Atmospheric propagation effects, including optical turbulence, impact performance of Naval systems including optical communications, laser, and imaging systems. Cn2 is a common quantitative measure used to characterize optical turbulence and its impact on optical systems. Parametric models of turbulence (such as NAVSLaM) generally perform well under well-characterized, relatively homogeneous environments, however are not typically designed for challenging environments, with varying terrain height. Machine learning is well suited to modeling these kinds of scenarios.

In this presentation, we will demonstrate Cn2 predictions along an inhomogeneous test path, by building and applying specific ML (machine learning) models, using meteorological data as input parameters. We use approximately two years of data collected on the SPARTA test path in San Diego, CA, a path that is well instrumented with meteorological and turbulence sensors. The extensive dataset provides a large training set, and enables diurnal and seasonal trend analysis. We explore different combinations of inputs and rank them in order of their contribution and relative importance to the model prediction. A comparison between various ML algorithms, such as LightGBM (Light Gradient Boosting Machine) and Extra Tree, is performed to determine which best predicts Cn2. In addition to using meteorological data as inputs to the model, we also experiment with using images captured at a test range setup at NIWC Pacific in San Diego.

Novel Approaches to Modeling Turbulence in Inhomogeneous Environments

Stephen Hammel

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