عنوان مقاله [English]
نویسندگان [English]چکیده [English]
The outer part of the upper atmosphere, the ionosphere, is where free electrons formed by solar X-rays and extreme ultraviolet (EUV) radiation play an important role in radio and satellite communication. Knowledge of the critical frequency and maximum electron density of the F2 ionospheric layer distribution is crucial for ionospheric storm studies and the estimation and correction of propagation delays in telecommunications. It has proven that the F2 ionospheric layer can significantly affect the propagation of radio waves. The ionosphere can be used to reflect radio signals over long distances. Indeed, the ionosphere is an efficient reflector with frequencies below approximately 30 MHz. Also, it can be useful to study the variability of the F2 ionospheric layer to show that this layer is affected primarily by space weather phenomena, mostly of solar origin such as the solar zenith angle, solar ionizing radiation, and the solar cycle. These extraterrestrial phenomena control the ionosphere from above. Some other waves enter the ionosphere from below and can cause some significant changes in the F2 ionospheric layer.
The International Reference Ionosphere (IRI) model is a standard model of the ionosphere supported by the Committee on Space Research (COSPAR) and International Union of Radio Science (URSI). The IRI model has many practical applications in High Frequency (HF) predictions. The IRI model offers a description of the average ionosphere. It is a mathematical description of the ionosphere as a function of location, time, altitude, solar activity and geomagnetic activity. Periodic updates to this model are essential to maintaining its prediction ability. A large number of independent studies have evaluated the IRI model in comparisons with direct and indirect ionospheric measurements, those not used in the model development. A favorable comparison with IRI model is often one of the major goals of ionospheric teams all over the world.
In this work, to evaluate the latest available ionospheric model, IRI-2007, we have obtained hourly monthly values of foF2 and NmF2 over the Tehran area (35.4N,51.2E, 52.7dip) during low solar activity period, in which the Rz12 (12-month running average sunspot number) varies between 7.7 and 15.3 from July 2006 to June 2007. Data measured using the IPS-71 at the ionospheric station at the Institute of Geophysics at the University of Tehran (35.4N, 51.2E, 52.7dip) were used to perform the calculations. Subsequently, the observed critical frequency and maximum electron density of the F2 ionospheric layer is compared with IRI-2007 model predictions. To run the IRI2007 model, the URSI and CCIR coefficients were used.
Our study shows that values of the foF2 and NmF2 parameters have the highest values during the daytime hours and the lowest values occur at pre-sunrise hours. Our study shows that the IRI-2007’s ability to predict semi-annual anomalies with a maximum electron density of the F2 ionospheric layer are most accurate in the summer and less so during the autumn and spring.
In general, the predictions obtained with CCIR and URSI are similar. Our results show that IRI-2007 can successfully predict the critical frequency and maximum electron density of the F2 ionospheric layer. Additionally, our study shows that differences between the foF2(OBS) and the foF2(IRI-2007) remains below 12% during all seasons. The best agreement occurs during the summer and winter, and the largest differences are observed in the spring and autumn. The average percentage deviation of a full year registers at approximately 6.5% for CCIR and nearly 8% for URSI coeffecients. Moreover, our results show that the percentage deviation between the NmF2(OBS) and the NmF2(IRI-2007) remains lower than 21% during all seasons. The total average percentage deviation of a full year is approximately 13% for the CCIR coefficient and nearly 16.5% for the URSI.
This result shows that the IRI-2007 has been able to predict ionospheric parameters correctly. Therefore, the IRI-2007 predictions can be used for cases in which the observed data are missing.