Abstract
Regional-scale air pollution models are routinely being used world-wide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology-atmospheric chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry, and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because these stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology-air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were "perfect". To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8-hr ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States. The results reveal that the expected root mean square error at the median and 95th percentile is about 2 ppb and 5 ppb, respectively, even for "perfect" air quality models driven with "perfect" input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state-of-the-science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.
Highlights
Confidence in model estimates of pollutant distributions is established through direct comparisons of modeled concentrations with corresponding observations made at discrete locations for retrospective cases. Pinder et al (2008) discussed the reducible uncertainties that are attributable to the errors in model input data as well as our incomplete or inadequate understanding of the relevant atmospheric processes
Inherent or irreducible uncertainties stem from our inability to properly characterize the stochastic nature of the atmosphere (Wilmott, 1981; Wilmott et al, 1985; Fox, 1984; Rao et al, 1985, 2011a, b; Dennis et al, 2010) and from the incommensurability associated with comparing the volumeaveraged model estimates with point measurements (e.g., McNair et al, 1996; Swall and Foley, 2009)
In achieving compliance with the ozone standard, this paper is aimed at quantifying the model performance errors to be expected at each monitoring site over contiguous United States (CONUS) even from perfect regional-scale ozone models driven with perfect input data from the ever-present stochastic nature of the atmosphere
Summary
Confidence in model estimates of pollutant distributions is established through direct comparisons of modeled concentrations with corresponding observations made at discrete locations for retrospective cases. Pinder et al (2008) discussed the reducible (i.e., structural and parametric) uncertainties that are attributable to the errors in model input data (e.g., meteorology, emissions, and initial and boundary conditions) as well as our incomplete or inadequate understanding of the relevant atmospheric processes (e.g., chemical transformation, planetary boundary layer evolution, transport and dispersion, deposition, rain, and clouds). Inherent or irreducible (random or unsystematic) uncertainties stem from our inability to properly characterize the stochastic nature of the atmosphere (Wilmott, 1981; Wilmott et al, 1985; Fox, 1984; Rao et al, 1985, 2011a, b; Dennis et al, 2010) and from the incommensurability associated with comparing the volumeaveraged model estimates with point measurements (e.g., McNair et al, 1996; Swall and Foley, 2009). Given the presence of the irreducible uncertainties, precise replication of observed concentrations or their changes by the models cannot be expected Rao et al.: On the limit to the accuracy of regional-scale air quality models
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