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Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance
Citation:
Hogrefe, C., P. Doraiswamy, B. Colle, K. Demerjian, W. Hao, M. Erickson, M. Souders, AND J. Ku. Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance. Chapter 83, Air Pollution Modeling and its Application XXII. Springer, Heidelburg, Germany, 2014:493-497, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations con-sidered in this study introduce a typical variability of ~1°C, 250 - 500 m, 1 m/s, and 15 - 30° for temperature, PBL height, wind speed, and wind direction, respectively. The effects of grid resolution are typically smaller and more localized. Results of the air quality simu-lations show that the perturbations in meteorology tend to have a larger impact on pollutant concentrations than emission perturbations and grid resolution effects. Operational model evaluation results show that the meteorological and grid resolution ensembles impact a wider range of model performance metrics than emission perturbations. Probabilistic model performance was found to vary with exceedance thresholds. The results of this study suggest that meteorological perturbations introduced through ensemble weather forecasts are the most important factor in constructing a model-based O3 and PM2.5 ensemble forecasting system.