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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 05, 2021
- Source ID
- 10.1364/ao.416680
Entities
People
- Charles Nelson
- Christopher Jellen
- Cody Brownell
- John Burkhardt
- Miles Oakley
Organizations
- Office of Naval Research
- United States Naval Academy