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| Titel |
Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data |
| VerfasserIn |
L. Cai, S. Y. Ma, Y. L. Zhou |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 28, no. 2 ; Nr. 28, no. 2 (2010-02-02), S.381-393 |
| Datensatznummer |
250016769
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| Publikation (Nr.) |
 copernicus.org/angeo-28-381-2010.pdf |
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| Zusammenfassung |
| Similar to the Dst index, the SYM-H index may also serve as an
indicator of magnetic storm intensity, but having distinct advantage of
higher time-resolution. In this study the NARX neural network has been used
for the first time to predict SYM-H index from solar wind (SW) and IMF
parameters. In total 73 time intervals of great storm events with IMF/SW data
available from ACE satellite during 1998 to 2006 are used to establish the
ANN model. Out of them, 67 are used to train the network and the other 6
samples for test. Additionally, the NARX prediction model is also validated
using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and
2005, as well as for the July 2000 Bastille day storm and November 2001
superstorm using Geotail and OMNI data at 1 AU, respectively. Five
interplanetary parameters of IMF Bz, By and total B components
along with proton density and velocity of solar wind are used as the original
external inputs of the neural network to predict the SYM-H index about one
hour ahead. For the 6 test storms registered by ACE including two
super-storms of min. SYM-H<−200 nT, the correlation coefficient between
observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as
0.95 and 0.98 with average relative variance of 13.2% and 7.4%,
respectively,
for the two super-storms. The prediction for the 7 storms with WIND data is
also satisfactory, showing averaged correlation coefficient about 0.91 and
RMSE of 14.2 nT. The newly developed NARX model shows much better capability
than Elman network for SYM-H prediction, which can partly be attributed to a
key feedback to the input layer from the output neuron with a suitable length
(about 120 min). This feedback means that nearly real information of the ring
current status is effectively directed to take part in the prediction of
SYM-H index by ANN. The proper history length of the output-feedback may
mainly reflect on average the characteristic time of ring current decay which
involves various decay mechanisms with ion lifetimes from tens of minutes to
tens of hours. The Elman network makes feedback from hidden layer to input
only one step, which is of 5 min for SYM-H index in this work and thus
insufficient to catch the characteristic time length. |
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