A Neural Network Tool for the Interpolation of foF2 Data in the Presence of Sporadic E Layer

This paper presents the application of Neural Networks for the interpolation of (critical frequency) foF2 data over Cyprus in the presence of sporadic E layer which is a frequent phenomenon during summer months causing inevitable gaps in the foF2 data series. This ionospheric characteristic (foF2) constitutes the most important parameter in HF (High Frequency) communications since it is used to derive the optimum operating frequency in HF links and therefore interpolating missing data is very important in preserving the data series which is used in long-term prediction procedures and models.