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.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence

Christopher Jellen, John Burkhardt, Cody Brownell, and Charles Nelson
Appl. Opt. 59(21) 6379-6389 (2020)

Learned linear models for detecting watercraft in a maritime environment

C. C. Olson and J. M. Nichols
Appl. Opt. 59(25) 7553-7559 (2020)

Comparison of maritime measurements of Cn2 with NAVSLaM model predictions

Rita Mahon, Christopher I. Moore, Mike S. Ferraro, William S. Rabinovich, and Paul A. Frederickson
Appl. Opt. 59(33) 10599-10612 (2020)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (15)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (18)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription