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Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization
Citation:
Pleim, Jon, R. Gilliam, W. Appel, J. Godowitch, David-C Wong, G. Pouliot, AND L. Ran. Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization. Chapter 80, Air Pollution Modeling & Its Application XXIII. Springer, New York, NY, , 489-493, (2014).
Impact/Purpose:
The National Exposure Research Laboratory’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:
The 2-way coupled WRF-CMAQ meteorology and air quality modeling system is evaluated for high-resolution applications by comparing to a regional air quality field study (Discover-AQ). The model was modified to better account for the effects of urban environments. High-resolution spatial data for fractions of impervious surfaces and tree canopy from the National Land-Cover Database (NLCD) were processed for each grid and used to scale ground heat capacity and to constrain vegetation coverage and characteristics. These simple algorithms along with modified albedo and roughness length in urban areas result in improved simulation of urban heat island and urban boundary layers. The reduced nocturnal stability and enhanced vertical mixing lead to reduced temperature and humidity biases and reduced under-predictions of ozone concentrations in urban areas.
