  The paper presents experiments of driving a physics-based thermosphere model
by assimilating electron density (Ne) and temperature (Tn) data using the
ensemble adjustment Kalman filter (EAKF) technique. This study not only helps
to gauge the accuracy of the assimilation, to explain the inherent model bias,
and to understand the limitations of the framework, but it also establishes
EAKF as a viable technique in the presence of realistic data assimilation
scenarios to forecast the highly dynamical thermosphere. The results from
perfect model scenarios show that data assimilation changes and, more often
than not, improves the model state. Data from Swarm-A, Swarm-C, CHAMP, and
GRACE-A are used to validate the resulting analysis states. The independent
validation results show that the Ne-guided thermosphere state does not
outperform the model state without data assimilation along the considered
orbits. This may be due to the limited number of bonafide Ne profiles available
for the thermosphere specification tasks in the experiments. More importantly,
the results show that the Ne-guided thermosphere state does not deteriorate
much in performance during geomagnetic storm time. The results reveal a few
challenges of using Ne profiles in a hypothetical operational data assimilation
exercise. The experiment with assimilating Tn shows more promise over Ne in
terms of estimating mass density along the orbits of both CHAMP and GRACE-A
satellites. The results show that the improvement gained in the overall
forecasted thermosphere state is better during solar minimum compared to that
of solar maximum. These results also provide insights into the biases inherent
in the physics-based model. The systematic biases that the paper highlight
could be an indication that the specification of plasma-neutral interactions in
the model needs further adjustments.
