Extended analysis of atmospheric refraction effects captured by time-lapse imaging: long-term trends and machine learning image shift prediction

Abstract

This work presents an extended analysis of atmospheric refraction effects captured by time-lapse imagery for near-ground and near-horizontal paths. Monthly trends and multipath analysis of image shift caused by refraction during daytime are studied. Nighttime shift measurements during moonlit nights are also presented. Advanced nonlinear machine learning approaches for image shift prediction are implemented and the performance of the models is evaluated.