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

Abstract

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations (𝐶2𝑛) 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 𝐶2𝑛. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting 𝐶2𝑛. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of R2 = 0.958 ± 0.001.