Seasonal Variation of the F2-Layer Critical Frequency and Comparison with the IRI-2016 Model during Two Extremes of Solar Activity Phase of Solar Cycle 22

Abstract

Abstract: Introduction: The variation of the ionosphere is mostly studied using the critical frequency of the F2-layer (foF2) whose values can also be predicted by an ionospheric model. The widely used model for predicting ionospheric parameters is the International Reference Ionosphere (IRI). Aim: This paper aims to demonstrate how well the current International Reference Ionosphere (IRI-2016) model performs in predicting the critical frequency of the F2-layer (foF2) over two equatorial stations during two extremes of solar activity phase of solar cycle 22.. Methods: The hourly foF2 experimental data collected during the Maximum Phase of Solar Activity MPSA year (1989) and Minimum Phase of Solar Activity MnPSA year (1986) at Ougadougou (Geomagnetic Latitude 0.59 oN, Geomagnetic Longitude 71.46 oE) in the African longitudinal sector and Manila (Geomagnetic Latitude 3.4 oN, Geomagnetic Longitude 191.1 oE) in the Asia longitudinal sector as well as the predicted foF2 data by the IRI-2016 model were used in this study. Sunspot data from Zurich was utilized as a measure of solar activity phase. The foF2 data were grouped into four seasons before analysis began. Comparing the seasonal means of the experimental foF2 data and the IRI-2016 modeled foF2, it was possible to determine how closely the model matched the experimental data at the different seasons and longitudinal sectors Results: The results showed that the IRI-2016 model overestimate and underestimate the observed foF2 at different periods of the day during the equinox and solstice seasons. Observation showed that the highest positive and negative percentage deviations were observed mostly during the post-midnight hours. Observation also showed that Seasonal mean values of the IRI-2016 model of both options showed remarkable improvement at this two stations since their values have little difference from the observed foF2 values. Conclusion: The discrepancy (underestimation and overestimation) in the IRI-2016 model is found larger during MPSA year than during MnPSA year. The URSI option performs better than the CCIR option since its predicted values are much closer to the observed values. Both options of the model perform better in the Asian longitudinal sector than the African longitudinal sector.