Numerical experiments on MM5–CMAQ sensitivity to various PBL schemes

Mao, Qi; Gautney, Larry L.; Cook, Toree M.; Jacobs, M. E.; Smith, S. N.; Kelsoe, J. J.

doi:10.1016/j.atmosenv.2005.12.055

Cited by 62 publications

(46 citation statements)

References 27 publications

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“…Pollutant concentrations measured at 29 sites of the airquality network of AIRPARIF (17 urban, 4 suburban and 8 rural) are compared to first-layer modeled concentrations on the grid-cells containing the corresponding monitor sites. To benchmark model performance we use the skill score S, which is based on the equations of Mao et al (2006):…”

Section: Data and Metrics For Model Evaluationmentioning

confidence: 99%

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

Markakis

Valari

Perrussel³

et al. 2015

Atmos. Chem. Phys.

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Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climateforced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM 2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (∼ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (∼ 5 %), while annual PM 2.5 levels are particularly sensitive to changes in their primary emissions (∼ 32 %) and the resolution of the emission inventory (∼ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM 2.5 . In contrast this consistency is not observed for PM 2.5 . In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (∼ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ∼ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM 2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 µg m −3 ), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

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Section: Data and Metrics For Model Evaluationmentioning

confidence: 99%

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

Markakis

Valari

Perrussel³

et al. 2015

Atmos. Chem. Phys.

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“…performance for different parameterization schemes. Another study [Mao et al 2006] has shown that notwithstanding the difference in meteorological variables, the performance of the overall photochemical model is comparable. For the current application, the schemes used are: Kain- Four Dimensional Data Assimilation (FDDA) is performed using the method of analysis nudging, where a nudging term is added to the prognostic equations for wind, temperature, and water vapor.…”

Section: Description Of the Proposed Characterization Modelmentioning

confidence: 99%

A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States

Shah

Ries

2009

Int J Life Cycle Assess

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iv Photochemical pollution is formed due to the chemical reactions of atmospheric NO x , volatile organic compounds, CO, and CH 4 in the presence of sunlight. It is a complex, non-linear process influenced by several parameters which change spatially and temporally. Ozone, which is the most common photochemical, damages human health, ecosystems, and man-made materials. It also contributes to climate change. Traditional life cycle impact assessment methodologies have used aggregated impact factors for a country or even for a continent, neglecting these variations.This research assesses the geographical and temporal variability in the characterization factors for emissions of NO x and VOC over the continental US by developing monthly statelevel factors. A photochemical air quality modeling system (CAMx-MM5-SMOKE) is used to simulate the process of formation, transformation, transport, and removal of photochemical pollutants. Characterization factors are calculated at three levels along the cause-effect chain, namely, fate level, human and ecosystem exposure level, and human effect level.The results indicate that a spatial variability of one order of magnitude and a temporal variability of two orders of magnitude exist in both the fate level and human exposure and effect level characterization factors for NO x . The highest temporal variation in the characterization factors for NO x is seen in the Northeastern US. The summer time characterization factors for NO x are higher than the winter time factors. However, for anthropogenic VOC, the summer time describes LCA as "an objective process to evaluate the environmental burdens associated with a product, process, or activity by identifying and quantifying energy and material usage and environmental releases, to assess the impact of those energy and material uses and releases to the environment, and to evaluate and implement opportunities to effect environmental improvements. The assessment includes the entire life cycle of the product, process, or activity, encompassing extracting and processing raw materials; manufacturing, transportation, and distribution; use/re-use/maintenance; recycling; and final disposal." [Fava et al 1991] A LCA is divided into four phases as shown in Figure 1-1. The goal of an LCA identifies the intended application, the motivation, and its intended audience. The scope delineates the product systems or processes to be studied, the system boundaries, the environmental stressors considered, the data requirements, and all assumptions and limitations of the study. The Life Cycle Inventory (LCI) is the process of compilation of the material and energy inputs, and emissions to air, water, and land associated with the life cycle of the system. Life Cycle Impact Assessment (LCIA) assesses the environmental effects of the inputs and outputs of the system. In the Interpretation phase the results of the LCI and LCIA are combined, the goal and scope are reviewed, and the results of the LCA are evaluated to identify opportunities of system improveme...

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“…Ao passo em que uma série de estudos têm sido conduzidos na tentativa de verificar a sensibilidade de modelos de qualidade do ar frente a diferentes parametrizações (Mao et al, 2006), pouco tem sido discutido acerca da influência da altimetria -ou, diga-se, dos MDEs -sobre os resultados finais de simulações da qualidade do ar. O raciocínio óbvio leva à conclusão de que quanto melhor a resolução do MDE empregado, mais precisos os resultados das simulações.…”

Section: Introductionunclassified

Avaliação do impacto da resolução espacial de modelos digitais de elevação sobre a estimativa de formação de ozônio superficial usando Models-3

Meira

Ducati

Teixeira

2009

Rev. bras. meteorol.

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Este trabalho estabelece uma comparação básica entre dois cenários distintos de simulação da formação de ozônio superficial sobre o Estado do Rio Grande do Sul. No primeiro cenário usa-se um modelo digital de elevação com resolução de 9 km com a interface de simulação "Models-3". No segundo cenário, um modelo com resolução de 3 km é usado. Em cada cenário, implementa-se um único domínio de simulação, com resoluções correspondentes as do respectivo modelo. Dados do Centro Nacional Estadunidense de Previsão Ambiental foram usados na simulação meteorológica, e dados do Instituto Brasileiro de Geografia e Estatística e do Departamento de Trânsito do Rio Grande do Sul foram usados na estimativa das emissões devidas à frota veicular da Região Metropolitana de Porto Alegre (única fonte emissora aqui considerada). Os resultados apresentaram diferenças marcantes entre um cenário e outro. As concentrações constantemente elevadas de ozônio observadas à resolução de 9 km, tiveram seus níveis reduzidos à metade a 3 km. Também os padrões de dispersão tiveram características bem distintas, espalhando-se amplamente sobre a parte sul do domínio de simulação no primeiro cenário e, no segundo, espalhando-se de forma mais limitada sobre a parte sudoeste, com um jato observado em direção noroeste. Elevados níveis de concentração de ozônio foram observados a cerca de 500 km das fontes emissoras.

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Numerical experiments on MM5–CMAQ sensitivity to various PBL schemes

Cited by 62 publications

References 27 publications

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States

Avaliação do impacto da resolução espacial de modelos digitais de elevação sobre a estimativa de formação de ozônio superficial usando Models-3

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