Abstract
We describe the implementation of a Kalman filter-based adjustment scheme for the correction of deterministic air quality forecast results. This scheme exploits the information on the mismatch between the deterministic forecast and observations of the prior period to calculate correction regression coefficients for the next forecast step. This method was applied to ground-level daily O3 and PM10 concentration fields simulated by the regional-scale deterministic air quality model AURORA for the year 2007, for a domain covering Belgium, and employing observations from the AirBase data archive. From a cross-validation analysis, it was found that the correction method improved the accuracy of daily mean PM10 and daily maximum O3 concentrations substantially.
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Acknowledgments
This work was carried out with support of the European Commission, within the LIFE+ project ATMOSYS and the FP7 project PASODOBLE.
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© 2014 Springer Science+Business Media Dordrecht
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De Ridder, K., Kumar, U., Lauwaet, D., Van Looy, S., Lefebvre, W. (2014). Kalman Filter-Based Air Quality Forecast Adjustment. In: Steyn, D., Builtjes, P., Timmermans, R. (eds) Air Pollution Modeling and its Application XXII. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5577-2_30
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DOI: https://doi.org/10.1007/978-94-007-5577-2_30
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