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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Annales geophysicae 12 (1994), S. 19-24 
    ISSN: 0992-7689
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract An artificial feed-forward neural network with one hidden layer and error back-propagation learning is used to predict the geomagnetic activity index (Dst) one hour in advance. The Bz-component and 〈sigma〉Bz, the density, and the velocity of the solar wind are used as input to the network. The network is trained on data covering a total of 8700 h, extracted from the 25-year period from 1963 to 1987, taken from the NSSDC data base. The performance of the network is examined with test data, not included in the training set, which covers 386 h and includes four different storms. Whilst the network predicts the initial and main phase well, the recovery phase is not modelled correctly, implying that a single hidden layer error back-propagation network is not enough, if the measured Dst is not available instantaneously. The performance of the network is independent of whether the raw parameters are used, or the electric field and square root of the dynamical pressure.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Annales geophysicae 14 (1996), S. 679-686 
    ISSN: 0992-7689
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: 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.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Annales geophysicae 17 (1999), S. 1268-1275 
    ISSN: 0992-7689
    Keywords: Magnetospheric physics (solar wind-magnetosphere interactions; storms and substorms)
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: 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.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Planetary and Space Science 32 (1984), S. 1541-1545 
    ISSN: 0032-0633
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Geosciences , Physics
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Planetary and Space Science 40 (1992), S. 457-464 
    ISSN: 0032-0633
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Geosciences , Physics
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Planetary and Space Science 29 (1981), S. 167-170 
    ISSN: 0032-0633
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Geosciences , Physics
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Solar physics 88 (1983), S. 377-382 
    ISSN: 1573-093X
    Source: Springer Online Journal Archives 1860-2000
    Topics: Physics
    Notes: Abstract Interplanetary magnetic field data compiled by King (1977, 1979) are used to obtain a list of high velocity solar wind streams for the period January 1975 through June 1978. The catalogue is a continuation of a previous list which covered 1964–75. The total number of high-speed solar wind streams listed in the catalogues is 455.
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Surveys in geophysics 17 (1996), S. 561-573 
    ISSN: 1573-0956
    Keywords: Large-scale solar magnetic fields ; coronal phenomena ; geomagnetic storms ; predictions ; artificial neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract This review deals with how the changes of the large-scale solar magnetic fields are related to the occurrence of solar phenomena, which are associated with geomagnetic storms. The review also describes how artificial neural networks have been used to forecast geomagnetic storms either from daily solar input data or from hourly solar wind data. With solar data as input predictions 1–3 days or a month in advance are possible, while using solar wind data as input predictions about an hour in advance are possible. The predictions one hour ahead of the geomagnetic storm indexD st from only solar wind input data have reached such high accuracy, that they are of practical use in combination with real-time solar wind observations at L1. However, the predictions days and a month ahead need to be much improved in order to be of real practical use.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Solar physics 120 (1989), S. 145-152 
    ISSN: 1573-093X
    Source: Springer Online Journal Archives 1860-2000
    Topics: Physics
    Notes: Abstract Interplanetary magnetic field data obtained from near-Earth spacecraft are used to compile a catalogue of high-velocity solar wind streams for the period June 1978 through October 1982 (Bartels rotations 1980–2039). The compilation of high-velocity streams is a continuation of two previous lists which covered the periods 1964–1975 and 1975–1978, respectively. The total number of high-speed solar wind streams listed in the catalogues is 637 of which 520 are corotating streams and 117 are classified as flare-associated streams.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Solar physics 74 (1981), S. 197-206 
    ISSN: 1573-093X
    Source: Springer Online Journal Archives 1860-2000
    Topics: Physics
    Notes: Abstract A catalogue of 346 well defined high-speed plasma streams detected in solar wind observations 1964–75 is presented. The data base for the study is the compilation of interplanetary plasma/magnetic field data prepared by J. King. It is believed that the catalogue may be found useful for studies of various solar-interplanetary and solar-terrestrial phenomena.
    Type of Medium: Electronic Resource
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