Uncertainties in Episodic Ozone Modeling Stemming from Uncertainties in the Meteorological Fields

Abstract

This paper examines the uncertainty associated with photochemical modeling using the Variable-Grid Urban Airshed Model (UAM-V) with two different prognostic meteorological models. The meteorological fields for ozone episodes that occurred during 17-20 June, 12-15 July, and 30 July-2 August in the summer of 1995 were derived from two meteorological models, the Regional Atmospheric Modeling System (RAMS) and the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The simulated ozone concentrations from the two photochemical modeling systems, namely, RAMS/ UAM-V and MM5/UAM-V, are compared with each other and with ozone observations from several monitoring sites in the eastern United States. The overall results indicate that neither modeling system performs significantly better than the other in reproducing the observed ozone concentrations. The results reveal that there is a significant variability, about 20% at the 95% level of confidence, in the modeled 1-h ozone concentration maxima from one modeling system to the other for a given episode. The model-to-model variability in the simulated ozone levels is for most part attributable to the unsystematic type of errors. The directionality for emission controls (i.e., NO x versus VOC sensitivity) is also evaluated with UAM-V using hypothetical emission reductions. The results reveal that not only the improvement in ozone but also the VOC-sensitive and NO x-sensitive regimes are influenced by the differences in the meteorological fields. Both modeling systems indicate that a large portion of the eastern United States is NO x limited, but there are model-to-model and episode-to-episode differences at individual grid cells regarding the efficacy of emission reductions.

Modelling constraints on the emission inventory and on vertical dispersion for CO and SO<sub>2</sub> in the Mexico City Metropolitan Area using Solar FTIR and zenith sky UV spectroscopy

“…Improvements in the specification of the vertical diffusion coefficients were suggested but evaluation was limited due to the lack of measurements of the vertical concentration profiles. Biswas and Rao (2001) report substantial differences between different models adding to uncertainties in ozone simulations and Roelofs et al (2003) suggest that coarse vertical resolution may lead to excessive dispersion. Brandt et al (1998) analysed different vertical dispersion schemes and found that the simplest scheme of high vertical dispersion yielded the best results, suggesting that non-local dispersion is an important factor.…”

Development and Validation of a Predictive Model to Assess the Impact of Coastal Operations on Urban Scale Air Quality

“…Model predictions did not exceed 80 ppb. This is typical problem observed in other studies (Bauer and Langmann, 2002, Biswas and Rao, 2001, Rao and Sistla, 1993, Sillman et al, 1998, Tory et al, 2004 and is generally corrected by adjusting the inventory to "calibrate" the model. Alternatively, this may be due to overestimating the turbulence transfer.…”

Air Quality Modeling and Decisions for Ozone Reduction Strategies

“…[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.…”

“…68 -72 Corroborative analyses have been increasingly used to confirm modeling results. 54,73 Independent peer reviews of modeling have been performed in a few areas to provide increased confidence in the modeling process and the interpretation of results. 74 We note that as PAQSMs are used more widely as a key component to air quality management, enhanced effort is necessary to provide modeling results that contain a certification of reliability, with defined uncertainties.…”