Spatiotemporal dynamics of the magnetosphere during geospace storms: Mutual information analysis

Details Spatiotemporal dynamics of the magnetosphere during geospace storms: Mutual information analysis Spatiotemporal dynamics of the magnetosphere during geospace storms: Mutual information analysis

May 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008jgra..11305217c&link_type=abstract

Journal of Geophysical Research, Volume 113, Issue A5, CiteID A05217

Physics

1

Magnetospheric Physics: Magnetic Storms And Substorms (7954), Magnetospheric Physics: Magnetospheric Configuration And Dynamics, Magnetospheric Physics: Solar Wind/Magnetosphere Interactions

Scientific paper

The magnetospheric response to strong driving by the solar wind has spatial variations, and corresponding data are essential for the understanding of the spatiotemporal dynamics. A database of ISEE3 and IMP8 spacecraft and ground-based magnetometer data from high-latitude stations (Kamide et al., 1998) is used to study the magnetospheric response to solar wind variables during geospace storms. This high-resolution database for the 6-month period January-June 1979 is used to compute the mutual information functions representing the correlations inherent in the system. A key feature of the mutual information function is its ability to yield the linear as well as nonlinear correlations and such functions are needed to characterize the inherently nonlinear magnetospheric dynamics. The minimum window length required for computing these functions is about 6 h, and this choice also avoids the diurnal variability. Another window length of 24 h is used to analyze the dynamics on longer timescales. The spreads in the average mutual information of the spatially distributed magnetometers show strong correlations with the convective electric field and dynamic pressure of the solar wind. This correlation is not seen in the linear correlation functions. The mutual information functions show an expansion of the disturbed region starting from the midnight region. From the space weather perspective these functions provide the correlations among the different regions, which are critical elements enabling forecasts of regional, rather than global, conditions.

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