A generalized multicomponent model of ionospheric parameter time series (GMCM) was proposed. The model offers the possibility to represent the regular time course of the parameters and to detect the anomalous periods in the data. The model identification is based on the joint application of the wavelet transform and the autoregressive-integrated moving average models (ARIMA). The GMCM identification was carried out on the basis of hourly data on the ionospheric critical frequency (foF2) of the Paratunka station (IKIR FEB RAS, Kamchatka) for different seasons and solar activity levels. The comparison of the obtained GMCM-models with the Empiric International Reference IRI model for the analyzed station has shown the efficiency of the proposed model. The modeling results have shown high efficiency of the model for detecting ionospheric anomalies occurring on the eve and during ionospheric storms. The research was supported by the RNF Grant № 14-11-00194.

Authors: O. V. Mandrikova, N. V. Fetisova, V. V. Geppener

Direction: Informatics and Computer Technologies

Keywords: Wavelet-transform, autoregression models, ionospheric parameters, anomalies

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