Towards neural network based models for the ionospheric D-region

Details Towards neural network based models for the ionospheric D-region Towards neural network based models for the ionospheric D-region

Aug 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003esasp.530..369m&link_type=abstract

In: 16th ESA Symposium on European Rocket and Balloon Programmes and Related Research, 2 - 5 June 2003, Sankt Gallen, Switzerlan

Physics

Earth Atmosphere: Physics, Earth Atmosphere: Chemistry

Scientific paper

The ionosphere is known to behave predictably as a function of solar zenith angle, solar activity and season. In the past analytical models have been developed to predict the behaviour of the ionosphere according to these parameters. However, at high latitudes there are other factors that usually dominate over the predictable ones. In this paper, initial results of a novel approach to model the auroral zone ionospheric D-region using the technique of Neural Networks (NNs) are reported. Data from the European Incoherent Scatter facility (EISCAT) combined with rocket measurements are used to provide the large database of reliable D-region data required by NNs. A major advantage of the NN technique is that no a-priori assumptions pertaining to the expected variation are required. In a conventional approach (called the MEDAL model), functions fitted to the available data, are expected to qualitatively describe the anticipated dependence of the electron density on the input parameters. Comparisons are done between the NN approach and the MEDAL model. The relevance of various geophysical parameters is also investigated. These first results show that NNs can be successfully applied to predicting the auroral D-region.

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