Abstract
In order to study in the field of the dynamics and continuous variations in the ionosphere, the ionospheric measurement tools such as ionosondes, incoherent scatter radars, satellites, and Global Positioning System (GPS) networks should be used. Total Electron Content (TEC) is a key parameter in the investigation and identification of ionosphere layer. From observations of dual frequency GPS receivers, the ionospheric TEC can be extracted. Global Ionospheric Maps (GIM) are auxiliary Maps for a study on ionosphere layer in around the world. It is necessary to produce the regional TEC map for precise studying of the ionospheric TEC. Bernese software is used to extract TEC by dual frequency GPS receivers. Regional modeling of ionospheric TEC by Artificial Neural Network (ANN) is a significant domain for prediction TEC at both single and double frequency GPS receivers. Five locations in Iran during the period of 2006-2010 were identified and used in the development of an input space and ANN architecture for the TEC modeling. The input space included the day number (seasonal variation), hour (diurnal variation), sunspot number (a measure of the solar activity) and magnetic index (a measure of magnetic activity). Based on the results, the ANN have capability and flexibility to model and to predict TEC. TEC predicted by ANN A (NN TEC) and TEC obtained from the IRI2007 version of the International Reference Ionosphere (IRI TEC) are compared during equinoxes and solstices. Results show that ANN predicts GPS TEC more accurately than the IRI over Iran. The IRI-2007 model is not a suitable method to produce TEC over IRAN.
Highlights
The ionosphere is a region of the upper Earth’s atmosphere where there are high concentrations of free ions and electrons
The Total Electron Content (TEC) values obtained from Global Positioning System (GPS) were calculated from the dual frequency GPS receiver network over some stations in IRAN such as TEHRAN, MASHHAD, RASHT, ABADAN and SHIRAZ stations using the Bernese software with the spatial and temporal resolutions 0.5° × 0.5° and 1 hour interval respectively
For the purpose of this work, hourly values of the TEC were extracted for about four years (2006–2009) in IONEX format file using GPS Estimation (GPSEST)
Summary
The ionosphere is a region of the upper Earth’s atmosphere where there are high concentrations of free ions and electrons. Global Navigation Satellite Systems (GNSS), like GPS, are widely used for various applications especially research and modeling of the ionosphere using temporal and spatial total electron content (TEC) variations [Meggs 2005, Schaer et al 1996, Tulunay et al 2004, 2006, Opperman et al 2007, Liu et al 2011]. If a single-frequency GPS receiver is used, deleting the ionospheric delay will be necessary to achieve an accurate navigation These values of the ionospheric delay can be calculated and eliminated using neural networks or other recommended techniques [Coster et al 2003, Skone 1998, Komjathy and Langley 1996, Liu and Gao 2003]. The neural networks are applied to predict the ionospheric TEC considering several observed ionospheric parameters such as local times, seasons, longitude, latitude, solar and geomagnetic activities. The neural networks are applied to predict the ionospheric TEC considering several observed ionospheric parameters such as local times, seasons, longitude, latitude, solar and geomagnetic activities. [Yilmaz et al 2009, Watthanasangmechai 2012, Leandro and Santos 2007, Ghaffari Razin et al 2015, Maruyama 2007, El-naggar 2013]
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