Enhancing Air Quality Forecasts over Catalonia (Spain) Using Model Output Statistics

Abstract

Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen Dioxide (NO 2), Particulate Matter (PM 10) and Ozone (0 3) have been the pollutants considered and the methodology has been applied on statistical values of these pollutants according to regulatory levels. Four MOS algorithms have been developed, characterized by different approaches in relation with seasonal stratification and stratification according to the measurement stations considered. Algorithms have been compared among them in order to obtain a MOS that reduces the forecast uncertainties. Results obtained show that the best MOS designed increases the accuracy of NO2 maximum 1-h daily value forecast from 71% to 75%, from 68% to 81% in the case of daily values of PM 10 , and finally, the accuracy of O 3 maximum 1-h daily value from 79% to 87%.