Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development

Abstract The ionosphere over Iran is located on the upper edge of equatorial ionization anomaly (EIA). The Equatorial Electrojet (EEJ) as the proxy of temporal evolution of the EIA is explored using the MAGDAS stations near the magnetic equator during quiet and active geomagnetic conditions. The pos...

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Main Authors: M. Vazifehkhah Hafteh, A. Mahmoudian, A. Yoshikawa, K. Girgis
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Space Weather
Online Access:https://doi.org/10.1029/2024SW004032
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author M. Vazifehkhah Hafteh
A. Mahmoudian
A. Yoshikawa
K. Girgis
author_facet M. Vazifehkhah Hafteh
A. Mahmoudian
A. Yoshikawa
K. Girgis
author_sort M. Vazifehkhah Hafteh
collection DOAJ
description Abstract The ionosphere over Iran is located on the upper edge of equatorial ionization anomaly (EIA). The Equatorial Electrojet (EEJ) as the proxy of temporal evolution of the EIA is explored using the MAGDAS stations near the magnetic equator during quiet and active geomagnetic conditions. The possible impact of EIA on Total Electron Content (TEC) over Iran is investigated. Analyzing data from 140 stations over 7 months, it is found that EEJ influences TEC and its latitudinal penetration. During geomagnetically active days, EEJ's behavior and TEC's latitudinal variations are investigated. Diurnal EEJ patterns and counter electrojets (CEJ) are analyzed, alongside solar wind timing effects on EEJ and TEC using data from stations in Brazil, Peru, the Philippines, and Sri Lanka. Statistical correlations between maximum TEC and parameters extracted from the daily EEJ profile, including EEJdaymax, integrated EEJ (IEEJ), and IEEJdaymax—highlight Iran's position at the EIA's upper edge, indicating high susceptibility to equatorial dynamics. In addition, the Rate of Total Electron Content Index (ROTI) has been analyzed under geomagnetic active conditions. The results demonstrate that ROTI exhibits good agreement with TEC in terms of latitude penetration and the detected gradient in TEC. Employing Artificial Neural Networks (ANN) for local TEC prediction, Iran is segmented into eight regions based on GNSS receiver distribution. Each region's ANN model, trained during the quiet geomagnetic condition, assesses predictability and accuracy. The ANN model demonstrates reliable local TEC forecasting and confirms the direct impact of equatorial dynamics on Iran's ionosphere.
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spelling doaj-art-d5245ede0a264385967fec07bdb3cf522025-01-14T16:26:51ZengWileySpace Weather1542-73902024-11-012211n/an/a10.1029/2024SW004032Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model DevelopmentM. Vazifehkhah Hafteh0A. Mahmoudian1A. Yoshikawa2K. Girgis3Institute of Geophysics University of Tehran Tehran IranInstitute of Geophysics University of Tehran Tehran IranSpace Environment Research Center Kyushu University Fukuoka JapanInternational Research Center for Space and Planetary Environmental Science (i‐SPES) Kyushu University Fukuoka JapanAbstract The ionosphere over Iran is located on the upper edge of equatorial ionization anomaly (EIA). The Equatorial Electrojet (EEJ) as the proxy of temporal evolution of the EIA is explored using the MAGDAS stations near the magnetic equator during quiet and active geomagnetic conditions. The possible impact of EIA on Total Electron Content (TEC) over Iran is investigated. Analyzing data from 140 stations over 7 months, it is found that EEJ influences TEC and its latitudinal penetration. During geomagnetically active days, EEJ's behavior and TEC's latitudinal variations are investigated. Diurnal EEJ patterns and counter electrojets (CEJ) are analyzed, alongside solar wind timing effects on EEJ and TEC using data from stations in Brazil, Peru, the Philippines, and Sri Lanka. Statistical correlations between maximum TEC and parameters extracted from the daily EEJ profile, including EEJdaymax, integrated EEJ (IEEJ), and IEEJdaymax—highlight Iran's position at the EIA's upper edge, indicating high susceptibility to equatorial dynamics. In addition, the Rate of Total Electron Content Index (ROTI) has been analyzed under geomagnetic active conditions. The results demonstrate that ROTI exhibits good agreement with TEC in terms of latitude penetration and the detected gradient in TEC. Employing Artificial Neural Networks (ANN) for local TEC prediction, Iran is segmented into eight regions based on GNSS receiver distribution. Each region's ANN model, trained during the quiet geomagnetic condition, assesses predictability and accuracy. The ANN model demonstrates reliable local TEC forecasting and confirms the direct impact of equatorial dynamics on Iran's ionosphere.https://doi.org/10.1029/2024SW004032
spellingShingle M. Vazifehkhah Hafteh
A. Mahmoudian
A. Yoshikawa
K. Girgis
Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
Space Weather
title Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
title_full Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
title_fullStr Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
title_full_unstemmed Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
title_short Mid‐Latitude Study of Ionospheric Variation Over Iran Associated With Equatorial Ionization Anomaly (EIA), and Artificial Neural Networks Model Development
title_sort mid latitude study of ionospheric variation over iran associated with equatorial ionization anomaly eia and artificial neural networks model development
url https://doi.org/10.1029/2024SW004032
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