Abstract:
The paper proposes an automated method for analyzing ionospheric parameters and detecting ionospheric anomalies. The method is based on a generalized multicomponent model of ionospheric parameters (GMCM) developed by the authors. The model identification is based on an integrated approach combining the wavelet-transform methods with the autoregressive-integrated moving average models (ARIMA models). The paper provides estimates of the method efficiency, describes the operations of detecting ionospheric anomalies and evaluating their parameters. On the example of ionospheric parameter processing (the ionospheric critical frequency (foF$_2$)) for the Kamchatka region, we demonstrate the possibility of applying the method in on-line mode (as data become available to system). On the basis of the method, we detected shot-period anomalous changes proceeding magnetic storms and characterizing the occurrences of oscillatory processes in the ionosphere at the background of increased solar activity. The method has been implemented in the “Aurora” system for complex geophysical data analysis
(http://lsaoperanalysis.ikir.ru/lsaoperanalysis.html).
Key words:time series model, wavelet-transform, autoregressive models, ionospheric parameters, anomalies.