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

TLDR

The study evaluates a first real‑time ensemble ozone forecast, formed by equally weighting seven air‑quality models, and examines how two bias‑correction methods affect its statistical performance. The authors statistically assessed the seven real‑time ozone forecasts and their equal‑weighted ensemble against 53 days of AIRNow observations from ~340 stations, then re‑evaluated them using mean‑bias subtraction and multiplicative ratio bias corrections. The ensemble forecasts achieved higher temporal correlation, lower RMSE, and better threshold statistics than any individual model, and bias correction further improved performance, demonstrating the ensemble’s superiority for real‑time ozone prediction.

Abstract

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.