A cumulant-based statistical analysis of the magnetospheric response to the solar wind input

Details A cumulant-based statistical analysis of the magnetospheric response to the solar wind input A cumulant-based statistical analysis of the magnetospheric response to the solar wind input

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agufmsm71a0566j&link_type=abstract

American Geophysical Union, Fall Meeting 2002, abstract #SM71A-0566

Computer Science

Performance

2722 Forecasting, 2784 Solar Wind/Magnetosphere Interactions, 3220 Nonlinear Dynamics, 7839 Nonlinear Phenomena

Scientific paper

It is well known that the magnetospheric response to the solar wind is nonlinear. In this work, we characterize the nonlinear dynamics underlying the evolution of the Kp index, given solar wind magnetic field and plasma input, using a nonparametric cumulant-based statistical approach. We examine the underlying dynamics of the system, the temporal statistical dependencies, the degree of nonlinearity, and the rate of information loss. A practical example of the usefulness of this approach is that it is possible to determine time intervals where the system is most predictable i.e. the information is most significant. By determining which periods are most predictable it would be possible to avoid training a neural network on irrelevant noise that would normally spoil the network. This approach also has the advantage that it is reliable even in the case of small data sets and therefore it is possible to avoid the assumption of stationarity, which allows for a measure of predictability even when the underlying system dynamics may change character. Evaluations of several leading Kp prediction models indicate that their performances are sub-optimal during active times. Possible improvements of these models using our results are discussed.

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