Comparison of two data assimilation methods for assessing PM 10 exceedances on the European scale

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. For the period studied, 2003, 127 suitable stations were available. 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 total root mean square
error of the daily mean concentrations of PM10 at the validation stations was reduced from
16.7 ug m-3 for the model to 9.2 ug m-3 using SI and to 13.5 ug m-3 using EnKF. Similarly
correlation (R2) is also significantly improved from 0.21 for the model to 0.66 using SI and
0.41 using EnKF. Significant improvement in the annual mean and number of exceedance
days of PM10 is also seen. In addition to the validation of the methods, maps of exceedances
and their associated uncertainty are presented. The most effective methodology is found to
be the statistical interpolation method. The application of EnKF is novel and yields
promising results, although its application to PM10 still needs to be improved