I. Djalalova

Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004

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.

Analog-Based Postprocessing Methods for Air Quality Forecasting

Two new postprocessing methods based on analogs are proposed to reduce the systematic and random errors of air quality prediction. The analog of a forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast. The first method is the weighted average of the observations that verified when the best analogs were valid (AN). The second method consists in applying a postprocessing algorithm inspired by the Kalman filter (KF) to AN (KFAN). The AN and KFAN are tested for ground level ozone and PM2.5 0–48 h predictions from the Community Multiscale Air Quality (CMAQ) model, with observations from 1602 surface stations from the EPA AirNow network over the continental United States for a 1-year period. Preliminary results of the new methods include a large reduction of the systematic and random errors of the direct model output, with an increase of the correlation between observations and predictions at all forecast lead times.

Use of Remote Sensors in Air Quality Monitoring and Prediction

Air quality is a highly interdisciplinary problem dependent on both chemical and meteorological processes. The investigation of atmospheric pollution requires information about the types of emitted chemical compounds, their concentrations, their mutual interaction under different ambient conditions, and finally about their transport and diffusion into the atmosphere. This chapter introduces the effects of meteorology on air quality and the use of meteorological data from remote sensors in air quality monitoring and prediction. The change in air quality over the course of a diurnal cycle is described, explaining the role of atmospheric boundary layer and turbulence motion. Because of the need to measure the strength of the vertical mixing and the depth through which it occurs, as well as the vertical profile of wind speed and direction throughout the lowest several kilometers of the atmosphere, remote sensors, such as lidars and wind profiling radars, are demonstrated to be extremely valuable for assessing and predicting air quality. Few examples of air quality experiments are presented to demonstrate how remote sensors can lead to new insights on the local meteorology control on air pollutant concentrations and the benefit of meteorological data assimilation in air quality prediction.