Theses and Dissertations
Advisor
Mercer, Andrew E.
Committee Member
Dyer, Jamie
Committee Member
Rudzin, Johna
Date of Degree
8-13-2024
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Geoscience (Professional Meteorology/Climatology)
Degree Name
Master of Science (M.S.)
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
This study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill.
Recommended Citation
Snyder, Colin Matthew, "Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting" (2024). Theses and Dissertations. 6237.
https://scholarsjunction.msstate.edu/td/6237