Improving ozone forecasts over Europe by synergistic use of the LOTOS-EUROS chemical transport model and in-situ measurements

In this paper we investigate the added value of assimilation of ozone in-situ measurements for both re-analysis and forecasting purposes. Various simulations were performed using the LOTOS-EUROS chemical transport model and an Ensemble Kalman Filter (EnKF) to assimilate measured ozone surface concentrations over Europe, for spring and summer of 2007. The results for the re-analysis of ozone show a significant improvement in the LOTOS-EUROS performance score when compared to measurements. The average correlation coefficient for the daily maximum ozone concentration improves from 0.72 to 0.83. Similarly, the average Root Mean Square Error (RMSE) for the daily maximum ozone concentration is reduced from 20.8 to 16.9 μg m−3. ee running model performs well in forecast mode and the agreement between in-situ and modeled ozone concentration is good. The average temporal correlation coefficient ranges from 0.62 for the first day forecast to 0.61 for the third day forecast. Based on these results, assimilated fields were used to initialize the forecast. As for the re-analysis a better comparison between model and observation was observed. The mean correlation coefficient increased by 0.07 and the averaged RMSE decreased by 0.65 μg m−3. However, the addition of an inheritance scheme to import additional information from the data assimilation to the forecast did not significantly improve the concentration fields.