Intercomparison of flux-, gradient-, and variance-based optical turbulence ( 2) parameterizations.

For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength ( 2) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If 2 is not measured directly (e.

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

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ( 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, Π-ML, based on dimensional analysis and gradient boosting to estimate 2.

Hybrid Profile-Gradient Approaches for the Estimation of Surface Fluxes.

The Monin-Obukhov similarity theory-based wind speed and potential temperature profiles are inherently coupled to each other. We have developed hybrid approaches to disentangle them, and as a direct consequence, the estimation of Obukhov length (and associated turbulent fluxes) from either wind-speed or temperature measurements becomes an effortless task. Additionally, our approaches give rise to two easily measurable indices of atmospheric stability.

Estimating higher-order structure functions from geophysical turbulence time series: Confronting the curse of the limited sample size.

Utilizing synthetically generated random variates and laboratory measurements, we document the inherent limitations of the conventional structure function approach in limited sample size settings. We demonstrate that an alternative approach, based on the principle of maximum likelihood, can provide nearly unbiased structure function estimates with far less uncertainty under such unfavorable conditions. The superiority of this approach over the conventional approach does not diminish even in the case of strongly correlated samples.

Utilizing the Kantorovich metric for the validation of optical turbulence predictions.

In this Letter, via idealized and realistic case studies, we have documented the limitations of several conventional metrics in validating Cn2 profile predictions. We have introduced the (normalized) Kantorovich metric as a viable alternative and demonstrated its prowess.

Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii.

In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence (Cn2) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed Cn2 data near the Mauna Loa Observatory, Hawaii are utilized for validation.

Extending a surface-layer Cn2 model for strongly stratified conditions utilizing a numerically generated turbulence dataset.

In Wyngaard et al., 1971, a simple model was proposed to estimate Cn2 in the atmospheric surface layer, which only requires routine meteorological information (wind speed and temperature) as input from two heights. This Cn2 model is known to have satisfactory performance in unstable conditions; however, in stable conditions, the model only covers a relatively short range of atmospheric stabilities which significantly limits its applicability during nighttime.

A simple approach for estimating the refractive index structure parameter (Cn²) profile in the atmosphere.

Utilizing the so-called Thorpe scale as a measure of the turbulence outer scale, we propose a physically-based approach for the estimation of Cn2 profiles in the lower atmosphere. This approach only requires coarse-resolution temperature profiles (a.k.

Synthetic turbulence, fractal interpolation, and large-eddy simulation.

Fractal interpolation has been proposed in the literature as an efficient way to construct closure models for the numerical solution of coarse-grained Navier-Stokes equations. It is based on synthetically generating a scale-invariant subgrid-scale field and analytically evaluating its effects on large resolved scales. In this paper, we propose an extension of previous work by developing a multiaffine fractal interpolation scheme and demonstrate that it preserves not only the fractal dimension but also the higher-order structure functions and the non-Gaussian probability density function of the velocity increments.