Predicting Optical Turbulence using Machine Learning Methodology
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Authors
Blau, Joseph
Coleman, Amanda
Tamus, Marthen
Subjects
High energy lasers (HELs)
atmospheric propagation
optical turbulence
machine learning (ML)
atmospheric propagation
optical turbulence
machine learning (ML)
Advisors
Date of Issue
2023-10
Date
October 2023
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Measuring and predicting optical turbulence is difficult and requires specialized equipment. The NPS Meteorology Department has previously developed a model (NAVSLaM) to predict optical turbulence in the surface layer (up to ~100 m above the ocean or land) based upon atmospheric measurements using simple, robust sensors. On the other hand, the Physics Department has developed machine learning models of optical turbulence using atmospheric measurements. This research involves measurements of optical turbulence over many months using sonic anemometers that served as the baseline to compare prediction from the models. Atmospheric parameters such as air temperature, wind speed, humidity at two different heights as well as solar flux and ground temperature were simultaneously collected. Those data were used as inputs for NAVSLaM and the machine learning models to predict optical turbulence. We then compared the performance of these prediction models to each other by calculating the root-mean-square error with respect to the baseline data from the sonic anemometers. The results from this research will help determine which model is more reliable for the given environment. Overall, the ML model appeared to work better than NAVSLaM for predicting the optical turbulence values that we observed. However, NAVSLaM is a more general model that should work well in a variety of environments. An accurate machine learning model of optical turbulence could significantly improve forecasts of directed energy weapon effectiveness. Eventually, it could even be used in an operational scenario to make real-time predictions of turbulence and its impact on directed energy weapon performance.
Type
Technical Report
Description
NPS NRP Technical Report
Series/Report No
Department
Organization
Identifiers
NPS Report Number
NPS-PH-23-003
Sponsors
Naval Postgraduate School, Naval Research Program; Office of Naval Research
Funder
Naval Research Program
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Format
38 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.