Abstract
This machine learning project is part of ongoing longitudinal long distance atmospheric acoustic propagation research being conducted at the East Carolina University Outer Banks campus in Wanchese, NC. The overall project seeks to connect changes in the atmosphere by taking concurrent acoustic and meteorological readings and relating them to differences in sound propagation. Wide angle images of the sky are used to correlate cloud cover with concurrent near-surface temperature gradient measurements. The camera is mounted on a mast that houses the temperature logger array. Images are imported, segmented, and labeled in MATLAB and used as both a training and test image set. The results of this project enable more accurate characterization of cloud cover. This information supplements knowledge of heat flux between ground and the atmosphere which in turn supports improved modeling of long distance sound propagation.
Published Version
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