Title: Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM over Portugal
Citation: ENVIRONMENTAL MODELING & ASSESSMENT vol. 18 no. 5 p. 533–546
Publisher: SPRINGER
Publication Year: 2013
JRC N°: JRC58827
ISSN: 1420-2026
URI: http://link.springer.com/article/10.1007%2Fs10666-013-9358-2
DOI: 10.1007/s10666-013-9358-2
Type: Articles in periodicals and books
Abstract: Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter (PM) was performed using monitoring data from 22 background sites. The ensemble approach, based on the mean and median of the five models, did not improve significantly the skill scores due to large deviations in each ensemble member. Different bias correction techniques, including a subtraction of the mean bias and a multiplicative ratio adjustment, were implemented and analysed. The obtained datasets were compared against the individual modelled outputs using the bias, the root mean square error (RMSE) and the correlation factor (r). In most cases, after the bias correction, the ensemble-mean and ensemble-median showed better skill than the highest scored model. The applied bias correction techniques also improved the skill of the individual models and work equally well over the entire range of observed O3 and PM values. The obtained results revealed that the best bias correction technique was the ratio adjustment with a 4-day training period, demonstrating significant improvements for both analysed pollutants. The increase of the ensemble skill found comprehends a bias reduction of 88% for O3, and 92% for PM10, and also a decrease of 23% for O3, and 43% for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach, non site specific technique, was also examined with successful skills comparing to the uncorrected ensemble, mainly regarding ozone.
JRC Directorate:Sustainable Resources

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