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An estimation of the difference in TEC predic- tion accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural net- works. Hourly-daily Faraday-rotation derived TEC measure- ments from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ±10% is considered, the popula- tion percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis- like effect between these two ionospheric characteristics.

Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models