Nonlinear forecasts of foF2: variation of model predictive accuracy over time

Reference:

Chan, A. H. Y. and Cannon, P. S., 2002. Nonlinear forecasts of foF2: variation of model predictive accuracy over time. Annales Geophysicae, 20 (7), pp. 1031-1038.

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Abstract

A nonlinear technique employing radial basis function neural networks (RBF-NNs) has been applied to the short-term forecasting of the ionospheric F2-layer critical frequency, foF2. The accuracy of the model forecasts at a northern mid-latitude location over long periods is assessed, and is found to degrade with time. The results highlight the need for the retraining and re-optimization of neural network models on a regular basis to cope with changes in the statistical properties of geophysical data sets. Periodic retraining and re-optimization of the models resulted in a reduction of the model predictive error by similar to0.1 MHz per six months. A detailed examination of error metrics is also presented to illustrate the difficulties encountered in evaluating the performance of various prediction/forecasting techniques.

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