Simulation Analysis of the Non-Kolmogorov Atmospheric Turbulence Detected by a Mie-Scattering Lidar Based on the Fractal Method

Abstract Atmospheric turbulence, characterized by irregular motions across various scales, poses significant challenges for accurate description using conventional methods due to its inherent complexity. This paper evaluates the reliability of detecting atmospheric turbulence using Mie-scattering lidar. We simulate non-Kolmogorov turbulence phase screens through both fractal and power spectrum methods, considering different internal and external scales. The impact of varying parameters on simulation accuracy is analyzed. By comparing the obtained phase structure function curves with theoretical curves and conducting error analysis for different methods, we verify the simulation speed and accuracy of the fractal method. A Mie-scattering lidar system is designed for detecting atmospheric turbulence, and the Gaussian laser beam is numerically simulated using the fractal method. Results indicate that the phase screen generated by this method possesses adequate high- and low-frequency components, offering faster simulation speeds and higher precision. The simulated scintillation index aligns with theoretical trends. Moreover, by employing Mie-scattering lidar for the actual detection of non-Kolmogorov turbulence profiles, the feasibility of these simulations has been validated, providing new insights into the multiscale structure and spatial distribution of atmospheric turbulence. Significance Statement Atmospheric turbulence presents significant challenges for various research areas, including space optical communication, laser propagation, and lidar technology. This paper investigates the reliability of detecting atmospheric turbulence using Mie-scattering lidar, employing non-Kolmogorov turbulent phase screens simulated through the fractal method. By analyzing the structure of turbulence and the motion of microscopic particles, this study offers a more robust theoretical foundation for atmospheric turbulence detection, ultimately contributing to more accurate predictions of weather and climate changes.