Intercomparison of flux-, gradient-, and variance-based optical turbulence ( 2) parameterizations.

For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength ( 2) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If 2 is not measured directly (e.

Π-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer.

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ( 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, Π-ML, based on dimensional analysis and gradient boosting to estimate 2.