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Dissertation/Thesis Abstract

The Impact of Using Observational Datasets to Verify Warn-On-Forecast Ensemble Forecasts of Severe Weather Events
by Jordan, Arianna Marie, M.S., Howard University, 2020, 59; 28025246
Abstract (Summary)

The observational datasets of LSRs and NWS Warnings were used to evaluate Warn-on-Forecast System (WoFS) ensemble forecasts of high-impact weather events. These datasets assessed forecast reliability and skill of several WoFS forecast variables, and the degree of sensitivity to various neighborhood sizes, smoothing weights, and hourly forecast periods were examined. The desired metrics used to analyze forecast performance were fractions skill scores (FSS) and reliability diagrams. Results depicted high skill for NWS Warnings at a 27-km neighborhood for a smoothing weight of 39 and a 4-hour period. WoFS reliability was best for NWS Warnings at a 27-km neighborhood, and the amount of smoothing necessary for good reliability depended on the hourly period. LSRs produced the worst skill and reliability for all variables except hail. The next steps will be to analyze additional years, perform statistical analysis on reliability measurements, and adjust LSRs to match that of the WoFS domain.

Indexing (document details)
Advisor: Morris, Vernon R., Hoogewind, Kimberly
Commitee: Clark, Adam, Demoz, Belay
School: Howard University
Department: Atmospheric Science
School Location: United States -- District of Columbia
Source: MAI 82/4(E), Masters Abstracts International
Subjects: Meteorology, Atmospheric sciences, Computer science
Keywords: Hail, Tornadoes, Warn-on-forecast
Publication Number: 28025246
ISBN: 9798678176622
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