Date of Award
Master of Science
Department of Engineering Physics
Robert C. Tournay, PhD
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.
Fioretti, John L., "Characterizing Regime-Based Flow Uncertainty" (2020). Theses and Dissertations. 3262.