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A neural network-based local model for prediction of geomagnetic disturbances

Gleisner, Hans and Lundstedt, Henrik LU (2001) In Journal of Geophysical Research 106(A5). p.8425-8434
Abstract
This study shows how locally observed geomagnetic disturbances can bepredicted from solar wind data with artificial neural network (ANN)techniques. After subtraction of a secularly varying base level, thehorizontal components X<SUB>Sq</SUB> and Y<SUB>Sq</SUB> of the quiettime daily variations are modeled with radial basis function networkstaking into account seasonal and solar activity modulations. Theremaining horizontal disturbance components DeltaX and DeltaY aremodeled with gated time delay networks taking local time and solar winddata as input. The observed geomagnetic field is not used as input tothe networks, which thus constitute explicit nonlinear mappings from thesolar wind to the locally observed... (More)
This study shows how locally observed geomagnetic disturbances can bepredicted from solar wind data with artificial neural network (ANN)techniques. After subtraction of a secularly varying base level, thehorizontal components X<SUB>Sq</SUB> and Y<SUB>Sq</SUB> of the quiettime daily variations are modeled with radial basis function networkstaking into account seasonal and solar activity modulations. Theremaining horizontal disturbance components DeltaX and DeltaY aremodeled with gated time delay networks taking local time and solar winddata as input. The observed geomagnetic field is not used as input tothe networks, which thus constitute explicit nonlinear mappings from thesolar wind to the locally observed geomagnetic disturbances. The ANNsare applied to data from Sodankylä Geomagnetic Observatory locatednear the peak of the auroral zone. It is shown that 73% of the DeltaXvariance, but only 34% of the DeltaY variance, is predicted from asequence of solar wind data. The corresponding results for prediction ofall transient variations X<SUB>Sq</SUB>+DeltaX andY<SUB>Sq</SUB>+DeltaY are 74% and 51%, respectively. The local timemodulations of the prediction accuracies are shown, and the qualitativeagreement between observed and predicted values are discussed. If drivenby real-time data measured upstream in the solar wind, the ANNs heredeveloped can be used for short-term forecasting of the locally observedgeomagnetic activity. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Ionosphere: Current systems, Ionosphere: Modeling and forecasting, Magnetospheric Physics: Solar wind/magnetosphere interactions, Mathematical Geophysics: Modeling
in
Journal of Geophysical Research
volume
106
issue
A5
pages
8425 - 8434
publisher
American Geophysical Union
ISSN
2156-2202
DOI
10.1029/2000JA900142
language
English
LU publication?
yes
id
43e3fb5a-bc7c-4e01-b425-0b9a8bbbfec4 (old id 130194)
date added to LUP
2007-07-13 10:07:15
date last changed
2016-04-16 09:35:44
@misc{43e3fb5a-bc7c-4e01-b425-0b9a8bbbfec4,
  abstract     = {This study shows how locally observed geomagnetic disturbances can bepredicted from solar wind data with artificial neural network (ANN)techniques. After subtraction of a secularly varying base level, thehorizontal components X&lt;SUB&gt;Sq&lt;/SUB&gt; and Y&lt;SUB&gt;Sq&lt;/SUB&gt; of the quiettime daily variations are modeled with radial basis function networkstaking into account seasonal and solar activity modulations. Theremaining horizontal disturbance components DeltaX and DeltaY aremodeled with gated time delay networks taking local time and solar winddata as input. The observed geomagnetic field is not used as input tothe networks, which thus constitute explicit nonlinear mappings from thesolar wind to the locally observed geomagnetic disturbances. The ANNsare applied to data from Sodankylä Geomagnetic Observatory locatednear the peak of the auroral zone. It is shown that 73% of the DeltaXvariance, but only 34% of the DeltaY variance, is predicted from asequence of solar wind data. The corresponding results for prediction ofall transient variations X&lt;SUB&gt;Sq&lt;/SUB&gt;+DeltaX andY&lt;SUB&gt;Sq&lt;/SUB&gt;+DeltaY are 74% and 51%, respectively. The local timemodulations of the prediction accuracies are shown, and the qualitativeagreement between observed and predicted values are discussed. If drivenby real-time data measured upstream in the solar wind, the ANNs heredeveloped can be used for short-term forecasting of the locally observedgeomagnetic activity.},
  author       = {Gleisner, Hans and Lundstedt, Henrik},
  issn         = {2156-2202},
  keyword      = {Ionosphere: Current systems,Ionosphere: Modeling and forecasting,Magnetospheric Physics: Solar wind/magnetosphere interactions,Mathematical Geophysics: Modeling},
  language     = {eng},
  number       = {A5},
  pages        = {8425--8434},
  publisher    = {ARRAY(0x8197b58)},
  series       = {Journal of Geophysical Research},
  title        = {A neural network-based local model for prediction of geomagnetic disturbances},
  url          = {http://dx.doi.org/10.1029/2000JA900142},
  volume       = {106},
  year         = {2001},
}