Neural networks in auroral data assimilation

Data assimilation is an essential step for improving space weather forecasting by means of a weighted combination between observational data and data from a mathematical model. In the present work data assimilation methods based on Kalman filter (KF) and artificial neural networks are applied to a t...

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Bibliographic Details
Published in:Journal of atmospheric and solar-terrestrial physics 2008-07, Vol.70 (10), p.1243-1250
Main Authors: Härter, Fabrício P., de Campos Velho, Haroldo F., Rempel, Erico L., Chian, Abraham C.-L.
Format: Article
Language:English
Subjects:
Auroral radio emissions
Chaos
Data assimilation
Earth, ocean, space
Exact sciences and technology
External geophysics
Kalman filter
Neural networks
Nonlinear dynamics
Physics of the ionosphere
Physics of the magnetosphere
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Summary:Data assimilation is an essential step for improving space weather forecasting by means of a weighted combination between observational data and data from a mathematical model. In the present work data assimilation methods based on Kalman filter (KF) and artificial neural networks are applied to a three-wave model of auroral radio emissions. A novel data assimilation method is presented, whereby a multilayer perceptron neural network is trained to emulate a KF for data assimilation by using cross-validation. The results obtained render support for the use of neural networks as an assimilation technique for space weather prediction.
ISSN:1364-6826
1879-1824
DOI:10.1016/j.jastp.2008.03.018