Modeling and Prediction of Magnetospheric Dynamics Using Multi-spacecraft Data

Details Modeling and Prediction of Magnetospheric Dynamics Using Multi-spacecraft Data Modeling and Prediction of Magnetospheric Dynamics Using Multi-spacecraft Data

Dec 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001agufmsm51d..12u&link_type=abstract

American Geophysical Union, Fall Meeting 2001, abstract #SM51D-12

Other

2740 Magnetospheric Configuration And Dynamics, 2784 Solar Wind/Magnetosphere Interactions, 2788 Storms And Substorms, 7839 Nonlinear Phenomena, 7863 Turbulence

Scientific paper

Many models of the coupled solar wind-magnetosphere system have been developed using time series data and the models based on local-linear filters have given good predictability of the global or large scale behavior. Most of these models use the solar wind induced electric field as the driver of the magnetosphere and the auroral electrojet as the response. We extend the conventional input-output modeling at the expense of using more input (solar wind) parameters and more direct description of magnetospheric response in terms of the effective lobe field compiled from Geotail and Interball measurements of the local magnetic field and plasma pressure in the magnetotail. The solar wind input is represented by the solar wind induced electric field as well as the dynamical pressure. The magnetotail lobe field trend is found to be controlled mainly by the solar wind dynamical pressure. The residual field obtained by subtracting the trend from the magnetic field data is used to study its correlation with the solar wind driver. It has two distinctive features. First, the lobe field is correlated with the solar wind induced electric field, with a time delay of 10 - 20 min. On the other hand, the unloading dynamics of the lobe field with the time delay of 1 - 2 hr looks rather autonomous, independent of the solar wind induced electric field. The same data set and similar approach are used then to obtain local-linear filters and predict the magnetotail lobe field. The use of autoregression and local fitting the data in embedded space allow to properly model both autonomous and input-output features of the lobe field dynamics. As a result, the predictability of the lobe field is found to be high, as in the case of auroral indices.

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