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

Code

• github.com/mpierzyna/piml
  OfficialIn papernone★ 2

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 (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, -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. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of R^2=0.9580.001.

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Reproductions