Predicting atmospheric refraction with weather modeling and machine learning

Wardeh Al-Younis; Christina Nevarez; David Voelz; Steven Sandoval; Sukanta Basu

doi:10.1117/12.2529533

6 September 2019 Predicting atmospheric refraction with weather modeling and machine learning

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

Proceedings Volume 11133, Laser Communication and Propagation through the Atmosphere and Oceans VIII; 111330E (2019) https://doi.org/10.1117/12.2529533
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Wardeh Al-Younis, Christina Nevarez, David Voelz, Steven Sandoval, and Sukanta Basu "Predicting atmospheric refraction with weather modeling and machine learning", Proc. SPIE 11133, Laser Communication and Propagation through the Atmosphere and Oceans VIII, 111330E (6 September 2019); https://doi.org/10.1117/12.2529533

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