Podrobnosti záznamu

Název
    A neural network Dst index model driven by input time histories of the solar wind-magnetosphere interaction
Autor
    Bochníček, Josef
    Hejda, Pavel
    Revallo, M.
    Valach, F.
Jazyk
    anglicky
Typ dokumentu
    článek v odborném periodiku
Zdrojový dokument - seriál
    Journal of Atmospheric and Solar-Terrestrial Physics
Svazek/č.
    110-111, April
Strany
    s. 9-14
Rok
    2014
Poznámky
    doplnit granty a zkontrolovat GB
    Rozsah: 6 s.
Předmětová kategorie
    artificial neural network
    Dst index
    geomagnetic storm
    magnetosphere
    solar wind
Klíčové slovo
    Driven
    Dst
    Histories
    Index
    Input
    Interaction
    Model
    Network
    Neural
    Solar
    Time
    Wind-magnetosphere
Abstrakt (anglicky)
   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.
Přispěvatel
    AV ČR Brno, Geofyzikální ústav
Kód přispěvatele
    AV ČR, GFÚ
Zdrojový formát
    U
Datum importu
    23. 10. 2014