No abstract

No abstract

No abstract

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements 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 population 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 hysteresislike effect between these two ionospheric characteristics.

Using a modified Chebyshev pattern function with some control parameters, the Zolotarev difference patterns are synthesized by two proposed approaches. One approach consists in forming the pattern function expressed as the multiplication of the modified Chebyshev polynomial with an arctangent function; the other applies an interpolation technique to control the side lobe levels. The control parameters are given in the expressions of the SLL (Sidelobe Level) and array element number. Under the Fourier relation, the sampling theorem and the FFT algorithm are used to obtain the Zolotarev difference patterns with equal or unequal sidelobe levels. The synthesis examples show the simplicity of these two techniques and the capability to control the pattern' s sidelobe levels.