Assessment of Kalman filter bias-adjustment technique to improve the simulation of ground-level ozone over Spain

The CALIOPE air quality modelling system has been used to diagnose ground level O(3) concentration for the year 2004, over the Iberian Peninsula. We investigate the improvement in the simulation of daily O(3) maximum by the use of a post-processing such as the Kalman filter bias-adjustment technique. The Kalman filter bias-adjustment technique is a recursive algorithm to optimally estimate bias-adjustment terms from previous measurements and model results. The bias-adjustment technique improved the simulation of daily O(3) maximum for the entire year and the all the stations considered over the whole domain. The corrected simulation presents improvements in statistical indicators such as correlation, root mean square error, mean bias, and gross error. After the post-processing the exceedances of O(3) concentration limits, as established by the European Directive 2008/50/CE, are better reproduced and the uncertainty of the modelling system, as established by the European Directive 2008/50/CE, is reduced from 20% to 7.5%. Such uncertainty in the model results is under the established EU limit of the 50%. Significant improvements in the O(3) timing and amplitude of the daily cycle are also observed after the post-processing. The systematic improvements in the O(3) maximum simulations suggest that the Kalman filter post-processing method is a suitable technique to reproduce accurate estimate of ground-level O(3) concentration. With this study we evince that the adjusted O(3) concentrations obtained after the post-process of the results from the CALIOPE system are a reliable means for real near time O(3) forecasts.