Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks

1996 ◽  
Vol 23 (4) ◽  
pp. 319-322 ◽  
Author(s):  
Jian-Guo Wu ◽  
Henrik Lundstedt
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Recurrent Neural Networks ◽  
Geomagnetic Storms ◽  
Wind Data ◽  
Elman Recurrent Neural Networks

scholarly journals Predicting geomagnetic storms from solar-wind data using time-delay neural networks

1996 ◽  
Vol 14 (7) ◽  
pp. 679-686 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt ◽  
P. Wintoft
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Time Delay ◽  
Input Data ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Time History ◽  
Recovery Phase ◽  
Wind Data ◽  
Data Sequence

Abstract. We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include solar-wind density n, velocity V and the southward component Bz of the interplanetary magnetic field. Dst is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed non-linear relations. In this study we especially consider the influence of varying the temporal size of the input-data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the input-data sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss processes, and is very much dependent on the ring-current history, and thus also the solar-wind history. With due consideration of the time history when optimizing the networks, we can reproduce 84% of the Dst variance.

scholarly journals Ring current influence on auroral electrojet predictions

1999 ◽  
Vol 17 (10) ◽  
pp. 1268-1275 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt
Keyword(s):  
Solar Wind ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Wind Data ◽  
Common Source ◽  
Neural Net ◽  
Operational Forecasting ◽  
Storms And Substorms ◽  
Highly Correlated ◽  
Strong Control

Abstract. Geomagnetic storms and substorms develop under strong control of the solar wind. This is demonstrated by the fact that the geomagnetic activity indices Dst and AE can be predicted from the solar wind alone. A consequence of the strong control by a common source is that substorm and storm indices tend to be highly correlated. However, a part of this correlation is likely to be an effect of internal magnetospheric processes, such as a ring-current modulation of the solar wind-AE relation. The present work extends previous studies of nonlinear AE predictions from the solar wind. It is examined whether the AE predictions are modulated by the Dst index.This is accomplished by comparing neural network predictions from Dst and the solar wind, with predictions from the solar wind alone. Two conclusions are reached: (1) with an optimal set of solar-wind data available, the AE predictions are not markedly improved by the Dst input, but (2) the AE predictions are improved by Dst if less than, or other than, the optimum solar-wind data are available to the net. It appears that the solar wind-AE relation described by an optimized neural net is not significantly modified by the magnetosphere's Dst state. When the solar wind alone is used to predict AE, the correlation between predicted and observed AE is 0.86, while the prediction residual is nearly uncorrelated to Dst. Further, the finding that Dst can partly compensate for missing information on the solar wind, is of potential importance in operational forecasting where gaps in the stream of real time solar-wind data are a common occurrence.Key words. Magnetospheric physics (solar wind · magnetosphere interactions; storms and substorms)

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