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Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field
Authors:Fridrich Valach  Josef Bochní?ek  Pavel Hejda  Miloš Revallo
Institution:1. Geomagnetic Observatory, Geophysical Institute, Slovak Academy of Sciences, Komárňanská 108, 947 01 Hurbanovo, Slovakia;2. Institute of Geophysics, Academy of Sciences of the Czech Republic, Bo?ní II/1401, 14131 Prague, Czech Republic;3. Geophysical Institute, Slovak Academy of Sciences, Dúbravská cesta 9, 845 28 Bratislava, Slovakia
Abstract:The paper deals with the relation of the southern orientation of the north–south component BzBz 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 BzBz with a negative sign occur at least 16 times in typical cases. Since it is very difficult to estimate the orientation of BzBz 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. The proposed model provides forecasts of potentially dangerous high geomagnetic activity should the interplanetary CME (ICME), the originator of geomagnetic storms, hit the Earth under the most unfavorable configuration of cosmic magnetic fields. We cannot know in advance whether the unfavorable configuration is going to occur or not; we just know that it will occur with the probability of 31%.
Keywords:Geomagnetic activity  Interplanetary magnetic field  Artificial neural network  Ejection of coronal mass  X-ray flares
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