Paper
1 September 2006 Using historic models of Cn2 to predict r0 and regimes affected by atmospheric turbulence for horizontal, slant, and topological paths
Janice K. Lawson, Carmen J. Carrano
Author Affiliations +
Abstract
Image data collected near the ground, in the boundary layer, or from low altitude planes must contend with the detrimental effects of atmospheric turbulence on the image quality. So it is useful to predict operating regimes (wavelength, height of target, height of detector, total path distance, day vs. night viewing, etc.) where atmospheric turbulence is expected to play a significant role in image degradation. In these regimes, image enhancement techniques such as speckle processing, deconvolution and Wiener filtering methods can be utilized to recover near instrument-limited resolution in degraded images. We conducted a literature survey of various boundary layer and lower troposphere models for the structure coefficient of the index of refraction (Cn2). Using these models, we constructed a spreadsheet tool to estimate the Fried parameter (r0) for different scenarios, including slant and horizontal path trajectories. We also created a tool for scenarios where the height along the path crudely accounted for the topology of the path. This would be of particular interest in mountain-based viewing platforms surveying ground targets. The tools that we developed utilized Visual Basic© programming in an Excel© spreadsheet environment for accessibility and ease of use. In this paper, we will discuss the Cn2 profile models used, describe the tools developed and compare the results obtained for the Fried parameter with those estimated from experimental data.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Janice K. Lawson and Carmen J. Carrano "Using historic models of Cn2 to predict r0 and regimes affected by atmospheric turbulence for horizontal, slant, and topological paths", Proc. SPIE 6303, Atmospheric Optical Modeling, Measurement, and Simulation II, 630304 (1 September 2006); https://doi.org/10.1117/12.679108
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Cited by 5 scholarly publications.
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KEYWORDS
Data modeling

Atmospheric modeling

Atmospheric turbulence

Mid-IR

Turbulence

Short wave infrared radiation

Submerged target modeling

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