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Evaluation and Intercomparison of CMAQ-Simulated Ozone and PM2.5 Trends Over the United States
Citation:
Hogrefe, C., K. Foley, W. Appel, S. Roselle, D. Schwede, J. Bash, AND R. Mathur. Evaluation and Intercomparison of CMAQ-Simulated Ozone and PM2.5 Trends Over the United States. 2018 CMAS Conference, Chapel Hill, NC, October 22 - 24, 2018.
Impact/Purpose:
By comparing observed and CMAQ-simulated long-term ozone variations and trends, the work in this study addresses the question of how well CMAQ performs when applied to simulate the effects of changing emissions and meteorological variability on ambient ozone. The study examines two sets of simulations that differ in two of the key input fields, i.e. anthropogenic emissions and boundary conditions. Results of this analysis can inform model development efforts by focusing such efforts on processes and input fields most directly connected to the model’s ability in reproducing observed variability and trends.
Description:
In this study, we compare ozone and PM2.5 fields from two sets of long-term WRF/CMAQ simulations against available observations and against each other. The two simulations were performed for 1990 – 2010 and 2002 – 2014, respectively, and differed in terms of emission inputs, chemical boundary conditions, and horizontal grid spacing (36 km vs. 12 km). The evaluation of modeled ozone and PM2.5 levels and trends utilizes observations from AQS, IMPROVE and CASTNET and is performed on a seasonal basis for nine different geographic regions considering different percentiles of the pollutant distributions. A special emphasis is placed on evaluating the time period common to both simulations (2002 – 2010) to determine commonalities and differences between the simulations. This analysis also includes a comparison of the emission inputs and boundary conditions used in both simulations to investigate to which extent these factors may have caused differences in the simulated pollutant levels, variations, and trends. The results are discussed in the context of designing and interpreting dynamic model evaluation studies.
