Figure 1: Impact of non-uniform spatial sampling – schematic representation (not to scale). (Top) Spatially homogeneous ionosphere at times t1 to tn. Increase in the intensity of the color represents increasing concentration of TEC at a constant rate. TEC is sampled along track A at IPPs (black dots marked as A1 to An) placed at uniform interval of time and space. Along the track A’ the TEC is sampled (green diamonds marked as A’1 to A’n) at non-uniform spatial intervals and uniform time intervals. Inter-IPP distances along track A (d1=d2=d3…=dn=d) are uniform; but along the track A’ (d’1≠d’2≠d’3.≠d’n) are non-uniform. (Bottom) Spatiotemporal gradient along the track A (black) and A’ (green) plotted as a function of inter-IPP distances.
Considering the growing importance of ionospheric perturbations forced from below the ionosphere and its multifaceted applications, the accurate detection of ionospheric perturbations and robust determination of its characteristics using GPS are essential. However, to the best of our knowledge, there is no study available in the published literature analyzing the aliases, artifacts, and methodology dependent errors in computing the ionospheric perturbations using GPS. In this study, for the first time, we analyze the aliases and artifacts present in the tsunami and earthquake induced ionospheric perturbations obtained using conventional methods, and show that adopting Spatio-Periodic Leveling Algorithm (SPLA) proposed by Shimna and Vijayan (2020) is efficient in obtaining aliasing and artifact free TIPs and CIPs irrespective of the elevation angles. We also demonstrate that SPLA removes the necessity of applying elevation cut-offs.
We use differential and residual methods to demonstrate the impact of aliasing and artifacts for brevity, though the problem of aliasing and artifacts are common to all the conventional methods including the ones which directly filters the TIP (Manta et al., 2020) or CIP from the TEC time series using a frequency filter. Further, we validate the efficiency of SPLA by testing the algorithm under two theoretically simulated scenarios (section 5), and using GPS observations carried out during the 2004 Indian Ocean tsunami and 2015 Nepal-Gorkha earthquake (section 6). Further, we quantify the improvement in characteristics viz. Phase, frequency, and Signal-to-Noise Ratio (SNR) of the TIPs and CIPs upon removing aliases and artifacts caused by the non-uniform spatial sampling (section 7).