COMPARISON OF OPTICAL TURBULENCE PREDICTION MODELS
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Authors
Tamus, Marthen R.
Subjects
laser propagation
optical turbulence
atmospheric measurement
Navy Atmospheric Vertical Surface Layer Model
NAVSLaM
optical turbulence
atmospheric measurement
Navy Atmospheric Vertical Surface Layer Model
NAVSLaM
Advisors
Blau, Joseph A.
Cohn, Keith R.
Date of Issue
2022-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Measuring and predicting optical turbulence is difficult and requires specialized equipment. NPS Meteorology Department has a developed model, Navy Atmospheric Vertical Surface Layer 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 measures 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 input for NAVSLaM and 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 to the given environment.
Type
Thesis
Description
Series/Report No
Department
Physics (PH)
Organization
Identifiers
NPS Report Number
Sponsors
Office of Naval Research (Arlington, VA 22217)
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.