Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1777857 | Journal of Atmospheric and Solar-Terrestrial Physics | 2008 | 8 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Geophysics
Authors
Fabrício P. Härter, Haroldo F. de Campos Velho, Erico L. Rempel, Abraham C.-L. Chian,