MACHINE LEARNING APPROACH FOR EVAPORATION DUCT NOWCAST
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Authors
Yanez, Josue F.
Subjects
propagation
forecast
nowcast
duct
evaporative
METOC
meteorology
oceanography
METOC
machine learning
artificial intelligence
electromagnetic
electro-optical
forecast
nowcast
duct
evaporative
METOC
meteorology
oceanography
METOC
machine learning
artificial intelligence
electromagnetic
electro-optical
Advisors
Wang, Qing
Feldmeier, Joel W.
Date of Issue
2021-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
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 EDH/EDS 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 intelligence/machine learning (AI/ML) algorithms to demonstrate the feasibility to nowcast (up to six-hour forecast) EDH/EDS while a naval vessel is underway. The tested AI/ML 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 AI/ML algorithms. Two methods to forecast EDH/EDS are tested—one to forecast EDH/EDS directly, the other to calculate EDH/EDS based on the AI/ML 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.
Type
Thesis
Description
Series/Report No
Department
Meteorology (MR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
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.