Simulation and analysis of time variations in ionospheric parameters on the basis of wavelet transform and multicomponent models

Mandrikova, Oksana; Glushkova, N. V.; Polozov, Yuryi

doi:10.1134/s1054661815030141

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variations of the Earth's magnetic field contain important information on the processes in the magnetosphere that occur during the period of high solar activity. Currently, methods [3][4][5][6] and application software for the processing and analysis of geophysical data are being developed. They provide the users with convenient tools for conducting experimental and theoretical studies (eg.…”

Section: Introductionmentioning

confidence: 99%

Interactive system for geomagnetic data analysis

Solovev

2017

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Interactive system for geomagnetic data analysis

Solovev

2017

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Wavelet transformations have the ability to analyse the time series with a complex structure (Chui 1992;Daubechies 1992;Mallat 1999). Wavelet transform can be used to determine and detect the anomalies in ionospheric parameters (Mandrikova et al 2014(Mandrikova et al , 2015a. The multiscale analysis uses Continuous wavelet transform (CWT) to register the local characteristics in the complex structures of ionospheric TEC.…”

Section: Introductionmentioning

confidence: 99%

Detection of ionospheric anomalies during intense space weather over a low-latitude GNSS station

et al. 2016

View full text Add to dashboard Cite

The operational availability of Global Navigation Satellite System is affected by large-scale irregularities of the ionosphere. The space weather events induce several intense irregularities and cause the non-linear distribution of ionospheric electron density. Monitoring of ionospheric responses due to extreme space weather events plays a key role in trans-ionospheric radio wave propagation. In the present analysis, a novel technique based on wavelet transform has been implemented for the analysis and detection of ionospheric anomalies during two intense space weather events that occurred in 2013. The investigations have been carried out using the ionospheric observable, Total Electron Content (TEC), derived from the Global Positioning System (GPS) receiver located at an Equatorial Ionisation Anomaly region, KL University, Guntur, India (Geographic Lat.16.37°N, Geographic Long. 80.37°E). The effects of geomagnetic storms (Sym-H B-100 nT) on the perturbations of ionospheric TEC have been investigated. The algorithm of Continuous wavelet transform has been used to study and characterise the presence of ionospheric anomalies in the local timescale plane. It can detect spatial and temporal details of ionospheric anomaly intensity during strong solar-terrestrial and geophysical events. It is observed that during the main phase of the geomagnetic storm that occurred during 17 March 2013, TEC enhanced by 7 TECU, while a suppression of 10 TECU in the GPS-TEC can be noticed during the main phase of the 29 June 2013 storm. The variation in the intensity of ionospheric TEC anomalies during storm time has been detected and compared to the intensity of the space weather events measured through solar and geomagnetic indices (F10.7, Sym-H, IMF Bz and IEF Ey).

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%