Macro-meteorological models predict optical turbulence as a function of weather data. Existing models often struggle to accurately predict the rapid fluctuations in C n 2 in near-maritime environments. Seven months of C n 2 field measurements were collected along an 890 m scintillometer link over the Severn River in Annapolis, Maryland. This time series was augmented with local meteorological measurements to capture bulk-atmospheric weather measurements. The prediction accuracy of existing macro-meteorological models was analyzed in a range of conditions. Next, machine-learning techniques were applied to train new macro-meteorological models using the measured C n 2 and measured environmental parameters. Finally, the C n 2 predictions generated by the existing macro-meteorological models and new machine-learning informed models were compared for four representative days from the data set. These new models, under most conditions, demonstrated a higher overall C n 2 prediction accuracy, and were better able to track optical turbulence. Further tuning and machine-learning architectural changes could further improve model performance.

The index of refraction structure constant, C n 2 , characterizing the intensity of optical turbulence, describes the disruption of a propagating electromagnetic beam passing through an inhomogeneously heated turbulent environment. In order to improve predictive models, it is critical to develop a deeper understanding of the relationships between environmental parameters and optical turbulence. To that end, an overwater, 890 m scintillometer link was established along the Chesapeake Bay adjacent to the Severn River in Annapolis, Maryland. Specifically, C n 2 data from the scintillometer, as well, as numerous meteorological parameters were collected over the period of approximately 15 months to characterize a scintillometer link in the near-maritime environment. The characteristics of this near-maritime link were distinct from those observed in prior over-land and open ocean links. Further, existing macro-meteorological models for predicting C n 2 from environmental parameters developed for open-ocean links were shown to perform poorly in the near-maritime environment. While the offshore adapted macro-meteorological model demonstrated lower prediction error, this study suggests that new models could be developed to reduce C n 2 prediction error in the near-maritime environment. The complete data set, including C n 2 measurements, and to our knowledge, one of the first to extend beyond one year, is available.