Statistical properties of mid-latitude TEC time series observed during rapidly developing short-term geomagnetic storms: A contribution to GNSS-related TEC predictive model development

Abstract

Total Electron Content (TEC) affects GNSS positioning accuracy due to its effects on GNSS pseudorange measurement. GNSS resilience against the ionospheric effects requires improved accuracy of TEC predictive model. Here a contribution to the subject of self-adaptive positioning environment-aware TEC predictive model development is provided trough statistical analysis of mid-latitude TEC time series observed during rapidly developing short-term geomagnetic storms. Statistical properties of TEC sets and time series are examined to address similarities in range, variability, and information content in order to establish rapidly developing short-term geomagnetic storms as a separate class of the ionospheric event cases, with potential to degrade GNSS positioning accuracy. Results of the analysis show cases of rapidly developing short-term geomagnetic storm share similar statistical properties, notably Shannon entropy and spike index, of TEC observations, which renders them eligible to be addressed with a common TEC prediction model to rise GNSS resilience against the ionospheric effects.