A neural network Dst index model driven by input time histories of the solar wind–magnetosphere interaction

0433347 - GFÚ 2015 RIV GB eng J - Journal Article
Revallo, M. - Valach, F. - Hejda, Pavel - Bochníček, Josef
A neural network Dst index model driven by input time histories of the solar wind–magnetosphere interaction.
Journal of Atmospheric and Solar-Terrestrial Physics. 110-111, April (2014), s. 9-14. ISSN 1364-6826. E-ISSN 1879-1824
R&D Projects: GA MŠMT OC09070
Institutional support: RVO:67985530
Keywords : solar wind * magnetosphere * geomagnetic storm * Dst index * artificial neural network
Subject RIV: DE - Earth Magnetism, Geodesy, Geography
Impact factor: 1.474, year: 2014

A model to forecast 1-hour lead Dst index is proposed. Our approach is based on artificial neural networks (ANN) combined with an analytical model of the solar wind-magnetosphere interaction. Previously, the hourly solar wind parameters have been considered in the analytical model, all of them provided by registration of the ACE satellite. They were the solar wind magnetic field component B-z, velocity V, particle density n and temperature T. The solar wind parameters have been used to compute analytically the discontinuity in magnetic field across the magnetopause, denoted as [B-t]. This quantity has been shown to be important in connection with ground magnetic field variations. The method was published, in which the weighted sum of a sequence of [B-t] was proposed to produce the value of Dst index. The maximum term in the sum, possessing the maximum weight, is the one denoting the contribution of the current state of the near-Earth solar wind. The role of the older states is less important - the weights exponentially decay.
Permanent Link: http://hdl.handle.net/11104/0237562