Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical\natmospheric models. In this contribution, we present the development of a MOS algorithm\nto improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are\nobtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen\nDioxide (NO2), Particulate Matter (PM10) and Ozone (03) have been the pollutants considered and\nthe methodology has been applied on statistical values of these pollutants according to regulatory\nlevels. Four MOS algorithms have been developed, characterized by different approaches in relation\nwith seasonal stratification and stratification according to the measurement stations considered.\nAlgorithms have been compared among them in order to obtain a MOS that reduces the\nforecast uncertainties. Results obtained show that the best MOS designed increases the accuracy\nof NO2 maximum 1-h daily value forecast from 71% to 75%, from 68% to 81% in the case of daily\nvalues of PM10, and finally, the accuracy of O3 maximum 1-h daily value from 79% to 87%.
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