Data assimilation of ground-level ozone in Europe with a Kalman filter and chemistry transport model

Two Kalman filter algorithms, the reduced rank square root (RRSQRT) and the ensemble Kalman filter (ENKF), were implemented in this study. Both required, in general, a large number of EUROS model simulations for an assimilation. The observations consisted of hourly ozone data in a set of 135 ground-based stations in Europe for the period, June 1996. Half of these stations were used for the assimilation and the other half only for validation of the results. The combination between data assimilation (Kalman filter) and the atmospheric chemistry transport model, EUROS, gave more accurate results for boundary layer ozone than the EUROS model or measurements used separately. The average difference between assimilated and measured ozone concentrations decreased from 27.4 to 20.5 μg m⁻³ for the average of the stations used for validation in Europe. Both algorithms tend to converge to about the same accuracy, with an increasing number of EUROS model runs. About 10–20 EUROS model calculations were found sufficient for a good assimilation. The results are supported by a number of simulations that also reveal a local character for the assimilation process.