Date of Award

January 2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Aaron Kennedy

Abstract

Regions of the globe, including the Northern Great Plains, are subject to adverse conditions during the wintertime. Among these conditions is blowing snow, which can cause significant societal and economic impacts by reducing visibility. The lack of this process in numerical weather prediction models creates a forecasting challenge for the region. In recent years, however, forecasters have gained access to a variety of National Oceanic Atmospheric Administration (NOAA) modeling and satellite tools such as the High-Resolution Rapid Refresh (HRRR) model and Geostationary Operational Environmental Satellites (GOES) that may improve Impact-Based Decision Support Services (IDSS) goals for blowing snow. This project worked to identify how GOES-16 and HRRR data may improve IDSS during blowing snow events. This was done through a case study approach for events over the winters of 2018-2019 and 2019-2020. To understand the model biases and provide useful insight into the performance of the models, HRRR forecasts were compared to surface observations across the Fargo/Grand Forks NWS County Warning Area (CWA). Visibility for point locations were also subjectively compared to output from GOES-16 imagery. Results of this study demonstrated that GOES-16 is most useful during clear sky events such as Arctic fronts. Limitations due to cloud cover and overnight timing of events suggest a continued need for in situ observations to monitor blowing snow conditions. Error between the model and observations showed the model had difficulty in forecasting visibility. However, small errors in wind speed, temperature, and relative humidity suggests that HRRR model output may be useful for driving blowing snow models. This will provide future utility for real-time forecasting and guidance for blowing snow events.

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