in two hemispheres, is consistent with the maps of the electric potential of the ionosphere according to the SuperDARN-RG96 model.

Authors and Affiliations

Penskikh Yu. V. , Lunyushkin S. B. , Kapustin V. E.

Keywords

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EP ID EP697554
DOI 10.12737/stp-72202106
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