Abstract
The atmosphere is exposed to the Sun–Earth interaction. The ionosphere is located in the upper part of the atmosphere, extending from 50 to 1000 km, and where the signal transfer is addressed. It is the layer where the changes resulting from this interaction are observed. Total electron content (TEC) is a dynamic and vital ionospheric variable that enables the understanding and interpretation of ionospheric changes. This study models Global Navigation Satellite System (GNSS)-based TEC data with an artificial neural network for the solar wind parameters during a moderate (February 02, 2015) and a weak (February 07, 2015) geomagnetic storm. Physical facts and the causality principle govern these models. The conclusions agree with the literature and are acceptable. The performance of the models is evaluated by the correlation coefficient (R), mean square error, and absolute mean error. The R for the TEC data of the moderate GS (Dst = – 55 nT) is 0.969, and the mean square error is 3.742. In addition to these values, the absolute mean error is 0.53% with a variance of 0.06. The R for the TEC data of the weak (Dst = – 44 nT) GS is 0.988, the mean square error value is 2.050, and the absolute mean error is 0.41% with a variance of 0.03. These results seem acceptable and comparable for both geomagnetic storms.
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Acknowledgements
The authors thank to NASA and Kyoto University. The authors thank to Center for Orbit Determination in Europe (CODE). Research of E. Nane is partially supported by a Simons Foundation Collaboration Grant for Mathematicians.
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Eroglu, E., Nane, E. GNSS-based TEC data modeling with the solar wind parameters. Indian J Phys 97, 1973–1980 (2023). https://doi.org/10.1007/s12648-022-02573-z
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DOI: https://doi.org/10.1007/s12648-022-02573-z