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

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 a-priori 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.

Keywords

• Ionospheric parameters
• Perturbation
• Earthquake
• Anomaly
• Prediction
• Wavelet transform
• Autoregressive integrated moving average model
• Critical frequency data
• Total electron content data