Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Robert C. Tournay, PhD

Abstract

The goal of this work was to analyze 24-hour back trajectory performance from a global, low-resolution weather model compared to a high-resolution limited area weather model in particular meteorological regimes, or flow patterns using K-means clustering, an unsupervised machine learning technique. The duration of this study was from 2015-2019 for the contiguous United States (CONUS). Three different machine learning algorithms were tested to study the utility of these methods improving the performance of the CFS relative to the performance of the RAP. The aforementioned machine learning techniques are linear regression, Bayesian ridge regression, and random forest regression. These results mean reducing computational time for the user. Additionally, the greatest improvement of CFS values occurred in July, August, and September.

AFIT Designator

AFIT-ENP-MS-21-M-119

DTIC Accession Number

AD1146028

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