Date of Award

August 2024

Degree Type

Thesis

Degree Name

Master of Science

Department

Atmospheric Science

First Advisor

Jon Kahl

Second Advisor

Clark Evans

Abstract

Wind gusts are small-scale weather phenomena characterized by sudden, brief increases in wind speed (American Meteorological Society 2023), posing significant forecasting challenges due to their small spatial and temporal scales. These gusts are typically assumed to be caused by turbulent eddies circulating fast winds aloft down to the surface. Accurate forecasting of wind gusts is crucial across various sectors, including wind energy, structural engineering, and electric power distribution, where gusts can cause substantial economic impacts. Current forecasting methods either rely on the dynamical assumption that boundary layer turbulence mixes momentum down to the surface or predominantly statistical methods that typically utilize extreme value statistics or wind distribution models (Brasseur 2001, Sheridan 2018).This study evaluates the mixdown model, which uses upper air wind speeds at specific altitudes (mixdown altitudes) to predict peak surface gusts, under high-pressure conditions. Through this study, it was found that under high-pressure conditions for locations where terrain forced turbulent mixing is the principal cause of wind gusts, the mixdown model is a skillful method to forecast peak wind gusts.

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