Sampled Monte Carlo uncertainty analysis for photochemical grid models

Moore, G.E.; Londergan, R.J.

doi:10.1016/s1352-2310(01)00260-6

Cited by 32 publications

(26 citation statements)

References 12 publications

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“…We estimated the uncertainty of vertical diffusion coefficients to be 35 % of the first guess values which are close to the average estimate of the above three estimates of Beekmann and Derognat (2003), Hanna et al (1998) and Moore and Londergan (2001). Also with reference to the studies of Hanna et al (1998), and Moore and Londergan (2001), the uncertainty of the modeled photolysis rates was estimated to be 30 %. The uncertainty of the modeled O 3 concentrations at the initial time was estimated to be 50 % after comparing the modeled O 3 concentrations with the O 3 observations.…”

Section: Data Assimilation Algorithmsupporting

confidence: 52%

“…Therefore, we estimated the uncertainty of the NO x emissions to be 60 % of the first guess emission rates, about twice the uncertainty in the INTEX-B Asia inventory. The uncertainties of vertical diffusion coefficients in ozone modeling have been estimated by Beekmann and Derognat (2003), Hanna et al (1998), and Moore and Londergan (2001) and range from 25 to 50 %. We estimated the uncertainty of vertical diffusion coefficients to be 35 % of the first guess values which are close to the average estimate of the above three estimates of Beekmann and Derognat (2003), Hanna et al (1998) and Moore and Londergan (2001).…”

Section: Data Assimilation Algorithmmentioning

confidence: 99%

“…The uncertainties of vertical diffusion coefficients in ozone modeling have been estimated by Beekmann and Derognat (2003), Hanna et al (1998), and Moore and Londergan (2001) and range from 25 to 50 %. We estimated the uncertainty of vertical diffusion coefficients to be 35 % of the first guess values which are close to the average estimate of the above three estimates of Beekmann and Derognat (2003), Hanna et al (1998) and Moore and Londergan (2001). Also with reference to the studies of Hanna et al (1998), and Moore and Londergan (2001), the uncertainty of the modeled photolysis rates was estimated to be 30 %.…”

Section: Data Assimilation Algorithmmentioning

confidence: 99%

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Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas

et al. 2016

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Abstract. This study investigates a cross-variable ozone data assimilation (DA) method based on an ensemble Kalman filter (EnKF) that has been used in the companion study to improve ozone forecasts over Beijing and surrounding areas. The main purpose is to delve into the impacts of the crossvariable adjustment of nitrogen oxide (NO x ) emissions on the nitrogen dioxide (NO 2 ) forecasts over this region during the 2008 Beijing Olympic Games. A mixed effect on the NO 2 forecasts was observed through application of the cross-variable assimilation approach in the real-data assimilation (RDA) experiments. The method improved the NO 2 forecasts over almost half of the urban sites with reductions of the root mean square errors (RMSEs) by 15-36 % in contrast to big increases of the RMSEs over other urban stations by 56-239 %. Over the urban stations with negative DA impacts, improvement of the NO 2 forecasts (with 7 % reduction of the RMSEs) was noticed at night and in the morning versus significant deterioration during daytime (with 190 % increase of the RMSEs), suggesting that the negative data assimilation impacts mainly occurred during daytime. Idealdata assimilation (IDA) experiments with a box model and the same cross-variable assimilation method confirmed the mixed effects found in the RDA experiments. In the same way, NO x emission estimation was improved at night and in the morning even under large biases in the prior emission, while it deteriorated during daytime (except for the case of minor errors in the prior emission). The mixed effects observed in the cross-variable data assimilation, i.e., positive data assimilation impacts on NO 2 forecasts over some urban sites, negative data assimilation impacts over the other urban sites, and weak data assimilation impacts over suburban sites, highlighted the limitations of the EnKF under strong nonlinear relationships between chemical variables. Under strong nonlinearity between daytime ozone concentrations and NO x emissions uncertainties (with large biases in the a priori emission), the EnKF may come up with inefficient or wrong adjustments to NO x emissions. The present findings reveal that bias correction is essential for the application of the EnKF in dealing with the data assimilation problem over strong nonlinear system.

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Section: Data Assimilation Algorithmsupporting

confidence: 52%

Section: Data Assimilation Algorithmmentioning

confidence: 99%

Section: Data Assimilation Algorithmmentioning

confidence: 99%

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Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas

et al. 2016

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“…The Monte Carlo simulation is considered to be the best method of assessing uncertainty distributions of the pollution concentrations and therefore often used for this purpose [30,31]. Error propagation method is another way to approach this issue.…”

Section: Discussionmentioning

confidence: 99%

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Holnicki

Nahorski

2015

Environ Model Assess

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Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM 10 , PM 2.5 , NO x , SO 2 , Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.

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“…[52][53][54][55] Regional models continue to be updated with improved representations of atmospheric processes, for example, the comprehensive air quality model with extensions (CAMx), 56 the community multiscale air quality model (CMAQ), 57 the SARMAP air quality model (SAQM), 58 the urban to regional multiscale air quality model (URM), 59 and the multiscale air quality simulation platform (MAQSIP). 60 The operational, diagnostic, and mechanistic procedures used to evaluate models and their input processors continue to be refined.…”

Section: Discussionmentioning

confidence: 99%

Air Quality Modeling and Decisions for Ozone Reduction Strategies

Roth

Reynolds

Tesche³

2005

Journal of the Air & Waste Management Association

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Despite the widespread application of photochemical air quality models (AQMs) in U.S. state implementation planning (SIP) for attainment of the ambient ozone standard, documentation for the reliability of projections has remained highly subjective. An "idealized" evaluation framework is proposed that provides a means for assessing reliability. Applied to 18 cases of regulatory modeling in the early 1990s in North America, a comparative review of these applications is reported. The intercomparisons suggest that more than two thirds of these AQM applications suffered from having inadequate air quality and meteorological databases. Emissions representations often were unreliable; uncertainties were too high. More than two thirds of the performance evaluation efforts were judged to be substandard compared with idealized goals. Meteorological conditions chosen according regulatory guidelines were limited to one or two cases and tended to be similar, thus limiting the extent to which public policy makers could be confident that the emission controls adopted would yield attainment for a broad range of adverse atmospheric conditions. More than half of the studies reviewed did not give sufficient attention to addressing the potential for compensating errors. Corroborative analyses were conducted in only one of the 18 studies reviewed. Insufficient attention was given to the estimation of model and/or input database errors, uncertainties, or variability in all of the cases examined.

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Sampled Monte Carlo uncertainty analysis for photochemical grid models

Cited by 32 publications

References 12 publications

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Air Quality Modeling and Decisions for Ozone Reduction Strategies

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Sampled Monte Carlo uncertainty analysis for photochemical grid models

Cited by 32 publications

References 12 publications

Limitations of ozone data assimilation with adjustment of NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; emissions: mixed effects on NO&lt;sub&gt;2&lt;/sub&gt; forecasts over Beijing and surrounding areas

Limitations of ozone data assimilation with adjustment of NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; emissions: mixed effects on NO&lt;sub&gt;2&lt;/sub&gt; forecasts over Beijing and surrounding areas

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Air Quality Modeling and Decisions for Ozone Reduction Strategies

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Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas