Abstract. A simple data assimilation algorithm based on statistical interpolation has been developed and coupled to a long-range chemistry transport model, the Danish Eulerian Operational Model (DEOM), applied for air pollution forecasting at the National Environmental Research Institute (NERI), Denmark. In this paper, the algorithm and the results from experiments designed to find the optimal setup of the algorithm are described. The algorithm has been developed and optimized via eight different experiments where the results from different model setups have been tested against measurements from the EMEP (European Monitoring and Evaluation Programme) network covering a half-year period, April–September 1999. The best performing setup of the data assimilation algorithm for surface ozone concentrations has been found, including the combination of determining the covariances using the Hollingsworth method, varying the correlation length according to the number of adjacent observation stations and applying the assimilation routine at three successive hours during the morning. Improvements in the correlation coefficient in the range of 0.1 to 0.21 between the results from the reference and the optimal configuration of the data assimilation algorithm, were found. The data assimilation algorithm will in the future be used in the operational THOR integrated air pollution forecast system, which includes the DEOM.
Improving air pollution forecast with ubiquitous mobile sensor network
