Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models

Oksana V Mandrikova Oksana V Mandrikova,Riad Taha Al-Kasasbeh Riad Taha Al-Kasasbeh,Vladimir V Geppener Vladimir V Geppener,Dmitry M Klionskiy Dmitry M Klionskiy,Nadezda V Fetisova (Glushkova) Nadezda V Fetisova (Glushkova),Maksim Y Ilyash Maksim Y Ilyash

doi:10.4401/ag-6729

Abstract

The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their <em>a-priori</em> uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models). This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC) datasets from Kamchatka (a region in the Russian Far East) and Magadan (a town in the Russian Far East). The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka.

Highlights

One of the most important tasks of ionospheric data analysis is connected with ionospheric state control along with anomaly discovery with subsequent interpretation of anomalies occurring during ionospheric perturbations [Afraimovich et al 2000, Afraimovich et al 2001, Nakamura et al 2007, Pervak et al 2008, Graphical abstract
Multicomponent time series models of ionospheric critical frequency frequency of F2-layer of the ionosphere (foF2) and total electron content (TEC) [Afraimovich et al 2000, Afraimovich et al 2001, Erdoğan and Arslan 2009] in Kamchatka and Magadan were constructed
The suggested technique of ionospheric parameter simulation and analysis based on combining the wavelet transform with ARIMA models was used to approximate trend in critical frequency data from Kamchatka and Magadan

Summary

Introduction

One of the most important tasks of ionospheric data analysis is connected with ionospheric state control along with anomaly discovery with subsequent interpretation of anomalies occurring during ionospheric perturbations [Afraimovich et al 2000, Afraimovich et al 2001, Nakamura et al 2007, Pervak et al 2008, Graphical abstract. Multicomponent time series models of ionospheric critical frequency foF2 (data were registered by the University of Cosmophysical Research and Radio Wave Propagation, Paratunka village, Kamchatka region, Russian Far East) and TEC (data were obtained from bi-frequency ground-based GPS receivers) [Afraimovich et al 2000, Afraimovich et al 2001, Erdoğan and Arslan 2009] in Kamchatka and Magadan were constructed. They confirmed the efficiency of the suggested technique and made it possible for scientists to analyze regular diurnal and seasonal parameter changes. Our detailed analysis has shown that the anomalies described above occur during the increased solar activity and strong earthquakes in Kamchatka (several seismic events of energy class k > 12.5 have been analyzed to make this conclusion)

Suggested technique

Data prediction and anomaly discovery

Conclusions