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A simple data assimilation algorithm based on statistical interpolation hasbeen developed and coupled to a long-range chemistry transport model, theDanish Eulerian Operational Model (DEOM), applied for air pollutionforecasting at the National Environmental Research Institute (NERI), Denmark.In this paper, the algorithm and the results from experiments designed tofind the optimal setup of the algorithm are described. The algorithm has beendeveloped and optimized via eight different experiments where the resultsfrom different model setups have been tested against measurements from theEMEP (European Monitoring and Evaluation Programme) network covering ahalf-year period, April–September 1999. The best performing setup of thedata assimilation algorithm for surface ozone concentrations has been found,including the combination of determining the covariances using theHollingsworth method, varying the correlation length according to the numberof adjacent observation stations and applying the assimilation routine atthree successive hours during the morning. Improvements in the correlationcoefficient in the range of 0.1 to 0.21 between the results from thereference and the optimal configuration of the data assimilation algorithm,were found. The data assimilation algorithm will in the future be used in theoperational THOR integrated air pollution forecast system, which includes theDEOM

Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM