Comparison of Data Assimilation Methods for Assessing PM10 Exceedances on the European Scale

  • Bruce Denby
  • Martijn Schaap
  • Arjo Segers
  • Peter Builtjes
  • Jan Horálek
Conference paper
Part of the NATO Science for Peace and Security Series Series C: Environmental Security book series (NAPSC)


Two different data assimilation techniques have been applied to assess exceedances of the daily and annual mean limit values for PM10 on the regional scale in Europe. The two methods include a statistical interpolation method (SI), based on residual kriging after linear regression of the model, and ensemble Kalman filtering (EnKF). Both methods are applied using the LOTOS-EUROS model. Observations for the assimilation and validation of the methods have been retrieved from the Airbase database using rural background stations only. The LOTOS-EUROS model is found to underestimate PM10 concentrations by a factor of 2. This large model bias is found to be prohibitive for the effective use of the EnKF methodology and a bias correction was required for the filter to function effectively. The results of the study show that both methods provide significant improvement on the model calculations when compared to an independent set of validation stations. The most effective methodology is found to be the statistical interpolation method.


Data assimilation air quality uncertainty ensemble Kalman filter kriging 


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Copyright information

© Springer Science + Business Media B.V 2008

Authors and Affiliations

  • Bruce Denby
    • 1
  • Martijn Schaap
    • 2
  • Arjo Segers
  • Peter Builtjes
    • 3
  • Jan Horálek
  1. 1.Urban Environment and Industry DepartmentNorwegian Institute for Air Research (NILU)KjellerNorway
  2. 2.TNO, B&OAH ApeldoornThe Netherlands
  3. 3.Air Quality and ClimateTNOAH ApeldoornThe Netherlands

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