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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 26, issue 2
Ann. Geophys., 26, 305-314, 2008
https://doi.org/10.5194/angeo-26-305-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: 3rd European Space Weather Week (ESWW)

Ann. Geophys., 26, 305-314, 2008
https://doi.org/10.5194/angeo-26-305-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Feb 2008

26 Feb 2008

A statistical approach for identifying the ionospheric footprint of magnetospheric boundaries from SuperDARN observations

G. Lointier, T. Dudok de Wit, C. Hanuise, X. Vallières, and J.-P. Villain G. Lointier et al.
  • Laboratoire de Pysique et Chimie de l'Environnement, UMR 6115 CNRS/University of Orléans, 3A avenue de la recherche Scientifique, 45071 Orléans cedex 2, France

Abstract. Identifying and tracking the projection of magnetospheric regions on the high-latitude ionosphere is of primary importance for studying the Solar Wind-Magnetosphere-Ionosphere system and for space weather applications. By its unique spatial coverage and temporal resolution, the Super Dual Auroral Radar Network (SuperDARN) provides key parameters, such as the Doppler spectral width, which allows the monitoring of the ionospheric footprint of some magnetospheric boundaries in near real-time. In this study, we present the first results of a statistical approach for monitoring these magnetospheric boundaries. The singular value decomposition is used as a data reduction tool to describe the backscattered echoes with a small set of parameters. One of these is strongly correlated with the Doppler spectral width, and can thus be used as a proxy for it. Based on this, we propose a Bayesian classifier for identifying the spectral width boundary, which is classically associated with the Polar Cap boundary. The results are in good agreement with previous studies. Two advantages of the method are: the possibility to apply it in near real-time, and its capacity to select the appropriate threshold level for the boundary detection.

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