{\Pi}-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer. (arXiv:2304.12177v2 [physics.ao-ph] UPDATED)

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 …

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