An algorithm for detecting intense anomalous changes in the time dependence of ionospheric parameters

Abstract

The paper presents a modified multicomponent model of ionospheric parameter time series. The model describes regular variations and anomalous changes of a multi-scale structure that characterize the occurrence of ionospheric irregularities. Identification of the model components is based on a combined application of the wavelet transform and autoregressive-integrated moving average models. An algorithm for analyzing ionospheric parameters has been developed on the basis of the proposed model. The algorithm allows the intensive ionospheric anomalies characterizing the occurrence of strong ionospheric storms to be detected on-line. Results of the evaluation of the algorithm performance are presented. The evaluation is performed by the example of processing and analyzing hourly and 15-minute data on the ionospheric critical frequency (foF2) during magnetic storms in 2015 – 2017. The performed estimations showed the efficiency of the algorithm and the possibility of its application for space weather forecasting.