Quiet Time foF2 Variation at Ouagadougou Station and Comparison with TIEGCM and IRI-2012 Predictions for 1985 and 1990

Abstract

The purpose of this study is to appreciate the estimation of TIEGCM (Thermosphere Ionosphere Electrodynamics General Circulation Model) and that of the 2012 version of IRI (International Reference Ionosphere) in African Equatorial Ionization Anomaly (EIA) region through the diurnal variation of F2 layer critical frequency (foF2).The comparison is made between data and theoretical values carried out from TIEGCM and IRI-2012 during solar cycle minimum and maximum phases and under quiet time condition over seasons. Data concern solar cycle 22 foF2 data of Ouagadougou station (Lat: 12.4° N; Long: 358.5°E, dip: 1.43°N for 2013) provided by Télécom Bretagne. Quiet time condition is determined by Aa inferior or equal to 20 nT and solar cycle maximum and minimum phases correspond to sunspot number Rz superior to 100 and Rz inferior to 20, respectively. Seasons are estimated by considering December as winter month, March as spring month, June as summer month and September as autumn month. The seasonal Hourly quiet time foF2 is given by the arithmetic mean values of the five quietest day hourly values. Data profiles show noon bite out profile with more and less pronounced morning or afternoon peak in equinox and that during solar maximum and that also in solar minimum except during solstice where the profile fairly is dome or plateau. During solar minimum, both models present more or less pronounced afternoon peak with more or less deep trough between 1000 LT and 1400 LT. During solar maximum, in general, TIEGCM shows afternoon peak and IRI-2012 present plateau profile. This result exhibits the non-well estimation of the dynamic process of this region. Model accuracy is highlighted by the Mean Relative Error (MRE) values. These values show better prediction for IRI-2012 except in September for both solar cycle phases involved. The non-good prediction of TIEGCM is observed in December during solar minimum and in June during solar maximum. Models predictions are better during solar maximum than during solar minimum and strongly dependent on pre-sunrise and post sunset periods.