Optical Turbulence

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the successful development and deployment of future free-space optical communication links. In this letter, we propose a physics-informed machine learning (ML) methodology, $\Pi$-ML, based on dimensional analysis and gradient boosting to estimate $C_n^2$. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting $C_n^2$....

Free-space optical communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength $C_n^2$ is challenging during the design phase of an optical link or network. Over the past 25 years, $C_n^2$ parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of $C_n^2$....

For free-space optical communication (FSOC) or ground-based optical astronomy, abundant data of optical turbulence strength ($C_n^2$) is imperative but typically scarce. Turbulence conditions are strongly site-dependent, so their accurate quantification requires in-situ measurements or numerical weather simulations. If $C_n^2$ is not measured directly, e.g., with a scintillometer, $C_n^2$ parameterizations must be utilized to estimate $C_n^2$ from meteorological observations or model output. Even though various such parameterizations exist in the literature, their relative performance is unknown....