A Contribution to Short-Term Rapidly Developing Geomagnetic Storm Classification for GNSS Ionosphere Effects Mitigation Model Development

The Global Navigation Satellite System (GNSS) resilience against adverse space weather effects has become the major research topic, as satellite navigation evolves to an essential component of national infrastructure, and the enabling technology of a growing number of technology and socio-economic applications (systems and services). Ionospheric effects have been identified as the prime single cause of the GNSS positioning performance degradation, thus placing mitigation of the ionospheric effects on the GNSS positioning performance into focus of research worldwide. Classification of scenarios of ionospheric disturbances provides an essential framework for development of the GNSS ionospheric effects prediction model. Conventional approach involves experimental and atmospheric-physics- based classification approaches, which frequently fail in reflection to the GNSS positioning performance sustainability. Here the results of the analysis of the GPS pseudo-range-derived Total Electron Content (TEC) times series, taken in selected recent cases of the short-term and rapidly developing geomagnetic storms, are presented. Particular scenarios are selected for their impact on GNSS positioning performance, their nature, and the risk of not being taken into account by existing generalised global models for GNSS ionospheric effects correction. The research identifies similarities and diversities in time series characterisation. As the inference and conclusion, a set of the time series characterisation indices is proposed as the classification elements for the purpose of the scenario identification, and development and application of the most suitable experimental statistical learning GNSS ionospheric effects prediction models. The proposed classification approach may replace the conventional classification methods, such as the NOAA Space Weather Scales, allowing for development of adaptive, and more accurate and direct GNSS ionospheric effects prediction models.