Accurate Quantification of Ionospheric State Based on Comprehensive Radiative Transfer Modeling and Optimal Inversion of the OI 135.6-nm Emission

Abstract:

Remote sensing of the nighttime OI 135.6-nm emission represents the primary means of quantifying the F-region ionospheric state from optical measurements. Despite its pervasive use for studying aeronomical processes, the interpretation of these emissions as a proxy for ionospheric state remains ambiguous in that the relative contributions of radiative recombination and mutual neutralization to the production and, especially, the effects of scattering and absorption on the transport of the 135.6-nm emissions have not been fully quantified. Moreover, an inversion algorithm, which is robust to varying ionospheric structures under different geophysical conditions, is yet to be developed for statistically optimal characterization of the ionospheric state. In this work, as part of the NASA ICON mission, we develop a comprehensive radiative transfer model from first principle to investigate the production and transport of the nighttime 135.6-nm emissions. The forward modeling investigation indicates that under certain conditions mutual neutralization can contribute up to ~38% to the 135.6-nm emissions. Moreover, resonant scattering and pure absorption can reduce the brightness observed in the limb direction by ~40% while enhancing the brightness in the nadir direction by ~25%. Further analysis shows that without properly addressing these effects in the inversion process, the peak electron density in the F-region ionosphere (NmF2) can be overestimated by up to ~24%. To address these issues, an inversion algorithm that properly accounts for the above-mentioned effects is proposed for accurate quantification of the ionospheric state using satellite measurements. The ill-posedness due to the intrinsic presence of noise in real data is coped with by incorporating proper regularizations that enforce either global smoothness or piecewise smoothness of the solution. Application to model-generated data with different signal-to-noise ratios show that the algorithm has achieved unprecedented accuracy in estimating the electron density, with only a few percentage errors in Nmf2 and a few kilometer differences in hmf2. The inversion is also demonstrated to be robust to different geophysical conditions, in which the electron density profile can be distinctly different from a smooth Chapman profile.