Open Access
10 March 2020 Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning
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Abstract

We develop and study two approaches for the prediction of optical refraction effects in the lower atmosphere. Refraction can cause apparent displacement or distortion of targets when viewed by imaging systems or produce steering when propagating laser beams. Low-cost, time-lapse camera systems were deployed at two locations in New Mexico to measure image displacements of mountain ridge targets due to atmospheric refraction as a function of time. Measurements for selected days were compared with image displacement predictions provided by (1) a ray-tracing evaluation of numerical weather prediction data and (2) a machine learning algorithm with measured meteorological values as inputs. The model approaches are described and the target displacement prediction results for both were found to be consistent with the field imagery in overall amplitude and phase. However, short time variations in the experimental results were not captured by the predictions where sampling limitations and uncaptured localized events were factors.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Wardeh Al-Younis, Christina Nevarez, Mohammad Abdullah-Al-Mamun, Steven Sandoval, Sukanta Basu, and David Voelz "Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning," Optical Engineering 59(8), 081803 (10 March 2020). https://doi.org/10.1117/1.OE.59.8.081803
Received: 3 December 2019; Accepted: 18 February 2020; Published: 10 March 2020
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Atmospheric modeling

Refraction

Cameras

Data modeling

Machine learning

Refractive index

Atmospheric propagation

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