Forecast Skill Assessment of a WRF-Chem Regional Air Quality Modeling System using Airborne In-situ and Remote Sensing Observations During the AEROMMA/STAQS 2023 Field Campaign

We developed two nested global/regional air-quality modeling systems to provide chemical forecasting support during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. Two 4km regional domains, centered over Chicago and New York, provided 72-hour predictions of atmospheric composition, clouds, and aerosols in the Chicago, New York, and Toronto using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The 1-km Neighborhood Emission Mapping Operation (NEMO) anthropogenic emission inventory was up-scaled to 4km for the WRF-Chem domains. Initial and boundary conditions were obtained from the Whole Atmosphere Community Climate Model (WACCM) and Real-time Air Quality Modeling System (RAQMS) global model forecasts. In this study, we assess the skill of our forecasts using in-situ and remotely sensed airborne ozone (O₃), nitrogen dioxide (NO₂), formaldehyde (HCHO), and aerosol extinction measurements collected during the AEROMMA/STAQS campaign. This dataset of airborne measurements provides unique opportunities to evaluate the forecast skill of the modeling system and identify areas of improvement.