Date of Award

3-2020

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Andrew J. Geyer, PhD

Abstract

This research proposes a new methodology for U.S. Air Force weather forecast metrics. Military weather forecasters are essentially statistical classifiers. They categorize future conditions into an operationally relevant category based on current data, much like an Artificial Neural Net or Logistic Regression model. There is extensive literature on statistically-based metrics for these types of classifiers. Additionally, in the U.S. Air Force, forecast errors (errors in classification) have quantifiable operational costs and benefits associated with incorrect or correct classification decisions. There is a methodology in the literature, Bayes Cost, which provides a structure for creating statistically rigorous metrics for classification decisions that have such costs and benefits. Applying these types of metrics to Air Force weather yields more informative metrics that account for random chance while remaining simple to calculate. This research conducts an analysis using notional values from an unnamed subject matter expert. Bayes Cost-based verification on Terminal Aerodrome Forecasts and Watches/Warnings/Advisories compared to surface observations from a selection of military installations in the continental United States during the period 01 May 2019 to 30 June 2019. The case study illustrates the added utility of the new metric paradigm.

AFIT Designator

AFIT-ENP-MS-20-M-077

DTIC Accession Number

AD1101469

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