Advisor

Mercer, Andrew E.

Committee Member

Fuhrmann, Christopher M.

Committee Member

Dyer, Jamie L.

Date of Degree

1-1-2018

Document Type

Graduate Thesis - Open Access

Major

Professional Meteorology / Climatology

Degree Name

Master of Science

College

College of Arts and Sciences

Department

Department of Geosciences

Abstract

Severe weather outbreaks are violent weather events that can cause major damage and injury. Unfortunately, forecast models can mistakenly predict the intensity of these events. Frequently, the prediction of outbreaks is inaccurate with regards to their intensity, hindering the efforts of forecasters to confidently inform the public about intensity risks. This research aims to improve outbreak intensity forecasting using severe weather parameters and an outbreak ranking index to predict outbreak intensity. Areal coverage values of gridded severe weather diagnostic variables, computed from the North American Regional Reanalysis (NARR) database for outbreaks spanning 1979 to 2013, will be used as predictors in an artificial intelligence modeling ensemble to predict outbreak intensity. NARR fields will be dynamically downscaled to a National Severe Storms Laboratory-defined WRF 4-km North American domain on which areal coverages will be computed. The research will result in a model that will predict verification information on the model performance.

URI

https://hdl.handle.net/11668/17655

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