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Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer
Maximilian Pierzyna, Rudolf Saathof, Sukanta Basu
Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations (Cₙ²) 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 Cₙ². Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting Cₙ². 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.
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