For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength (C 2 n) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If C 2 n is not measured directly (e.g., with a scintillometer), C 2 n parameterizations must be utilized to estimate it from meteorological observations or model output. Even though various parameterizations exist in the literature, their relative performance is unknown. We fill this knowledge gap by performing a systematic three-way comparison of a flux-, gradient-, and variance-based parameterization. Each parameterization is applied to both observed and simulated meteorological variables, and the resulting C 2 n estimates are compared against observed C 2 n from two scintillometers. The variance-based parameterization yields the overall best performance, and unlike other approaches, its application is not limited to the lowest part of the atmospheric boundary layer (i.e. the surface layer). We also show that C 2 n estimated from the output of the Weather Research and Forecasting model aligns well with observations, highlighting the value of mesoscale models for optical turbulence modeling.

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We present a machine learning-based measure-correlate-predict approach that predicts a multi-year time-series of optical turbulence strength (Cn2) with high accuracy (r = 0.78 at 16 locations) based on a single year of in-situ Cn2 measurements and reanalysis data.@en

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.@en

Free-Space Optical Communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength (Cn 2) is challenging during the design phase of an optical link or network. Over the past 25 years, (see manuscript PDF for symbol) parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of (Cn 2). However, the outputs of these models can exhibit substantial variability based on the user-defined configuration that determines how atmospheric processes are represented. To address this concern, we propose to run not a single model configuration but multiple diverse ones to generate an ensemble estimate of (Cn 2). We employ the Weather Research and Forecasting model (WRF) with ten different Planetary Boundary Layer (PBL) physics schemes forming a diverse ensemble yielding a probabilistic (Cn 2) estimate. We demonstrate that this ensemble outperforms the individual runs when compared to scintillometer field measurements and show it to be robust against outliers. We believe that FSOC downstream tasks such as link budget estimations should also become more robust if based on a (Cn 2) ensemble estimate compared to single model runs.

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