Statistical analysis of the regional and global ionosphere model on intense geomagnetic storm

Başçiftçi, Fuat; Bülbül, Sercan

doi:10.1007/s12648-023-02700-4

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…In eqs. (5)(6), setting such boundaries ensures an approximately 95% probability that the out-of-bound deviations are NON-RANDOM. In determining which day is the anomaly day, if one-third of the TEC values of that day fall above the upper limit (positive anomaly) or below the lower limit (negative anomaly), that day is expressed as the anomaly day [12,[24][25].…”

Section: Fig 1 𝐸 𝑖𝑗 Interpolationmentioning

confidence: 99%

Examination of the Relationships between TEC Changes and Space Weather Conditions Before and After the February 6, 2023 Kahramanmaraş Earthquake (Mw= 7.8 and Mw=7.5)

Bulbul

2024

Preprint

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Earthquakes are major natural disasters that cause loss of life and property. Predicting earthquakes in advance is highly important. In recent years, GNSS measurements have been used to predict earthquakes, particularly by analysing total electron content (TEC) values obtained from these measurements. However, it is crucial to accurately analyse the TEC changes influenced by space weather conditions and distinguish them from those caused by earthquakes. This study investigated the impact of the Pazarcık-Kahramanmaraş (Mw: 7.8) and Ekinözü-Kahramanmaraş (Mw: 7.5) earthquakes that occurred on February 6, 2023, on TEC changes. To achieve this goal, space weather conditions, such as the Kp, Dst (nT), F10.7 (sfu), P (\(N⁄c{m}^{3}\)), Bx (nT), By (nT), and Bz (nT) indices, were examined for one month before and after the earthquake occurred. Potentially active days were identified. After establishing space weather conditions, their impact on TEC changes was investigated. The results indicated that there was no significant influence of space weather conditions on TEC changes during the selected time frame, suggesting that these changes may be attributed to the earthquake. Additionally, CODE-TEC and ESA-TEC values for the selected time frame were obtained using the coordinates of 15 CORS-Tr stations in the earthquake region. The upper and lower boundary values of the TEC were determined, and days with anomalies were identified. As a result, it was found that there were days with a positive anomaly approximately 15–18 days before the occurrence of possible earthquakes, which could serve as a preparation period. Furthermore, postearthquake anomalies were suggested to potentially be caused by aftershocks.

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Section: Fig 1 𝐸 𝑖𝑗 Interpolationmentioning

confidence: 99%

Examination of the Relationships between TEC Changes and Space Weather Conditions Before and After the February 6, 2023 Kahramanmaraş Earthquake (Mw= 7.8 and Mw=7.5)

Bulbul

2024

Preprint

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Seismo ionospheric anomalies related to the Mw 7.5, Kepulauan Alor, Indonesia earthquake

Koklu

2023

Acta Geophys.

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Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm

Eroglu,

Koksal

2024

Discrete Dynamics in Nature and Society

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The discussion models the IRI-2012 TEC map over a moderate geomagnetic storm period (5 days) in February 2015 and compares the yield of the models. The models are constructed with the help of cubic Bézier curves and machine learning. In a sense, the comparison of a classical and mechanical approach with a modern and computer-based one is a considerable experience for the paper. The parametric curve approach governs models of piecewise continuous Bézier cubics, while the models employ only the TEC map. The design is separated into curve components at every five-hour curvature point, and each component is handled independently. Instead of the traditional least squares method for finding control points of cubics, it utilizes the mean of every five-hour of the piecewise curves of the TEC data. Accordingly, the prediction error can be controlled at a rate that can compete with the modern network approach. In the network model, 120 hours of the solar wind parameters and the TEC map of the storm are processed. The reliability of the network model is assessed by the (R) correlation coefficient and mean square error. In modeling the TEC map with the classical approach, the mean absolute error is 0.0901% and the correlation coefficient (R) score is 99.9%. The R score of the network model is 99.6%, and the mean square error is 0.71958 (TECU) (at epoch 47). The results agree with the literature.

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Statistical analysis of the regional and global ionosphere model on intense geomagnetic storm

Cited by 3 publications

References 44 publications

Examination of the Relationships between TEC Changes and Space Weather Conditions Before and After the February 6, 2023 Kahramanmaraş Earthquake (Mw= 7.8 and Mw=7.5)

Examination of the Relationships between TEC Changes and Space Weather Conditions Before and After the February 6, 2023 Kahramanmaraş Earthquake (Mw= 7.8 and Mw=7.5)

Seismo ionospheric anomalies related to the Mw 7.5, Kepulauan Alor, Indonesia earthquake

Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm

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