Date of Award

May 2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Aaron Kennedy

Abstract

Wildfire smoke impacts air quality and has occurred more frequently in recent years in the Northern Great Plains (NGP). To effectively communicate the density of pollutants like wildfire smoke, the EPA established the Air Quality Index (AQI), but measurements in the NGP are sparse. Recently, the High-Resolution Rapid Refresh (HRRR) model was upgraded to include near-surface and vertically integrated smoke products to help forecast the impacts of any smoke event. Few studies exist that explore the verification of smoke forecasts. Forecasts for any weather event are essential to Impact-Based Decision Support Services (IDSS); thus, the quality of smoke forecasts is equally essential for IDSS. Due to the recent uptick in smoke episodes, IDSS of this hazard is still in its infancy in the NGP. Work is needed to understand best practices of IDSS, which also means understanding the performance of the underlying models that can be used to forecast poor air quality.

To verify HRRR-Smoke forecasts, the summers of 2021-2023 were evaluated using skill scores, time lag analysis, and case study analyses. Case studies were defined using polar orbiting satellite imagery and regional lidar data (ceilometers). Forecasts were evaluated against observed PM2.5 measurements from PurpleAir air quality sensors in the County Warning Area (CWA) of the Fargo/Grand Forks (FGF) National Weather Service Forecast Office (NWSFO). Overall, results show skill in forecasts; however, with a negative bias (better simulated air quality) compared to observations.

An additional qualitative study was conducted via interviews of current IDSS and existing air quality policies for the FGF CWA. The goal of a qualitative analysis was to guide future IDSS strategies for poor air quality events. Results from the interviews outline how all partners communicate air quality information, and how only one partner issues air quality alerts. Model verification, identified biases, and a correction to model data will aid operational meteorologists in forecasting future smoke events and improve IDSS management.

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