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 95(th) 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.