Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere

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.

Joint analysis of the ionospheric parameters and cosmic ray data during periods of magnetic storms 2015

“…During the periods of disturbances, anomalous changes are observed in the recorded ionospheric parameters, which indicate the occurrence of anomalous processes in the ionosphere. It is known that the strongest ionospheric disturbances occur during solar events and geomagnetic storms, the study of which has important scientific and applied significance [17][18][19][20][21]. In this paper, based on the joint analysis of the ionosphere parameters and GCR data on the eve of magnetic storms, anomalous increases in variations in the intensity of cosmic rays and the increase in the electron density of the ionosphere that arise during these periods are likely to be associated with the approaching events.…”

“…On the basis of the computational solutions proposed in [18], we determine the values of wavelet coefficients that exceed a given threshold:…”

Analysis of ionospheric parameters during Solar events and geomagnetic storms

“…Among the general approaches we can emphasize the traditional moving median method [1,9], empirical models of the ionosphere [2,3,[5][6][7], application of neural networks [2,8,10], and wavelet transform [10][11][12][13][14]. The most developed empirical model of the ionosphere is the International Reference Ionosphere (IRI) model [5], which is based on a wide range of ground and space data.…”

“…In the paper, we use a complex approach based on wavelet transform methods and their combinations with classical autoregressive approaches and neural networks. In the previous papers [4,[12][13][14] we showed that application of classical autoregressive methods [15] in combination with modern methods for pattern recognition allow us to obtain quite exact estimates and they are easily realized in automatic mode. The main advantage of the suggested approach is the mathematical validity and, as a consequence, the possibility to receive the results with defined confidence probability.…”

Special issue “Coupling of the high and mid latitude ionosphere and its relation to geospace dynamics”

“…Furthermore, it is important to model the ionospheric parameters to extract the main characteristics of ionospheric disturbances. Mandrikova et al (2015) have developed an algorithm for modeling ionospheric parameter FoF2 and extracting the main characteristics of ionospheric perturbations during different seasons and geomagnetic activities.…”