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DIRECTED
ENERGY
PROFESSIONAL
SOCIETY
Abstract: 24-Symp-160
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UNCLASSIFIED, PUBLIC RELEASE
Machine Learning Prediction of Optical Turbulence Using Two-Level Observations Collected over a Year at NRL-CEOBS
Optical turbulence can disrupt the focus of a laser beam and affect the performance of a directed energy weapon. Thus, it is important to reliably estimate turbulence levels along the beam path. Existing physical models, such as NAVSLaM, can reliably predict turbulence in certain environments, e.g., over the open ocean. However, modeling turbulence in more complex environments is difficult. Over the past year, we have collected data from NRL-Coastal Environmental Observation Station (CEOBS), a site in a near-coastal region of the Monterey Bay. We used sonic anemometers at two levels to estimate optical turbulence (Cn2) and observed various atmospheric parameters (air temperature, humidity, solar flux, etc.). Using these observed atmospheric parameters as predictors, we developed a machine learning regression model using Cn2 as the response. Preliminary results are very encouraging.
UNCLASSIFIED, PUBLIC RELEASE
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