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Machine Learning Approach for Evaporation Duct Nowcast

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[Technical Report, Master's Thesis]

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The Evaporation Duct Height EDH and Strength EDS are properties of the evaporation duct that affects electromagnetic EM signal propagation close to the air-sea interface. Hence, the accuracies of EDH and EDS affect radar and communication propagation, which can be exploited for detection and counter-detection operations. The EDHEDS can be calculated utilizing meteorological and oceanographical METOC data collected onboard naval ships, including air temperature, sea surface temperature, wind direction, wind speed, sea level pressure, and relative humidity. In this work, we explore the utilization of artificial intelligencemachine learning AIML algorithms to demonstrate the feasibility to nowcast up to six-hour forecast EDHEDS while a naval vessel is underway. The tested AIML algorithms include linear regression, decision trees, random forest, and neural networks. Datasets from the 2017 Coupled Air-Sea Processes and Electromagnetic Ducting Research CASPER-West project were used to train, test, and verify the predictions from the AIML algorithms. Two methods to forecast EDHEDS are tested - one to forecast EDHEDS directly, the other to calculate EDHEDS based on the AIML forecast variables as input to NAVSLaM. The results are compared to those directly derived from the CASPER measurements. The effectiveness and limitations of the methods and algorithms are discussed.

Subject Categories:

  • Electricity and Magnetism
  • Meteorology
  • Cybernetics

Distribution Statement:

[A, Approved For Public Release]