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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 4
Geosci. Model Dev., 8, 957–973, 2015
https://doi.org/10.5194/gmd-8-957-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The community version of the Weather Research and Forecasting...

Geosci. Model Dev., 8, 957–973, 2015
https://doi.org/10.5194/gmd-8-957-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 07 Apr 2015

Development and technical paper | 07 Apr 2015

Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation

C. W. Tessum1, J. D. Hill2, and J. D. Marshall1 C. W. Tessum et al.
  • 1Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Minneapolis, Minnesota, USA
  • 2Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, Minnesota, USA

Abstract. We present results from and evaluate the performance of a 12-month, 12 km horizontal resolution year 2005 air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a volatility basis set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary modeling efforts used for regulatory and health-effects analysis, with an annual average daytime ozone (O3) mean fractional bias (MFB) of 12% and an annual average fine particulate matter (PM2.5) MFB of −1%. WRF-Chem, as configured here, tends to overpredict total PM2.5 at some high concentration locations and generally overpredicts average 24 h O3 concentrations. Performance is better at predicting daytime-average and daily peak O3 concentrations, which are more relevant for regulatory and health effects analyses relative to annual average values. Predictive performance for PM2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 36%), underpredicts particulate nitrate (MFB = −110%) and organic carbon (MFB = −29%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM2.5 subspecies. Model predictive performance for PM2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.

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We evaluate the predictive performance of a 12-month, 12km horizontal resolution WRF-Chem air quality model simulation for the contiguous United States. Model performance is comparable to other contemporary models used for regulatory and health-effects analysis, with 12% bias for daytime ozone and -1% bias for fine particulate matter (PM2.5). Performance for PM2.5 is worse in winter and in the western U.S. than in other seasons and regions, suggesting opportunities for future model development.
We evaluate the predictive performance of a 12-month, 12km horizontal resolution WRF-Chem air...
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