Self-Consistent Atmosphere Representation and Interaction in Photon Monte Carlo Simulations

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.