Work

Predicting atmospheric refraction with weather modeling and machine learning

Year: 2019

Type: conference-paper

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.

Source: Laser Communication and Propagation through the Atmosphere and Oceans

DOI: 10.1117/12.2529533

Authors: Wardeh Al-Younis, Christina Nevarez, David Voelz, Steven Sandoval, Sukanta Basu

Institutions: New Mexico State University, Delft University of Technology

Language: English

Topic: Radio Wave Propagation Studies

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: bronze

Funder: New Mexico State University

OpenAlex ID: W2971640468