Machine-learning informed macro-meteorological models for the near-maritime environment

Abstract

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.