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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 28, issue 2
Ann. Geophys., 28, 381–393, 2010
https://doi.org/10.5194/angeo-28-381-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 28, 381–393, 2010
https://doi.org/10.5194/angeo-28-381-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Feb 2010

02 Feb 2010

Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

L. Cai, S. Y. Ma, and Y. L. Zhou L. Cai et al.
  • Dept. of Space Physics, College of Electronic Information, Wuhan University, Wuhan 430079, China

Abstract. 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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