[1] The real-time forecasts of ozone (O 3 ) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 monitoring stations throughout the eastern United States and southern Canada. One of the first ever real-time ensemble O 3 forecasts, created by combining the seven separate forecasts with equal weighting, is also evaluated in terms of standard statistical measures, threshold statistics, and variance analysis. The ensemble based on the mean of the seven models and the ensemble based on the median are found to have significantly more temporal correlation to the observed daily maximum 1-hour average and maximum 8-hour average O 3 concentrations than any individual model. However, root-mean-square errors (RMSE) and skill scores show that the usefulness of the uncorrected ensembles is limited by positive O 3 biases in all of the AQFMs. The ensembles and AQFM statistical measures are reevaluated using two simple bias correction algorithms for forecasts at each monitor location: subtraction of the mean bias and a multiplicative ratio adjustment, where corrections are based on the full 53 days of available comparisons. The impact the two bias correction techniques have on RMSE, threshold statistics, and temporal variance is presented. For the threshold statistics a preferred bias correction technique is found to be model dependent and related to whether the model overpredicts or underpredicts observed temporal O 3 variance. All statistical measures of the ensemble mean forecast, and particularly the bias-corrected ensemble forecast, are found to be insensitive to the results of any particular model. The higher correlation coefficients, low RMSE, and better threshold statistics for the ensembles compared to any individual model point to their preference as a real-time O 3 forecast.

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The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality.The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined meteorological variables for regional air quality prediction. Meteorological observations are critical to effective air quality prediction, yet meteorological observing systems are designed to support prediction of severe weather, not the subtleties of adverse air quality. Three-dimensional meteorological and chemical observations and advanced data assimilation schemes are essential. In the same way, it is important to develop high-resolution and self-consistent databases for air quality modeling; these databases should include land use, vegetation, terrain elevation, and building morphology information, among others. New work in the area of chemically adaptive grids offers significant promise and should be pursued. The quantification and effective communication of forecast uncertainty are still in their early stages and are very important for decision makers; this also includes the visualization of air quality and meteorological observations and forecasts. Research is also needed to develop effective metrics for the evaluation and verification of air quality forecasts so that users can understand the strengths and weaknesses of various modeling schemes. Last, but not of least importance, is the need to consider the societal impacts of air quality forecasts and the needs that they impose on researchers to develop effective and useful products.

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