Papers by the Researchers from the Laboratory of System Analysis of Atmospheric Processes of IKIR FEB RAS were published in Atmosphere and Ocean Optics Journal

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Papers by the Researchers from the Laboratory of System Analysis of Atmospheric Processes of IKIR FEB RAS were published in Atmosphere and Ocean Optics Journal

A new automated method to estimate ionosphere state based on the data of ground vertical radio sounding has been presented in the paper “An automated method of estimating the state of the ionosphere based on ionosonde data in the Aurora interactive system” by the Chief Researcher of IKIR FEB RAS, Madrikova Oksana Viktorovna, and Senior Researcher, Polozov Yuriy Alexandrovich. The method is based on the combination of elements of statistical solution theory and threshold estimates with wavelet transform (Fig. 2, 3, 4).

Numerical solutions constructed using this method are used in the Aurora interactive system. 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 case of the perturbed state. The algorithms are adaptive and do not require preliminary training. The method was tested on Paratunka (Russia, Kamchatskiy kray) and Wakkanai (Japan) station data. The behavior of the ionosphere during strong geomagnetic storms in 2023–2024 was studied.

The study confirmed the method efficiency in analyzing ionospheric data and detecting inhomogeneities. Prior to the analyzed events, features of anomalous increases in electron density in the ionosphere were identified that 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.

For the reference: Mandrikova O.V., Polozov Yu.A. An Automated Method of Estimating the State of the Ionosphere Based on Ionosonde Data in the Aurora Interactive System // Atmospheric and Oceanic Optics, 2026, V. 39. No. 03. pp. 371–377. https://doi.org/10.1134/S1024856026700168. Ful text is available at link.

The paper "Anomaly recognition in neutron monitor observations using wavelet decompositions and rules of statistical decision theory", written by the Chief Researcher of IKIR FEB RAS, Madrikova Oksana Viktorovna, and the Researcher, Mandrikova Bogdana Sergeevna, presents a new automated method for detecting and estimating anomaly intensity in neutron monitor (NM) observations (Fig. 5).

Discrete wavelet decompositions with adaptive threshold functions are used to detect anomalies. Parameters of the threshold functions are automatically estimated (as the data enter the processing system) using rules of statistical decision theory. Applying these rules yields a solution with an error not exceeding an a priori specified value. The intensity of anomalies in NM observations is then calculated by summing the amplitudes of wavelet coefficients exceeding the estimated thresholds.

The paper studies periods of strong and extreme geomagnetic storms in 2024–2025. Correlations between values of the Dst index and the intensity of anomalies in NM observations are analyzed. The results show significant increases in the correlation during geomagnetic storms. The Dst index reached its maximums relative to the intensity maximums with a delay of several hours that demonstrates the importance of neutron monitor observations and their consideration when solving space weather problems. The results of the work can be used in space weather forecasting for early detection of sporadic variations in a cosmic ray flux.

For the reference: Mandrikova O.V., Mandrikova B.S. Anomaly Recognition in Neutron Monitor Observations Using Wavelet Decompositions and Rules of Statistical Decision Theory // Atmospheric and Oceanic Optics, 2026, V. 39. No. 03. pp. 362–370. https://doi.org/10.1134/S1024856026700156 Full text is available at link.