Date of Award

January 2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Aaron Kennedy

Abstract

Small Uncrewed Aerial Systems (sUAS) can be flown in many conditions under Part 107 of the Federal Aviation Administration (FAA) Code of Regulations. While some weather restrictions exist (visibility, ceiling, and horizontal distance from clouds), subjective flight limits (such as acceptable wind limits) are needed by pilots to ensure safe flight. Multiple websites or applications exist to aid sUAS pilots in decision-making regarding weather for flights, though most are not comprehensive or do little to aid the pilot beyond providing basic weather information. This work evaluates High-Resolution Rapid Refresh (HRRR) forecast wind data in the context of an open-source application to aid sUAS pilots in decision-making. Besides traditional model verification, skill is assessed by evaluating forecasts for specific wind speed thresholds that pilots could use adjust based on their skill level. Finally, bias correction is explored to improve HRRR forecasts.This work finds that systematic biases are present in HRRR surface winds, especially for wind gusts fields. The amount of bias is dependent on land surface type, PBL height, and climatological region. This information is used to understand how forecast output can be improved for the real-world beta test of the sUAS application.

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