Date of Award

3-26-2020

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Lance E. Champagne, PhD

Abstract

The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground weather radar gaps result in over-smoothing of the vertical gradient and over-estimate the risk of rocket triggered lightning, potentially resulting in costly, unnecessarily delayed launches. This research explores the application of existing interpolation methods using convolutional neural networks to perform two-dimensional image interpolation, called inpainting, into the three-dimensional weather radar scan domain. Results demonstrate that convolutional neural networks can improve the accuracy of cloud characterization over current interpolation methods, potentially resulting in fewer launch delays with substantial associated cost savings due to increased capability to meet the LLCC.

AFIT Designator

AFIT-ENS-MS-20-M-158

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