On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations

Abstract

Abstract The study analyses the added value of data assimilation for short-term air quality forecasting by means of three modelling experiments with sulphur oxides. Two ways of utilising the observations are considered: determination of the optimal model initial state and adjustment of the emission fluxes of atmospheric pollutants. It is demonstrated that the influence of the initial conditions on the predicted SOx concentrations disappears within less than a day in European-scale applications. Adjusting the emission fluxes had a longer lasting impact on the model results, frequently covering the whole forecast window. The two-week long data assimilation exercise for Southern Europe showed that the largest improvement of the model score with regard to individual monitoring sites was obtained for the stations with the worst initial model-measurement agreement. With the emission adjustment, a major improvement was achieved for the stations near the Etna volcano, the strongest source in the area, where the SO 2 emission was reduced by almost 50% as a result of the data assimilation.