Browsing OU - Dissertations by Author "Fagg, Andrew"
Now showing items 1-12 of 12
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Coupling Data Science Techniques and Numerical Weather Prediction Models for High-Impact Weather Prediction
Gagne, David John II (2016-08-12)Meteorologists have access to more model guidance and observations than ever before, but this additional information does not necessarily lead to better forecasts. New tools are needed to reduce the cognitive load on ... -
The creation and analysis of next-day random forest-based high-impact weather forecasts
Loken, Eric (2021-05)Flash floods, tornadoes, damaging winds, and large hail are costly and difficult to predict, even for state-of-the-art, high-resolution numerical weather prediction (NWP) systems. Current operational NWP ensembles have a ... -
EEG/MEG Sparse Source Imaging and Its Application in Epilepsy
Zhu, Min (2013-12-13)This dissertation is a summary of my Ph.D. work on the development of sparse source imaging technologies based on electroencephalography (EEG) and magneto-encephalography (MEG) and their application to noninvasively ... -
In Situ Observations of Southern Ocean Clouds from The SOCRATES Field Campaign: Evaluating Cloud Phase, Aerosol-Cloud Interactions, Cloud Layer Types and Entrainment-Mixing Impacts on Mixed Phase Clouds
D'Alessandro, John (2022-12-16)Low level clouds are ubiquitous over the Southern Ocean. However, climate and weather models fail to accurately simulate their radiative impact. This has been attributed in part to the inadequate representation of cloud ... -
Learning Relational Concepts with the Spatiotemporal Multidimensional Relational Framework
Bodenhamer, Matthew (2014-05)The real world can be seen as containing sets of objects that have multidimensional properties and relations. Whether an agent is planning the next course of action in a task or making predictions about the future state ... -
Modeling Relationships Between Brain/Muscle Activity and Locomotive Behavior
Shotande, Monique (2022-12)The dynamics of locomotion involve a fine-tuned, continuous feedback loop between processes in the brain, functioning of the muscles, and interactions with the environment. Neurological or motor disability can often disrupt ... -
MOVIT: MONOCULAR VISION-BASED TRACKING
Ghazi, Mustafa (2018)Cerebral Palsy (CP) is a physical disability that affects approximately 17 million individuals globally. CP can severely impact the development of motor, cognitive, and social skills. Recent research efforts in this domain ... -
Observation of the triboson process pp → W±W∓γ and limits on anomalous quartic gauge couplings with the ATLAS detector
Wilbern, Daniel (2023-05-12)This thesis presents a search for evidence of $W^\pm W^\mp \gamma$ production from $p-p$ scattering with $\sqrt{s}$ = 13 TeV at the Large Hadron Collider using 140 fb$^-1$ of integrated luminosity. The case where the $W$ ... -
Observations and Simulations of Moisture Changes Occurring Around Sunset at the ARM Climate Research Facility
Blumberg, William Gregory (2018-05-11)Each summer, the U.S. Southern Great Plains (SGP) hosts a variety of ingredients (e.g. moisture, shear, instability, and lift) critical to understanding the life-cycle of deep, moist convection. Past studies have primarily ... -
Scaling up Labeling, Mining, and Inferencing on Event Extraction
Liang, Yan (2022-05)Numerous important events happen every day and are reported in different media sources with varying narrative styles across different knowledge domains and languages. Detecting the real-world events that have been reported ... -
Storm-scale Ensemble-based Severe Weather Guidance: Development of an Object-based Verification Framework and Applications of Machine Learning
Flora, Montgomery (2020-12-18)A goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather ... -
Using Deep Learning to Improve Prediction and Understanding of High-impact Weather
Lagerquist, Ryan (2020-05)This dissertation describes the application of convolutional neural networks (CNN), a type of deep-learning method, to high-impact weather. CNNs are specially designed to learn directly from spatial grids, which improves ...