Preparing Dispersion Model Surface Meteorological Inputs Using High-Resolution Rapid Refresh (HRRR) Data

Accurate meteorological inputs are essential for air pollution dispersion modeling. Traditionally, dispersion models rely on observational meteorological data collected from weather stations. However, the sparse distribution of weather stations limits the ability to capture fine-scale meteorological variability, particularly in areas far from weather stations. In this study, we developed a novel framework for generating American Meteorological Society/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) compatible surface meteorology data (.sfc) using the High-Resolution Rapid Refresh (HRRR) dataset, which provides predicted meteorological variables at a 3-km spatial resolution and an hourly temporal resolution. We followed the AERMOD Model Formulation document to create three scenarios of surface meteorology data, including two exploratory scenarios by setting HRRR meteorology parameter ranges and recalculating key parameters based on whether an hour filled in convective or stable planetary boundary layer status. We then applied these HRRR-derived meteorology data in the Research LINE source (R-LINE) dispersion model to predict traffic-related nitrogen dioxide (NO ₂ ) concentrations at 443 Air Quality System monitoring sites across the United States (U.S.) in the year 2019. For comparison, we also ran R-LINE using observational-based surface meteorology data preprocessed with AERMET. NO ₂ concentrations predicted by R-LINE were compared against NO ₂ measurement data by the meteorology inputs (three HRRR scenarios versus weather station data) using simple linear regression coefficient of determination (R ² ) and Index of Agreement (IOA). The three HRRR scenarios and observational data yielded similar R ² . Stratified by distance between NO ₂ sites and weather stations, HRRR data generally outperformed observational data at sites located more than 2km distance from weather station. Site-specific IOA analyses further showed that HRRR inputs performed better than observational meteorology data across most of the continental U.S. but not as well in urban areas. Because HRRR data are readily available and our framework eliminates the need to operate a meteorological preprocessor, HRRR-derived meteorology represents a practical and effective alternative for dispersion modeling, particularly in regions lacking nearby weather stations.