  We present a self-consistent representation of the atmosphere and implement
the interactions of light with the atmosphere using a photon Monte Carlo
approach. We compile global climate distributions based on historical data,
self-consistent vertical profiles of thermodynamic quantities, spatial models
of cloud variation and cover, and global distributions of four kinds of
aerosols. We then implement refraction, Rayleigh scattering, molecular
interactions, Tyndall-Mie scattering to all photons emitted from astronomical
sources and various background components using physics first principles. This
results in emergent image properties that include: differential astrometry and
elliptical point spread functions predicted completely to the horizon,
arcminute-scale spatial-dependent photometry variations at 20 mmag for short
exposures, excess background spatial variations at 0.2% due the atmosphere, and
a point spread function wing due to water droplets. We reproduce the well-known
correlations in image characteristics: correlations in altitude with absolute
photometry (overall transmission) and relative photometry (spectrally-dependent
transmission), anti-correlations of altitude with differential astrometry
(non-ideal astrometric patterns) and background levels, and an anti-correlation
in absolute photometry with cloud depth. However, we also find further subtle
correlations including an anti-correlation of temperature with background and
differential astrometry, a correlation of temperature with absolute and
relative photometry, an anti-correlation of absolute photometry with humidity,
a correlation of humidity with Lunar background, a significant correlation of
PSF wing with cloud depth, an anti-correlation of background with cloud depth,
and a correlation of lunar background with cloud depth.
