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

Area of Science:

• Atmospheric Science
• Environmental Science
• Air Quality Modeling

Background:

• Accurate meteorological data is crucial for air pollution dispersion modeling.
• Traditional models use fixed-location weather station data, which has limitations in capturing fine-scale variability due to sparse distribution.
• Gaps in observational data hinder accurate pollution predictions, particularly in remote or data-sparse regions.

Purpose of the Study:

• To develop and evaluate a novel framework for generating American Meteorological Society/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) compatible surface meteorology data using the High-Resolution Rapid Refresh (HRRR) dataset.
• To assess the performance of HRRR-derived meteorology data in predicting traffic-related nitrogen dioxide (NO2) concentrations using the Research LINE source (R-LINE) dispersion model.
• To compare the predictive accuracy of HRRR data against traditional observational meteorological data.

Main Methods:

• Generated AERMOD-compatible surface meteorology (.sfc) data from the 3-km, hourly HRRR dataset.
• Created three scenarios for HRRR meteorology data, including adjustments for convective and stable planetary boundary layer conditions.
• Applied HRRR-derived and AERMET-processed observational meteorology data in the R-LINE model to predict NO2 concentrations at 443 U.S. monitoring sites.
• Evaluated model performance using coefficient of determination (R2) and Index of Agreement (IOA) by comparing predicted NO2 with measured data.

Main Results:

• HRRR-derived meteorology data scenarios yielded a higher average R2 (0.26) compared to observational data (R2=0.16), indicating over a 60% increase in explained variance.
• HRRR data generally outperformed observational data in predicting NO2 concentrations, especially in areas farther from weather stations and with varying traffic magnitudes.
• Site-specific Index of Agreement analyses showed HRRR inputs performed better across most of the continental U.S., though less effectively in dense urban areas compared to observational data.

Conclusions:

• The High-Resolution Rapid Refresh (HRRR) dataset offers a viable and potentially superior alternative to traditional observational data for air pollution dispersion modeling.
• The developed framework demonstrates the potential of HRRR data to improve the accuracy of dispersion models, particularly in data-scarce environments.
• HRRR data shows enhanced predictive capabilities for pollutants like nitrogen dioxide, highlighting its utility for air quality management and research.