Podrobnosti záznamu

Název
    Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field
Autor
    Bochníček, Josef
    Hejda, Pavel
    Revallo, M.
    Valach, F.
Jazyk
    anglicky
Typ dokumentu
    článek v odborném periodiku
Zdrojový dokument - seriál
    Advances in Space Research
Svazek/č.
    Roč. 53, č. 4
Strany
    s. 589-598
Rok
    2014
Poznámky
    Projekt: IAA300120608, GA AV ČR, CZ3cav_un_auth*0216774
    Projekt: OC09070, GA MŠk3cav_un_auth*0252836
    Rozsah: 10 s. : P
Předmětová kategorie
    artificial neural network
    ejection of coronal mass
    geomagnetic activity
    interplanetary magnetic field
    X-ray flares
Klíčové slovo
    Activity
    Dominant
    Field
    Forecast
    Geomagnetic
    Interplanetary
    Magnetic
    Networks
    Neural
    Orientation
    Southern
    Strong
Abstrakt (anglicky)
   The paper deals with the relation of the southern orientation of the north-south component Bz of the interplanetary magnetic field to geomagnetic activity (GA) and subsequently a method is suggested of using the found facts to forecast potentially dangerous high GA. We have found that on a day with very high GA hourly averages of Bz with a negative sign occur at least 16 times in typical cases. Since it is very difficult to estimate the orientation of Bz in the immediate vicinity of the Earth one day or even a few days in advance, we have suggested using a neural-network model, which assumes the worse of the possibilities to forecast the danger of high GA - the dominant southern orientation of the interplanetary magnetic field. The input quantities of the proposed model were information about X-ray flares, type II and IV radio bursts as well as information about coronal mass ejections (CME).
   In comparing the GA forecasts with observations, we obtain values of the Hanssen-Kuiper skill score ranging from 0.463 to 0.727, which are usual values for similar forecasts of space weather.
Přispěvatel
    AV ČR Brno, Geofyzikální ústav
Kód přispěvatele
    AV ČR, GFÚ
Zdrojový formát
    U
Datum importu
    23. 10. 2014