Validation of E‐Region Model Electron Density Profiles With AURIC Utilizing High‐Resolution Cross Sections

Abstract

AbstractE‐region models have traditionally underestimated the ionospheric electron density. We believe that this deficiency can be remedied by using high‐resolution photoabsorption and photoionization cross sections in the models. Deep dips in the cross sections allow solar radiation to penetrate deeper into the E‐region producing additional ionization. To validate our concept, we perform a study of model electron density profiles (EDPs) calculated using the Atmospheric Ultraviolet Radiance Integrated Code (AURIC; D. Strickland et al., 1999, https://doi.org/10.1016/s0022-4073(98)00098-3) in the E‐region of the terrestrial ionosphere. We compare AURIC model outputs using new high‐resolution photoionization and photoabsorption cross sections, and solar spectral irradiances during low solar activity with incoherent scatter radar (ISR) measurements from the Arecibo and Millstone Hills observatories, Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC‐1) observations, and outputs from empirical models (IRI‐2016 and FIRI‐2018). AURIC results utilizing the new high‐resolution cross sections reveal a significant difference to model outputs calculated with the low‐resolution cross sections currently used. Analysis of AURIC EDPs using the new high‐resolution data indicate fair agreement with ISR measurements obtained at various times at Arecibo but very good agreement with Millstone Hills ISR observations from ∼96–140 km. However, discrepancies in the altitude of the E‐region peak persist. High‐resolution AURIC calculations are in agreement with COSMIC‐1 observations and IRI‐2016 model outputs between ∼105 and 140 km while FIRI‐2018 outputs underestimate the EDP in this region. Overall, AURIC modeling shows increased E‐region electron densities when utilizing high‐resolution cross sections and high‐resolution solar irradiances, and are likely to be the key to resolving the long standing data‐model discrepancies.