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A Heuristic Approach to Defining the Structure Parameter of the Refractive Index of the Atmosphere from Turbulent Lidar Data

  • OPTICS OF STOCHASTICALLY-HETEROGENEOUS MEDIA
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Abstract

Applicability of the existing theoretical approach to solving the inverse problem of retrieving optical turbulence parameters from lidar data is experimentally studied. It is shown that calculations by a theoretical formula relating the echo signal to the intensity of turbulent pulsations of the air refractive index for the case of statistically homogeneous medium satisfactorily agree with sounding data up to a numerical coefficient. For a specific size of the lidar aperture, a procedure for determining the coefficient in the Vorob’ev formula is recommended. Construction of a nomogram for determining the structure parameter \(C_{n}^{2}\) for homogeneous turbulence from lidar readings is proposed. It is established that the theory is inconsistent with results of the experiment when sounding inhomogeneous turbulence. It is shown that the section of the sounding path before the scattering volume makes the main contribution to the formation of the turbulent component of the echo signal due to the backscatter enhancement effect. It is proposed to define the structure parameter of optical turbulence \(C_{n}^{2}\) using an approximate formula where the normalized parameter \(C_{n}^{2}\) is directly proportional to the echo signal and inversely proportional to the integral determining the dispersion of radiation intensity fluctuations.

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Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation (project no. 075-15-2021-934).

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Authors and Affiliations

  1. V.E. Zuev Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, 634055, Tomsk, Russia

    I. A. Razenkov

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  1. I. A. Razenkov
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Correspondence to I. A. Razenkov.

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Translated by A. Nikol’skii

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Razenkov, I.A. A Heuristic Approach to Defining the Structure Parameter of the Refractive Index of the Atmosphere from Turbulent Lidar Data. Atmos Ocean Opt 35, 345–354 (2022). https://doi.org/10.1134/S1024856022040169

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