Image shift due to atmospheric refraction: Prediction by numerical weather modeling and machine learning

Wardeh Al-Younis, Christina Nevarez, Mohammad Abdullah-Al-Mamun, Steven Sandoval, Sukanta Basu, David Voelz

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number081803
Number of pages11
JournalOptical Engineering
Volume59
Issue number8
DOIs
Publication statusPublished - 2020

Keywords

  • atmospheric refraction
  • machine learning algorithms
  • remote mobile station
  • time-lapse imaging
  • weather modeling

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