Abstract
In this study, a new multicomponent model (MCM) to determine the time variation of ionospheric parameters is suggested. The model was based on the combination of wavelets with autoregressive-integrated moving average model classes and allowed the study of the seasonal and diurnal variations of ionospheric parameters and the determination of anomalies occurring during ionospheric disturbances. To investigate in detail anomalous changes in the ionosphere, new computational solutions to detect anomalies of different scales and estimate their parameters (e.g., time of occurrence, duration, scale, and intensity) were developed based on a continuous wavelet transform. The MCM construction for different seasons and periods of solar activity was described using ionosphere critical frequency f o F2 data from Kamchatka (Paratunka Station, 52° 58′ N, 158° 15′ E, Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS). A comparison of the MCM with the empiric International Reference Ionosphere (IRI) model and the moving median method for the analyzed region showed that the suggested method was promising for future research, since it had the advantage of providing quantitative estimates for the occurrence time, duration, and intensity of the anomalies, characterizing the ionospheric state and disturbance degree with a higher accuracy. Geomagnetic storms from 17 March and 2 October 2013 were analyzed using the suggested method, and it was shown that the ionospheric disturbances were at maximum during the strongest geomagnetic disturbances. An increase in the electron concentration in comparison with the background level, under calm or weakly disturbed geomagnetic field conditions, was identified before the analyzed magnetic storms.
Highlights
The present study aimed to develop tools for ionospheric parameter analysis and anomaly detection during ionospheric disturbances
The most important tasks in ionospheric parameter processing and analysis are the monitoring of the ionospheric conditions and the detection of anomalies (Afraimovich et al 2000, 2001; Liu et al 2008a, 2008b; Nakamura et al 2009; Watthanasangmechai et al 2012; Danilov 2013; Ezquer et al 2014; Zhao et al 2014), which affect many aspects of our life and have a negative impact on satellite system operation and radio communication propagation
The problems associated with the analysis of ionospheric conditions and detection of anomalies have been addressed by many authors (Bilitza and Reinisch 2007; Liu et al 2008a, 2008b; Nakamura et al 2009; Maruyama et al 2011; Klimenko et al 2012a, 2012b; Oyekola and Fagundes 2012; Watthanasangmechai et al 2012; Ezquer et al 2014; Zhao et al 2014)
Summary
The present study aimed to develop tools for ionospheric parameter analysis and anomaly detection during ionospheric disturbances. The International Reference Ionosphere (IRI) model (Jee et al 2005; Bilitza and Reinisch 2007; Klimenko et al 2012b; Oyekola and Fagundes 2012) is the best ionospheric empirical model It is based on a wide range of ground and space data and, since its parameter estimation accuracy for a particular region depends significantly on the availability of local registered data, its results can largely deviate from the experimental data (Bilitza and Reinisch 2007; Ezquer et al 2014). The recent development of empirical models using pattern recognition techniques and neural networks (Nakamura et al 2007, 2009; Wang et al 2013; Zhao et al 2014) allowed for a significant improvement of the forecast quality in comparison with the IRI model, as they are easy to implement automatically and flexible enough. Their main advantage is their mathematical basis and consequent ability to obtain results with a given confidence probability
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