Case studies with multi-source data assimilation

Existing and newly developed observation operators and error estimates will be combined and their mutual compatibility and complementarity will be assessed in all models involved, as well as the added value of each type of measurements. Experience of multi-pollutant air quality assimilation will be taken into account to identify and suppress potential “competition” between the datasets of different type. The impact and synergies of assimilating the data of different types into an ensemble of independent models will be assessed – and the harmonization procedure towards the coherent unified products will be suggested.