METEOROLOGICAL FACTORS OF OZONE PREDICTABILITY AT HOUSTON, TEXAS
Roland R. Draxler
Journal of the Air and Waste Management Association, 50:259-271, February 2000.
Implications - The purpose of the paper is to provide some understanding of the uncertainties meteorological data introduce into the calculation of maximum ozone concentrations. The paper is intended to provide guidance to managers on the resources that might be needed to develop an operational ozone forecast system in the light of meteorological uncertainty and the tradeoffs involved regarding model complexity, accuracy, and precision.
Abstract - Several ozone modeling approaches were investigated to determine if uncertainties in the meteorological data would be sufficiently large to limit the application of physically realistic ozone forecast models. Three diagnostic schemes were evaluated for the period of May through September of 1997 for Houston, Texas. Correlations between measured daily maximum and model calculated ozone air concentrations were found to be 0.70 using a linear regression model, 0.65 using a non-advective box model, and 0.49 using a 3-Dimensional transport and dispersion model. Although the regression model had the highest correlation, it showed substantial under-estimates of the highest concentrations. The box model results were the most similar to the regression model and did not show as much underestimation. The more complex 3-D modeling approach yielded the worst results, likely resulting from ozone maxima that were driven by local factors rather than by the transport of pollutants from outside of the Houston domain. The highest ozone concentrations at Houston were associated with light winds and meandering trajectories. A comparison of the gridded meteorological data used by the 3-D model to the observations showed that the wind direction and speed values at Houston differed most on those days in which the ozone underestimations were the greatest. These periods also tended to correspond with poor precipitation and temperature estimates. It is concluded that better results are not just obtained through additional modeling complexity but there needs to be a comparable increase in the accuracy of the meteorological data.
Email me the full report