Kalman filter-based air quality forecast adjustment

We evaluate a Kalman Filter (KF) based adaptive regression method for the correction of deterministic air quality forecasts. In this method, corrected forecast concentrations are obtained by linear regression, using the free model forecast values as predictors, and estimating the regression coefficients dynamically by means of the KF technique. Basically, this method exploits the information regarding the mismatch between the deterministic forecast and observations of the prior period to calculate regression coefficients for the correction of the next forecast step. sidered model output generated by the deterministic regional air quality model AURORA over northern Belgium for the year 2007, together with observed values at a few tens of stations. It was found that, for daily mean PM10 concentrations, and averaged over the monitoring stations, the correction scheme reduced the root mean square error from 15.9 to 10.5 μg m−3, largely thanks to the bias reduction from 8.8 to 0.5 μg m−3. The correlation coefficient increased from 0.65 to 0.73. For daily maximum O3 concentrations, the root mean square error was reduced from 25.9 to 17.2 μg m−3, the bias from 7.9 to 0.2 μg m−3, and the correlation coefficient increased from 0.60 to 0.79. o implemented a non-adaptive linear regression scheme to the same data. It was found that the adaptive regression method outperformed this simpler scheme consistently, demonstrating the relevance of the dynamic KF-based method for use in the correction of deterministic air quality forecasts.