Composition and Chemistry
Analysis of regional meteorology and surface ozone during the TexAQS II field program and an evaluation of the NMM-CMAQ and WRF-Chem air quality models
Article first published online: 30 JUL 2009
Copyright 2009 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 114, Issue D7, 16 April 2009
How to Cite
2009), Analysis of regional meteorology and surface ozone during the TexAQS II field program and an evaluation of the NMM-CMAQ and WRF-Chem air quality models, J. Geophys. Res., 114, D00F14, doi:10.1029/2008JD011675., , , , , , , , , and (
- Issue published online: 30 JUL 2009
- Article first published online: 30 JUL 2009
- Manuscript Accepted: 16 APR 2009
- Manuscript Revised: 27 MAR 2009
- Manuscript Received: 24 DEC 2008
 This study examines meteorological conditions associated with regional surface ozone using data collected during the summer Second Texas Air Quality Experiment, and the ability of the Nonhydrostatic Mesoscale Model–Community Multi-scale Air Quality Model (NMM-CMAQ) and the Weather Research and Forecast (WRF) model coupled with Chemistry (WRF-Chem) models to simulate the observed meteorology and surface ozone. The surface ozone data consist of 118 sites that are part of the U.S. Environmental Protection Agency Aerometric Information Retrieval Now (AIRNow) network, while the meteorological data came from a network of eleven 915-MHz wind profilers with RASS temperatures and supporting surface meteorological stations. High and low 8-h maximum ozone occurrences most frequently develop as regional events, with similar ozone concentration patterns across all of east Texas, allowing for a separate analysis of high- and low-ozone day conditions. The ability of the NMM-CMAQ and WRF-Chem models to simulate the meteorologically distinct high- and low-ozone events is analyzed. Histograms of surface ozone show that both the NMM-CMAQ and WRF-Chem models underpredict the full range found in the observations. For low ozone values, the analysis indicates that the models have a positive bias because of too large of an ozone inflow boundary condition value over the Gulf of Mexico. In contrast, the models have a negative bias for very high ozone values that occur mostly in Houston and Dallas, which suggests that the urban emissions and/or chemistry is misrepresented in the models.