Towards a Reliable Ionospheric Polar Patch Climatology With Ground-Based GNSS

We present a new algorithm of ionospheric patch detection with ground-based GNSS data and its application to statistical investigations of polar activity in the northern hemisphere. The patch detection is performed with relative STEC values derived from a time series of geometry-free linear combination of GNSS signals. The approach allows distinguishing between the structures and monitoring polar ionosphere with a high temporal resolution. The results, covering the period between January and May 2014, demonstrate the effectiveness of the proposed methodology and show good agreement with ISR and SWARM datasets. Furthermore, a strong seasonal dependence of polar activity characterized simultaneously by a mutable daily pattern was proven. During the winter months, the minimum and maximum of patch occurrence correspond to pre- and postnoon UT hours, whereas in March the pattern is reversed. This modification is fully explained by a corresponding variation of the ionospheric gradient between subauroral and polar regions. The support of this parameter also clarifies the seasonal effects in patch occurrence as well as the strength of structures observed for particular months. Accordingly, the most severe patches correlate well with the highest ionospheric gradient near the spring equinox, while the suppressed polar activity observed in May 2014 corresponds to the opposite conditions. We also confirmed a strong dependence of patch occurrence on IMF Bz and a close relationship between the enhancement of structures observed during southward orientation and ionospheric gradient.

Automated summary

This paper presents a new algorithm for detecting ionospheric patches using ground-based GNSS data and applies it to statistical investigations of polar activity in the northern hemisphere. The results show the effectiveness of the proposed methodology and its agreement with other datasets. Additionally, the paper confirms the seasonal and daily pattern variations in patch occurrence.