Date of Award
3-2020
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 is to develop a regime-based quantification of horizontal wind field uncertainty utilizing a global ensemble numerical weather prediction model. In this case, the Global Ensemble Forecast System Reforecast (GEFSR) data is utilized. The machine learning algorithm that is employed is the mini-batch K-means clustering algorithm. 850 hPa Horizontal flow fields are clustered and the forecast uncertainty in these flow fields is calculated for different forecast times for regions across the globe. This provides end-users quantified flow-based forecast uncertainty.
AFIT Designator
AFIT-ENP-MS-20-M-093
Recommended Citation
Fioretti, John L., "Characterizing Regime-Based Flow Uncertainty" (2020). Theses and Dissertations. 3262.
https://scholar.afit.edu/etd/3262