At altitudes below about 600 km, satellite drag is one of the most
important and variable forces acting on a satellite. Neutral mass
density predictions in the upper atmosphere are therefore critical for
(1) designing satellites; (2) performing adjustments to stay in an
intended orbit; and (3) collision avoidance maneuver planning. Density
predictions have a great deal of uncertainty, including model biases and
model misrepresentation of the atmospheric response to energy input.
These may stem from inaccurate approximations of terms in the
Navier-Stokes equations, unmodeled physics, incorrect boundary
conditions, or incorrect parameterizations. Two commonly parameterized
source terms are the thermal conduction and eddy diffusion. Both are
critical components in the transfer of the heat in the thermosphere.
Determining how well the major constituents ($N_2$, $O_2$, $O$)
are as heat conductors will have effects on the temperature and mass
density changes from a heat source. This work shows the effectiveness of
using the retrospective cost model refinement (RCMR) technique at
removing model bias caused by different sources within the Global
Ionosphere Thermosphere Model (GITM). Numerical experiments, Challenging
Minisatellite Payload (CHAMP) and Gravity Recovery and Climate
Experiment (GRACE) data during real events are used to show that RCMR
can compensate for model bias caused by both inaccurate
parameterizations and drivers. RCMR is used to show that eliminating
model bias before a storm allows for more accurate predictions
throughout the storm.