Detecting ionospheric disturbances using GPS without aliasing caused by non-uniform spatial sampling: Algorithm, validation and illustration

Abstract

Ionospheric disturbance (ID) detected using GPS based Total Electron Content (TEC) measurements are widely used to study the morphology and dynamics of the ionosphere, and its impact on radio communication and satellite based navigation. The IDs normally derived as rate of change of TEC (ROT) between consecutive ionospheric pierce points at uniform time interval implicitly results in non-uniform spatial sampling along the GPS satellite tracks. The non-uniform spatial sampling introduces aliasing in ROT. These aliasing corrupt amplitude and Signal-to-Noise Ratio (SNR) of the detected IDs. In this study, we propose a Spatio-Periodic Leveling Algorithm (SPLA) to remove such aliasing. Efficiency of the proposed algorithm was tested by simulating the IDs along a satellite track and validated with GPS observations carried out during 2015 St. Patrick's day geomagnetic storm. Spatiotemporal, and SNR analyses of simulated and observed IDs reveal that the SPLA is (i) efficient in removing the aliases, (ii) increases the SNR on an average of 99.5% compared to ROT, (iii) removes the need of applying elevation cut-off, and (iv) expands the area of coverage up to 65%.