Date of Award

December 2021

Degree Type

Thesis

Degree Name

Master of Science

Department

Atmospheric Science

First Advisor

Clark Evans

Committee Members

Jonathon Kahl, Sergey Kravtsov

Keywords

Convection Allowing Models, High-shear Low-CAPE, Modeling, Sea-surface Temperature Uncertainty, Severe Weather Predictability, Southeast United States

Abstract

Environments conducive to severe weather and tornadoes occur throughout the southeastern United States, particularly during the cold-season. Throughout the cold-season, severe weather in this region predominantly occurs in environments characterized by high-shear, low-CAPE (HSLC). An important aspect to the production of severe weather in HSLC environments in the southeast United States is that air parcels that help contribute to the limited positive-buoyancy generation originate over areas such as the Gulf of Mexico, western Caribbean Sea, and western Atlantic Ocean. These relatively warm bodies of water, particularly outside of the cooler coastal shelf regions, allow the air parcels to warm and moisten via latent heat and surface sensible fluxes. It is hypothesized that the forecasts of cold-season severe weather in the southeastern United States are sensitive to the treatment of the underlying ocean surface, which influences the simulated representation of the surface heat exchange between the air and sea. We aimed to address and quantify these sensitivities by conducting numerical simulations for eight identified cold-season southeastern United States severe weather cases initialized using several different sea-surface temperature (SST) analyses. An ensemble of forecasts using varying atmospheric and SST analyses is also conducted for the case with the largest variability in forecast skill between SST initializations to quantify the contributions of initial atmospheric and SST uncertainty to subsequent forecast uncertainty. Neighborhood-based forecast verification techniques based off updraft helicity swaths are used to quantify these uncertainties.

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