Identification of Non-Linear Space Weather Models of the Van Allen Radiation Belts Using Volterra Networks

Details Identification of Non-Linear Space Weather Models of the Van Allen Radiation Belts Using Volterra Networks Identification of Non-Linear Space Weather Models of the Van Allen Radiation Belts Using Volterra Networks

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aspc..424...92t&link_type=abstract

Proceedings of the 9th International Conference of the Hellenic Astronomical Society, proceedings of a conference held 20-24 Sep

Astronomy and Astrophysics

Astronomy

Scientific paper

Many efforts have been made to develop general dynamical models of the Van Allen radiation belts based on data alone. Early linear prediction filter studies focused on the response of daily-averaged relativistic electrons at geostationary altitudes Nagai 1988, Baker et al. 1990). Vassiliadis et al (2005) extended this technique spatially by incorporating SAMPEX electron flux data into linear prediction filters for a broad range of L-shells from 1.1 to 10.0 RE. Nonlinear state space models (Rigler & Baker 2008) have provided useful initial results on the timescales involved in modeling the impulse-response of the radiation belts. Here, we show how NARMAX models, in conjunction with nonlinear time-delay FIR neural networks (Volterra networks) hold great promise for the development of accurate and fully data-derived space weather specification and forecast tools.

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