Evaluating the effect of location and temperature components on improved estimation of the ionospheric electron content

Ionosphere is a region of ionized plasma that extends at an altitude of 80 to 1,200 km above the earth's surface. The ionosphere consists of free electrons and ions formed during the ionization process. Total electron content (TEC) in the ionosphere is reported in TECU units. Each TECU is equivalent to 1016 electron units per square meter. Ionosphere is highly sensitive to any atmospheric turbulence, and thus is considered to be an atmospheric event sensor. The present study seeks to investigate the effect of space and temperature on the amount of total ionospheric electron content in order to accurately estimate TEC value. To reach this aim, variations in latitude and longitude are decomposed for a given period of time using the process of transforming wavelet to frequency component and modeled using a variety of artificial neural networks.

 Here, after separating the location and temperature parameters in each region, ionospheric electron density is estimated for each spatial and temperature parameter separately and also as a combination using the capabilities of artificial neural networks and wavelet transform. TEC value for each location and temperature parameter is extracted from the ionospheric maps and then used as input data in the suggested method. These maps show ionospheric electron content. The standard format of ionospheric maps, which contains TEC values is called IONEX. These files are received from the website of Iranian National Mapping Agency.

 In general, IONEX is divided into three different parts: description, TEC maps, and standard deviations of maps. TEC values are presented in a regular network. Each IONEX file includes 25 maps, the last of which is the first map of the next day. As mentioned before, TEC value gives us a better understanding of ionospheric behavior. Availability of enough data and time coverage are two important factors in understanding a phenomenon and proper evaluation of its behavior.

 As results of artificial neural networks indicate, MLP generally has lower RMSE values. Therefore, it gives a more accurate estimation of TEC, compared to other artificial neural networks. Also compared to artificial neural networks, a combination of artificial neural networks and wavelet shows better results. The best condition of all three methods shows that compared to other methods, temperature variations give us a better estimation of TEC in ionosphere.