Application of model output statistics to the GEM-AQ high resolution air quality forecast

The aim of the presented work was to analyse the impact of data stratification on the efficiency of the Model Output Statistics (MOS) methodology as applied to a high-resolution deterministic air quality forecast carried out with the GEM-AQ model. The following parameters forecasted by the GEM-AQ model were selected as predictors for the MOS equation: pollutant concentration, air temperature in the lowest model layer, wind speed in the lowest model layer, temperature inversion and the precipitation rate. A representative 2-year series were used to construct regression functions. Data series were divided into two subsets. Approximately 75% of the data (first 3 weeks of each month) were used to estimate the regression function parameters. Remaining 25% (last week of each month) were used to test the method (control period). The subsequent 12 months were used for method verification (verification period). A linear model fitted the function based on forecasted parameters to the observations. We have assumed four different temperature-based data stratification methods (for each method, separate equations were constructed). For PM10 and PM2.5, SO2 and NO2 the best correction results were obtained with the application of temperature thresholds in the cold season and seasonal distribution combined with temperature thresholds in the warm season. For the PM10, PM2.5 and SO2 the best results were obtained using a combination of two stratification methods separately for cold and warm seasons. For CO, the systematic bias of the forecasted concentrations was partly corrected. For ozone more sophisticated methods of data stratification did not bring a significant improvement.