Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities

Timoçin, Erdinç; Temuçin, Hüseyin; İnyurt, Samed; Shah, Munawar; Jamjareegulgarn, Punyawi

doi:10.1016/j.actaastro.2020.12.042

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Numerous events took place, including ion production and molecular reactions, and also in the vicinity of the future EQ variations in electric eld are observed. Pholes spread through the atmosphere to the ionosphere at various altitudes as more and more of them reach the surface (Timoçin, 2020;Thomas, 2017).…”

Section: Introductionmentioning

confidence: 99%

GNSS TEC and Swarm Satellites for the detection of Ionospheric Anomalies Possibly associated with 2018 Alaska Earthquake

Haider,

Yan,

Shahzad

et al. 2023

Preprint

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In the hunt for seismic precursors with GNSS to detect earthquake-related anomalies in ionosphere are proved as an effective strategy. One method is to use GNSS TEC to distinguish between seismic anomalies in ionosphere and anomalies induced by geo magnetic storm. In this study, TEC data of four GNSS sites near the epicenter of November 30, 2018, Alaska earthquake (Mw 7.1) are examined. We also examined the TEC from Swarm satellites during the local day and nighttime to further support the EQ-induced perturbations in ionosphere. One to six days before the major EQ, the GNSS stations' TEC displayed considerable disturbance positive anomalies crossing the upper bound. The GNSS TEC from the four stations near the EQ epicenter detected ionosphere perturbations in 1 to 6 days prior to the EQ. The swarm satellites TEC data also confirmed these findings. On the other hand, retrieving TEC from all GNSS sites during the EQ preparation phase and weak geo magnetic storm (Kp 4, Dst − 50 nT), we discover evidence of low-intensity anomalies in ionosphere 25–30 days prior to the major shock. Further research shows that EQ-induced TEC anomalies are considerable between UTC 17:30 and 23:00 and that the storm-induced TEC anomaly (caused by Dst = -50 nT and Kp 4) predominates in all GNSS stations between UTC 17:00 and 23:30. In the EQ preparation phase, all of these anomalies before the primary shock are helpful in separating seismic from geomagnetic storm anomalies. Additionally, using TEC monitoring, this work contributes to the growing lithosphere-ionosphere connection concept.

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Section: Introductionmentioning

confidence: 99%

GNSS TEC and Swarm Satellites for the detection of Ionospheric Anomalies Possibly associated with 2018 Alaska Earthquake

Haider,

Yan,

Shahzad

et al. 2023

Preprint

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“…The research conducted by Erdinç et al (2021) aimed to improve the performance of the IRI-2016 model in forecasting foF2 across three latitudes in different hemispheres using data from six ionosonde stations: Manila (14.7°N, 121.1°E), Yamagawa (31.2°N, 130.6°E), Yakutsk (62.0°N, 129.6°E), Townsville (19.6°S, 146.8°E), Hobart (42.9°S, 147.3°E), and Terre Adelie (66.6°S, 140.0°E) during periods of both low and high solar activity. Their study concludes that the performance of the IRI-2016 model is strongly influenced by solar activity, latitude, seasonal altitude, local time, and hemisphere.…”

Section: Introductionmentioning

confidence: 99%

foF2 diurnal variation at Dakar station during the period of geomagnetic shock of variable duration over solar cycle 21 and 22: Prediction with IRI 2016

Ali,

Douzane,

Sibri

et al. 2023

Int. J. Phys. Sci.

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This work concerns the comparative study of the diurnal variation of the critical frequency of the F2 layer (foF2) between experimental data from the Dakar station and those of the IRI 2016 model through its URSI. The comparison was conducted during solar cycles 21 and 22, across different phases, and on various days of geomagnetic shock activity. The results indicate that during solar minimum, the graphs of the experimental data generally exhibit the signature of vertical drift, whereas the IRI model presents a plateau profile indicative of the absence of an electrojet. During this phase, the highest deviation percentages were typically observed around sunrise. The results also demonstrate that at the Dakar station, the increasing phase was characterized by a pre-reversal of the electric field on different shock days. Regardless of the duration of the shock, the decreasing phase was marked by the complete absence of an electrojet, as observed in both sets of data. The quantitative study reveals that at solar maximum, our results exhibit a strong correlation between the experimental data and the IRI model. Throughout all phases, predictions were accurate during the daytime and exhibited high values around sunrise.

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Influence of the ionosphere on the accuracy of the satellite navigation system

2022

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Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities

Cited by 11 publications

References 18 publications

GNSS TEC and Swarm Satellites for the detection of Ionospheric Anomalies Possibly associated with 2018 Alaska Earthquake

GNSS TEC and Swarm Satellites for the detection of Ionospheric Anomalies Possibly associated with 2018 Alaska Earthquake

foF2 diurnal variation at Dakar station during the period of geomagnetic shock of variable duration over solar cycle 21 and 22: Prediction with IRI 2016

Influence of the ionosphere on the accuracy of the satellite navigation system

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