Neural networks in auroral data assimilation

Details Neural networks in auroral data assimilation Neural networks in auroral data assimilation

Jul 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008jastp..70.1243h&link_type=abstract

Journal of Atmospheric and Solar-Terrestrial Physics, vol. 70, issue 10, pp. 1243-1250

Physics

Auroral Radio Emissions, Nonlinear Dynamics, Chaos, Data Assimilation, Kalman Filter, Neural Networks

Scientific paper

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.

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