Forecast Skill Assessment of a WRF-Chem Regional Air Quality Modeling System using RO3QET Ozone LiDAR Observations During the AEROMMA and STAQS 2023 Field Campaigns

We developed a regional air quality modeling system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide chemical forecasting support during the Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. Every day during the “East Coast Urban” phase of the campaign, the forecasting system provided 72-hour predictions of the spatiotemporal distribution of air pollutants in the Lake Michigan region of the United States. In this study, we assess 1-, 2-, and 3-day forecast skill using ozone (O₃) LiDAR observations from Chiwaukee Prairie, Wisconsin, a receptor site of air pollution from the Chicago, Illinois, metropolitan area. The O₃ LiDAR observations were taken from the University of Alabama in Huntsville’s Rocket-city Ozone Quality Evaluation in the Troposphere (UAH RO3QET) mobile observation platform, which is a member of NASA's Tropospheric Ozone LiDAR Network (TOLNet). This dataset of ground-based O₃ profiles provides a unique opportunity to evaluate the vertical forecast skill of the modeling system and identify areas for improvement.