Vol. 39, issue 02, article # 7

Abstract:

Monitoring and analysing the dynamics of ionospheric parameters allow one to detect disturbances that negatively impact technical systems. Problems of the timely detection of ionospheric disturbances are associated with a high degree of uncertainty in our prior knowledge about the dynamics of ionospheric processes during disturbed periods and the influence of interference and uneven observation network in certain areas. These issues necessitate the development of data recording and analysis methods that guarantee high accuracy and efficiency. The paper presents a new automated method for estimating the state of the ionosphere using ground-based vertical radiosonde data. This method combines elements of statistical decision theory and threshold estimation with wavelet transform. Numerical solutions constructed using this method are used in the Aurora interactive system (https://lsaoperanalysis.ikir.ru/lsaoperanalysis.html), which was developed at the Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch, Russian Academy of Sciences. The results of the implemented algorithms provide data on the state of the ionosphere over Kamchatka (calm or perturbed) and the parameters of ionospheric inhomogeneities in perturbed state. The algorithms are adaptive and do not require preliminary training. Data from the Paratunka (Russia, Kamchatka Region) and Wakkanai (Japan) stations were used to evaluate the method. The behaviour of the ionosphere during periods of strong geomagnetic storms in 2023–2024 was studied. The study confirmed the method efficiency in analysing ionospheric data and detecting inhomogeneities. Prior to the analysed events, signs of an anomalous increase in electron density in the ionosphere were identified. This is of significant practical importance. The suggested method can be used in ionospheric data analysis techniques for monitoring and forecasting space weather conditions, with the aim of timely detection of ionospheric disturbances.

Keywords:

data analysis method, ionosphere, space weather, magnetic storm

Figures:

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