2015
DOI: 10.1016/j.atmosenv.2015.02.034
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Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

Abstract: ?? 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modelling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American d… Show more

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Cited by 61 publications
(74 citation statements)
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“…However, the accurate modeling of CO is a common problem in the European modeling community and our results are similar to other studies (Nopmongcol et al, 2012;Solazzo et al, 2013Solazzo et al, , 2017Giordano et al, 2015). Since CO concentrations do not change rapidly by chemistry and deposition processes, the differences between model and observations are mostly related to boundary conditions, vertical mixing and emissions (Solazzo et al, 2013(Solazzo et al, , 2017Giordano et al, 2015). Although the bias for NO 2 is small (−0.2 ppb), the MGE and RMSE are much higher (in absolute terms), indicating compensation between over-and underestimation throughout the day leading to a weak correlation coefficient (0.4).…”
Section: Model Performance Evaluationsupporting
confidence: 82%
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“…However, the accurate modeling of CO is a common problem in the European modeling community and our results are similar to other studies (Nopmongcol et al, 2012;Solazzo et al, 2013Solazzo et al, , 2017Giordano et al, 2015). Since CO concentrations do not change rapidly by chemistry and deposition processes, the differences between model and observations are mostly related to boundary conditions, vertical mixing and emissions (Solazzo et al, 2013(Solazzo et al, , 2017Giordano et al, 2015). Although the bias for NO 2 is small (−0.2 ppb), the MGE and RMSE are much higher (in absolute terms), indicating compensation between over-and underestimation throughout the day leading to a weak correlation coefficient (0.4).…”
Section: Model Performance Evaluationsupporting
confidence: 82%
“…The overall model performance for the daily mean concentrations of the air pollutants in summer (JJA) 2010 (Table 5) was reasonably good. The statistical evaluation results for most chemical species were in line with those reported for various models and parameterizations for summer periods in Europe (Bessagnet et al, 2004Solazzo et al, 2012a, b;Nopmongcol et al, 2012;Giordano et al, 2015). Model performance goals and criteria for O 3 and PM 2.5 (Table 2), recommended by Boylan and Russell (2006) and EPA (2007), were met.…”
Section: Model Performance Evaluationsupporting
confidence: 65%
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“…Boundary conditions (BCs) can have a significant impact on regional model predictions (Schere et al, 2012) towards changed BC for January and July 2006 while Giordano et al (2015) compare pollutant concentrations simulated by IFS-MOZART and the AQMEII-2 regional models for 2010 to estimate the degree to which BC affect regional simulations. In this study, we complement their analyses by presenting comparisons of IFS-MOZART seasonal mean mid-tropospheric predictions over North America to gain some insight into the likely influence of large-scale background changes between 2006 and 2010 on the regional model predictions based on the assumption that mid-tropospheric conditions are roughly indicative of impacts of BC tendencies on concentrations at the surface.…”
Section: Boundary Conditionsmentioning
confidence: 99%
“…In a previous study by , it was found that the GEM-MACH air quality forecasting model (Moran et al, 2010;Moran et al, 2013;Makar et al, 2015a, b;Gong et al, 2015), using a domain covering the Canadian provinces of Alberta and Saskatchewan at 2.5 km resolution, under-predicted summertime tropospheric ammonia VMRs by 0.4-0.6 ppbv (which is 36-100 % depending on altitude -see Fig. 16 in in the AOSR when compared to the Tropospheric Emission Spectrometer (TES) satellite measurements and aircraft measurements.…”
Section: Introductionmentioning
confidence: 99%