Date of Award
2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Atmospheric and Earth Science
Committee Chair
Xiaomin Chen
Committee Member
Lawrence Carey
Research Advisor
Patrick Duran
Subject(s)
Cyclones--Development--Tropics, Cirrus clouds--Identification, Machine learning, Neural networks (Computer science)
Abstract
Transverse cirrus bands (TCBs) are bands of upper-level clouds commonly observed in mesoscale and synoptic-scale weather systems. In tropical cyclones (TCs), their appearance has been hypothesized to relate to TC intensity, intensification, and the diurnal cycle. Rigorous testing of these relationships has been limited by the subjective nature of identifying TCBs in satellite imagery. This paper describes a convolutional neural network (CNN) that successfully identifies TCBs in satellite imagery. Using the CNN, a climatology of TCBs from 2019 to 2023 was developed for the Atlantic and Eastern Pacific TC basins. The climatology indicates that TCBs display a diurnal cycle with a maximum in coverage during the afternoon, occur more frequently under higher relative humidity (RH) and higher TC intensities, and occur left of shear (850-200 mb bulk vertical wind shear). The results will also show that TCBs increase with TC intensity until reaching a plateau or decline.
Recommended Citation
Mayhall, John Mark, "Identification and analysis of transverse cirrus bands in tropical cyclones using a U-Net model" (2026). Theses. 810.
https://louis.uah.edu/uah-theses/810