Taylor’s Frozen Turbulence Hypothesis (TFTH) has been used extensively in theoretical studies to model the temporal fluctuations of optical quantities affected by atmospheric turbulence. It has been relied upon to provide temporal-frequency spectra under varying propagation conditions and for different atmospheric refractive index models. However, experimental works have revealed its limitations, such as systematic inaccuracies in estimating cross winds during calm nights in scintillation measurements at astronomical sites, scintillation discrepancies in ground-layer measurements, and broad estimates of the coherence time in phase fluctuation measurement techniques. This highlights the need to recognize the limitations of the TFTH and seek alternatives that can provide a more reliable description of atmospheric turbulence’s temporal fluctuations. Here, we propose a spatio-temporal statistics for refractive index fluctuations through fluid dynamics models and evaluate the complex phase propagation under weak turbulence. Then, we test its ability to reproduce experimental observations under different ground-layer turbulence conditions.
In the last decade, a nascent trend of characterizing turbulence from observing features of distant targets through ground-layer turbulence have been relentless growing. Either from observing regular geometrical features of buildings or arrays of LEDs, it is possible to retrieve the structure constant of the refractive index fluctuations. On the other hand, because of the lack of a definitive theoretical model describing anisotropic or inhomogeneous turbulence, most experimental observations have been reduced to mere descriptions in the event of deviations from expected Obukhov-Kolmogorov predictions. Our group has been able to retrieve power-spectrum exponents, without a prior knowledge of a subjacent model, and henceforth determine anisotropic behavior in controlled optical turbulence; furthermore, under convective turbulence, an exponent can be obtained from time series of the occurrence of power drops in optical communication links: extreme events.
In this manuscript, we present a technique identifying as extreme events sudden changes in morphological characteristics of an array of point sources observed through real controlled anisotropic turbulence assisted by a deep-learning ad-hoc. This approach provides an effective approach to reduce high-volume data from imaging targets into a real-time stream of parameters to fully characterize optical turbulence.
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