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Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study

Grose, Mitchell
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788

Abstract Details

, Master of Science (M.S.), University of Dayton, Electro-Optics.
Optical (atmospheric) turbulence (Cn2) is a highly stochastic process that can apply many adverse effects on imaging and laser propagation systems. Modeling atmospheric turbulence conditions has been proposed by physics-based models but they are unable to capture the many cases. Recently, machine learning surrogate models have been used to learn the relationship between local environmental (weather) and turbulence conditions. These models predict a turbulence strength at time t from weather at time t. This thesis proposes a technique to forecast four hours of future turbulence conditions at 30-minute intervals from prior environmental parameters using artificial neural networks. First, local weather and turbulence measurements are formatted to pairs of input sequence and output forecast. Next, a grid search is performed to find the best combination of model architecture and training parameters. The architectures investigated are the Multilayer Perceptron (MLP) and three variants of the Recurrent Neural Network (RNN). Finally, the selected model is applied to the test dataset and analyzed. It is shown that the model has generally learned the relationship between prior environmental and future turbulence conditions.
Edward Watson (Advisor)
Vijayan Asari (Committee Member)
Yakov Diskin (Committee Member)
Artificial Intelligence; Physics
atmospheric characterization; optical turbulence; machine learning; meteorological turbulence modeling; recurrent neural network

Recommended Citations

Citations

  • Grose, M. (2021). Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788

    APA Style (7th edition)

  • Grose, Mitchell. Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788.

    MLA Style (8th edition)

  • Grose, Mitchell. "Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788

    Chicago Manual of Style (17th edition)

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This open access ETD is published by University of Dayton and OhioLINK.