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The paper presents the results of detection of ionospheric anomalies in online mode according to the ionosonde data at Paratunka station, Kamchatka peninsula (IKIR FEB RAS). The developed algorithms have been implemented in Aurora system for online geophysical data analysis (http://lsaoperanalysis.ikir.ru:9180/lsaoperanalysis.html). The algorithms allow us to detect sudden anomalous changes of varying intensity in the dynamics of ionospheric parameters, as well as to estimate their characteristics. The efficiency of the system and the possibility of its application in space weather forecast tasks have been shown on the examples of events occurred in 2019.

In the present paper, we have carried out an analysis of the ionospheric critical frequency data of the F2 layer during strong magnetic storms in 2017-2018. The ionospheric data of Paratunka (IKIR FEB RAS, Kamchatka, 53.0 N, 158.7 E), Wakkanai (Japan, 45.16 N, 141.75 E), and Moscow stations (Russia, 55.49 N, 37.29 E) were used. The study was carried out using a generalized multicomponent model (GMCM) developed by the authors. GMCM allows studying the dynamics of the ionospheric parameters in detail and estimating their characteristics. Using the modeling, we detected and studied anomalous changes in the ionosphere preceding and accompanying the periods of magnetic storms in the analyzed areas. The study results were compared with the traditional median method and showed the perspectiveness of GMCM. The research is supported by a grant from the Russian Science Foundation (project No. 14-11-00194).

Algorithms for ionospheric data processing are presented in the paper. The algorithms are implemented in the real-time mode of ionospheric parameter analysis. They are a component of “Aurora” software system for geophysical data analysis. The algorithms allow us to estimate the state of the ionosphere in the region of Kamchatka Peninsula and to detect ionospheric anomalies. Assessment of the algorithms efficiency has shown that it is possible to use them to detect ionospheric anomalies that may occur on the eve of magnetic storms. The research is supported by the Russian Science Foundation Grant (Project No. 14-11-00194).

The paper presents methods of modeling and analysis of ionospheric parameters, which realized in the program system of complex analysis of geophysical parameters “Aurora”. The methods allow to analyze of characteristic changes in the ionospheric parameters and allocate the anomalous features during periods of ionospheric disturbances. The algorithm parameters are adapted for analyzing the ionospheric data of the Paratunka station (Kamchatka) and based on results of the estimates (station data of Yakutsk, Gakona, etc. were analyzed). Methods can be applied for the mid-latitude region. The system is implemented in the public domain (http://aurorasa.ikir.ru:8580). The research was supported by RSF Grant, project No 14-11-00194.

The paper shows new methods of analysis of ionospheric and magnetic data applying the models of multicomponent constructions (MCM models) developed by the authors. Based on ground station data, the analysis of ionospheric and magnetic data during increased solar activity was carried out.

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency. [ABSTRACT FROM AUTHOR]