Measurement and Modeling of Optical Turbulence Through the Near-maritime Atmospheric Boundary Layer

Optical turbulence, defined as the turbulent fluctuations of the refractive index in a medium, is complex in the lower atmosphere. Existing physics-based models can have mixed results, particularly in littoral environments, which are subject to complex influences from both land and sea. Thus, it is difficult for physical models to accurately predict optical turbulence in this context. Recently, machine learning has been used as an alternative method to develop predictive models of optical turbulence. This project compares several machine learning models and established physical models against experimental data from a long-term measurement campaign. Along with typical bulk meteorological quantities, the data includes image-based optical turbulence measurements of the refractive index structure parameter at multiple heights between ground level and 180-m.