Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal
- 430 Downloads
Five air quality models were applied over Portugal for July 2006 and used as ensemble members. Each model was used, with its original set up in terms of meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and the ensemble of ozone (O3) and particulate matter (PM) is 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 coefficient. 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 in the ensemble skill found comprehends a bias reduction of 88 % for O3, and 92 % for PM10, and also a decrease in 23 % for O3 and 43 % for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach was also examined with successful skills comparing to the uncorrected ensemble for both pollutants.
KeywordsAir quality modelling Additive bias correction Multiplicative bias correction Spatial bias correction
The authors acknowledge the Portuguese Environmental Protection Agency for the observational dataset support. Thanks are extended to the Portuguese ‘Ministério da Ciência, da Tecnologia e do Ensino Superior’ for the financing of ENSEMBLAIR (POCI/AMB/66707/2006) project, for the PhD grant of V. Martins (SFRH/BD/39799/2007) and of Isabel Ribeiro (SFRH/BD/60370/2009) and the post doc grant of J. Ferreira (SFRH/BPD/40620/2007). COST ES0602 is also acknowledged. In addition, this work was also supported by the German Academic Exchange Service Program and the CRUP—Accoes Integradas Luso-Alemãs.
- 3.McKeen, S., et al. (2005). Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004. Journal of Geophysical Research, 110(D21).Google Scholar
- 4.Vautard, R., Builtjes, P., Thunis, P., Cuvelier, K., Bedogni, M., Bessagnet, B., et al. (2007). Evaluation and intercomparison of ozone and PM10 simulations by several chemistry-transport models over 4 European cities within the City-Delta project. Atmospheric Environment, 41, 173–188.CrossRefGoogle Scholar
- 15.Ciaramella, A., Giunta, G., Riccio, A. & Galmarini, S. (2009). Independent model selection for ensemble dispersion forecasting. Studies in computational intelligence, Vol. 245 (pp. 213–231). Berlin/Heidelberg: Springer.Google Scholar
- 19.ENVIRON (2008) User’s guide to the Comprehensive Air Quality model with extensions (CAMx) version 4.50 (May, 2008). http://www.camx.com.
- 22.Strunk, A., Ebel, A., Elbern, H., Friese, E., Goris, N. & Nieradzik, L. P. (2010). Four-dimensional variational assimilation of atmospheric chemical data—Application to regional modelling of air quality. In: Lecture notes in computer science (LNCS), Vol. 5910 (pp. 222–229). Berlin: Springer.Google Scholar
- 27.Monteiro, A., Borrego, C., Miranda, A. I., Góis, V., Torres, P. & Perez, A.T. (2007). Can air quality modelling improve emission inventories? In: Proceedings of the 6th International Conference on Urban Air Quality, 26–30 March, Limassol, Cyprus, pp. 13–14.Google Scholar
- 29.Tchepel, O., Ferreira, J. & Borrego, C. (2009). Analysis of long-range transport of aerosols for Portugal using a 3D Chemical Transport Model and OMI measurements. Atmospheric Science Conference, ESA, Barcelona, Spain, 7–11 September 2009 (poster presentation).Google Scholar
- 31.Stull, R. B. (1988). An introduction to boundary-layer meteorology. Dordrecht: Kluwer.Google Scholar
- 33.Talagrand, O., Vautard, R. & Strauss, B. (1998). Evaluation of probabilistic prediction systems. Proceedings of the Seminar on Predictability, Reading, UK, ECMWF, pp. 1–26.Google Scholar