@inproceedings{3795660b875046f5a4507c3e8dfce501,
title = "Predicting atmospheric refraction with weather modeling and machine learning",
abstract = "This work details the analysis of time-lapse images with a point-tracking image processing approach along with the use of an extensive numerical weather model to investigate image displacement due to refraction. The model is applied to create refractive profile estimates along the optical path for the days of interest. Ray trace analysis through the model profiles is performed and comparisons are made with the measured displacement results. Additionally, a supervised machine learning algorithm is used to build a predictive model to estimate the apparent displacement of an object, based on a set of measured metrological values taken in the vicinity of the camera. The predicted results again are compared with the field-imagery ones.",
keywords = "Atmospheric refraction, Machine learning algorithms, Remote mobile station, Time-lapse imaging, Weather modeling",
author = "Wardeh Al-Younis and Christina Nevarez and David Voelz and Steven Sandoval and Sukanta Basu",
year = "2019",
doi = "10.1117/12.2529533",
language = "English",
volume = "11133",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
pages = "1--8",
editor = "Bos, {Jeremy P.} and {van Eijk}, {Alexander M. J.} and Stephen Hammel",
booktitle = "Laser Communication and Propagation through the Atmosphere and Oceans VIII",
address = "United States",
note = "Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019 ; Conference date: 13-08-2019 Through 15-08-2019",
}