Articles | Volume 20, issue 3
Atmos. Chem. Phys., 20, 1627–1639, 2020
https://doi.org/10.5194/acp-20-1627-2020
Atmos. Chem. Phys., 20, 1627–1639, 2020
https://doi.org/10.5194/acp-20-1627-2020

Research article 10 Feb 2020

Research article | 10 Feb 2020

On the limit to the accuracy of regional-scale air quality models

S. Trivikrama Rao et al.

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Cited articles

Appel, K. W., Chemel, C., Roselle, S. J., Francis, X. V., Hu, R.-M., Sokhi, R. S., Rao, S. T., and Galmarini, S.: Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains. Atmospheric Environment, AQMEII: An International Initiative for the Evaluation of Regional-Scale Air Quality Models – Phase 1, 53, 142–155, https://doi.org/10.1016/j.atmosenv.2011.11.016, 2012. 
Astitha, M., Luo, H., Rao, S. T., Hogrefe, C., Mathur, R., and Kumar, N.: Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States, Atmos. Environ., 164, 102–116, https://doi.org/10.1016/j.atmosenv.2017.05.020, 2017. 
Biswas, J. and Rao, S. T.: Uncertainties in episodic ozone modeling stemming from uncertainties in the meteorological fields, J. Appl. Meteor., 40, 117–136, 2001. 
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. 
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Short summary
Since numerical air quality models do not explicitly simulate stochastic variations in the atmosphere, there will always be differences between modeled and measured pollutant levels even when the model's physics, chemistry, numerical analysis, and its input data are perfect. This paper quantifies the inherent uncertainty in regional models due to the stochastic nature of the atmosphere. A knowledge of the expected error helps model developers in evaluating the real progress in improving models.
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