Turbulence spectral models and their effect on anisoplanatic error

In problems involving optical wave propagation in the atmosphere it is common to assume turbulence statistics obey a Kolmogorov power spectral density model. It is well known that this model has a key weakness in that it does not model the outer scale. Also, some observations indicate a deviation from the Kolmogorv -11/3 power law often referred to as non-Kolmogorov turbulence. Understanding the effect of a change in power law has been a difficulty for some researchers and most assume the turbulence volume is homogeneous, which is unlikely. In this work, I describe how anisoplanatic error can be described for inhomogeneous turbulence using the mean turbulence height and the generalized Fried parameter. I also consider a two scale outer scale model and describe its effect compared to the single parameter von Karman model.

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Published
2024-10-03
DOI
https://doi.org/10.1117/12.3028224
Primary Topic
Statistical and numerical algorithms
Type
article
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article

Turbulence spectral models and their effect on anisoplanatic error

Jeremy P. Bos
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Statistical and numerical algorithms
article

Turbulence spectral models and their effect on anisoplanatic error

Jeremy P. Bos
article en

Abstract

In problems involving optical wave propagation in the atmosphere it is common to assume turbulence statistics obey a Kolmogorov power spectral density model. It is well known that this model has a key weakness in that it does not model the outer scale. Also, some observations indicate a deviation from the Kolmogorv -11/3 power law often referred to as non-Kolmogorov turbulence. Understanding the effect of a change in power law has been a difficulty for some researchers and most assume the turbulence volume is homogeneous, which is unlikely. In this work, I describe how anisoplanatic error can be described for inhomogeneous turbulence using the mean turbulence height and the generalized Fried parameter. I also consider a two scale outer scale model and describe its effect compared to the single parameter von Karman model.

Michigan Technological University (US)
Climate action
Openalex Percentile: Top 71%
Statistical and numerical algorithms
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