  The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has
been developed and improved since the early 1970's. The most recent version of
MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric
model. NRLMSIS 2.0 provides species density, mass density, and temperature
estimates as function of location and space weather conditions. MSIS models
have long been a popular choice of atmosphere model in the research and
operations community alike, but - like many models - does not provide
uncertainty estimates. In this work, we develop an exospheric temperature model
based in machine learning (ML) that can be used with NRLMSIS 2.0 to calibrate
it relative to high-fidelity satellite density estimates. Instead of providing
point estimates, our model (called MSIS-UQ) outputs a distribution which is
assessed using a metric called the calibration error score. We show that
MSIS-UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and
satellite density of 25% and is 11% closer to satellite density than the Space
Force's High Accuracy Satellite Drag Model. We also show the model's
uncertainty estimation capabilities by generating altitude profiles for species
density, mass density, and temperature. This explicitly demonstrates how
exospheric temperature probabilities affect density and temperature profiles
within NRLMSIS 2.0. Another study displays improved post-storm overcooling
capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can
capture.
