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Validation of NeQuick 2 Model Topside Ionosphere and Plasmasphere Electron Content Using COSMIC POD TEC
Author(s) -
Kashcheyev A.,
Nava B.
Publication year - 2019
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1029/2019ja026971
Subject(s) - tec , total electron content , plasmasphere , ionosphere , international reference ionosphere , environmental science , local time , earth's magnetic field , gnss applications , atmospheric sciences , physics , remote sensing , geophysics , geology , satellite , mathematics , plasma , magnetosphere , astronomy , statistics , quantum mechanics , magnetic field
Abstract An accurate representation of the topside ionosphere and plasmasphere electron content is still one of the important issues for ionospheric models, particularly for those used in GNSS applications. In this work, a validation of NeQuick 2 empirical ionospheric model has been performed in terms of ionosphere topside and plasmasphere electron content in the altitude range ~800–20,200 km. For this purpose, total electron content (TEC) data derived from precise orbit determination (POD) antennas onboard COSMIC low earth orbit (LEO) satellites tracking GPS signals have been used. The data span the period from 2006 to 2018 and correspond to different heliogeophysical conditions. In order to remove unrealistic TEC values from the validation process, a specific filtering procedure has been applied to POD‐derived TEC data. Subsequently, the statistical analysis of the difference between the modeled and the corresponding experimentally derived TEC values has been performed on data obtained during geomagnetically quiet periods. The results are presented as a function of solar activity level, season, local time, and geomagnetic latitude plots. They show that NeQuick 2 model underestimates the ionosphere topside and plasmasphere electron content, particularly during the early morning hours. The conditions under which the discrepancy between the model and experimental data is the highest have been also identified and considered as indications for future model improvements.