Nonlinear forecasts of foF2: Variation model predictive accuracy over time

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 ∼0.1 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.