Adaptive Estimation of Thermal Conductivity Coefficients in the Global Ionosphere Thermosphere Model

Neutral mass density predictions have a great deal of uncertainty when calculated using models, including model biases, that may be caused by unmodeled physics, incorrect boundary conditions, incorrect model parameterizations, or other effects, and model misrepresentation of the atmospheric response to energy input, which may also be caused by a wide variety of issues. Many physics-based models are not able to simulate seasonal variations in the globally averaged thermospheric mass density at a given altitude or the densities during strong magnetospheric driving conditions. This may stem from inaccurate approximations in source terms in the Navier-Stokes equations or incorrect parameterizations. This work uses a retrospective cost model refinement (RCMR) technique to estimate the thermospheric thermal conductivity coefficients based on Challenging Minisatellite Payload (CHAMP) data. The goal is to validate this adaptive estimation technique in the Global Ionosphere Thermosphere Model (GITM), use it to solve for thermal conductivity coefficients, and understand the effect of thermal conductivity during quiet and storm time periods.