697 Comparison of Atmospheric Refractive Index Gradient Variations Derived from Time-lapse Photography to Mesoscale Modeling and Radar Measurements

Wednesday, 13 January 2016
New Orleans Ernest N. Morial Convention Center
Santasri Basu, Air Force Institute of Technology, Wright-Patterson AFB, OH; and J. McCrae, S. Fiorino, L. Burchett, and C. Rice

Handout (3.7 MB)

A time-lapse imaging experiment was conducted to monitor the effects of the atmosphere over some period of time. A tripod-mounted digital camera captured images of a distant building every minute. Correlation techniques were used to calculate the position shifts between the images. Two factors causing shifts between the images are: atmospheric turbulence, causing the images to move randomly and quickly, plus changes in the average refractive index gradient along the path which cause the images to move vertically, more slowly and perhaps in noticeable correlation with solar heating and other weather conditions. The amount of refractive bending in the path due to the temperature and density structure of the atmosphere along the viewing path, and a technique for estimating the path averaged index of refraction structure constant, Cn2, from the random component of the image motion is presented here. The Cn2 technique uses a derived set of weighting functions that depend on the size of the imaging aperture and the patch size in the image whose motion is being tracked. Since this technique is phase-based, it can be applied to strong turbulence paths where traditional irradiance based techniques suffer from saturation effects. This light-based quantification of the amount of refractive bending and turbulence along the viewing path is applied as a ground-truth measurement of refractive bending and turbulence for comparison to derived quantification methods such as refractive bending estimates from temperature and moisture gradients, and turbulence inferred from scintillometer measurements. Comparisons are made to turbulence estimates made with weather radar, those derived from nested mesoscale numerical weather models.
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