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

Abstract

Abstract 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, and 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 (PSFs) 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 to the atmosphere, and a PSF wing due to water droplets. We use a common atmosphere representation framework to self-consistently model all phenomena by simulating individual photons. We reproduce the well-known correlations in image characteristics: correlations in altitude with absolute photometry (overall transmission) and relative photometry (spectrally dependent transmission), anticorrelations of altitude with differential astrometry (nonideal astrometric patterns) and background levels, and an anticorrelation in absolute photometry with cloud depth. However, we also find further subtle correlations including an anticorrelation of temperature with background and differential astrometry, a correlation of temperature with absolute and relative photometry, an anticorrelation of absolute photometry with humidity, a correlation of humidity with lunar background, a significant correlation of PSF wing with cloud depth, an anticorrelation of background with cloud depth, and a correlation of lunar background with cloud depth.