Analysis of ionospheric TEC from GPS, GIM and global ionosphere models during moderate, strong, and extreme geomagnetic storms over Indian region

Abstract

In this paper, we investigate the Global Positioning System (GPS) derived ionospheric Total Electron Content (TEC) response to different levels of geomagnetic storms over the Indian region during the solar cycle-24. The corresponding storm-time effectiveness of the Global Ionosphere Maps (GIMs), International Reference Ionosphere (IRI-2016), and its plasmaspheric extension Standard Plasmasphere Ionosphere Model (SPIM-2017/IRI-Plas 2017) is also probed by validating with the GPS-TEC observations. The results show that in spite of favorable prompt response of GIM-TEC to the storm commencement, there is a varied uncertainty in GIM-TEC with locations, which could be due to the sparse regional coverage of GPS stations and high ionospheric density gradients. Concerning IRI-2016 and SPIM 2017/IRI-Plas 2017, both the models failed to exhibit any visible alterations in TEC with the storm onset. However, it is very surprising to notice sporadically slight flipping magnitudes in the IRI-2016 prediction but in the opposite direction to that of GPS-TEC and GIM-TEC during two storm events (28 June to 1 July 2013 and 22–25 June 2015) under the summer solstice months. Such type of abnormality is possibly due to the limited database of the underlying storm sub-model in IRI and hence tempts towards for further refinement aspects. However, the estimated TEC from SPIM-2017/IRI-Plas 2017 overestimated the observed TEC irrespective of event type and location of observation, suspected to be due to the ever-arguing shortcomings of basement IRI model across low latitudes and the apparent high latitude characteristics of the Standard Model of Ionosphere (SMI) fitted to it on the top. The other significant observation in our study is the remarkable improvement in the SPIM-TEC predictions from regular model run to the post-ingestion of GPS-TEC into the model as an optional input parameter, with the predicted TEC effectively coinciding with the variation of observed TEC. This substantiates the idea of plausible improvement in the SPIM model configuration with external data ingestion and optimizations. Further, an efficient readjustment of the altitude dependent parameter would augment the reliable predictability of TEC over the region.