Abstract
This work is devoted to development of instruments for analysis of ionospheric parameters and detection of anomalies that occur during ionospheric disturbances. An algorithm is proposed to determine the parameters of a multicomponent model of ionospheric data. It is based on a combination of a wavelet transform and autoregressive-integrated moving average models. Methods for the model diagnosis are described. The multicomponent model allows description of quiet variations in ionospheric parameters, prediction of the variations, and detection of anomalies during disturbances. An algorithm based on wavelets and threshold functions is used for detection and detailed analysis of the anomalies. Data from the Institute of Cosmophysical Research and Radio Wave Propagation, Far East Branch, Russian Academy of Sciences, on the ionospheric foF2 critical frequency above Kamchatka were used during the experiments. Anomalies that occur in the ionosphere during increased solar and seismic activity above Kamchatka have been revealed on the basis of the simulation and data analysis.
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