The help of Model Output Statistic tool in Alert air quality system

The golden rule of creating a machine learning algorithm is to handle as much data as possible under consistent conditions. In the field of air quality, these models can be used for short-term air quality forecasting, helping predict high pollution peaks and alerting residents in cases where pollutant concentrations exceed legal or World Health Organization (WHO) limits.

For daily air quality predictions and short-term forecasts, the Slovenian Environment Agency uses the CAMx dispersion model, which is frequently upgraded with new input data. Based on the model results and expert opinions, we inform the public about air quality levels and issue alerts when concentrations reach the limit values. The Copernicus Atmosphere Monitoring Service (CAMS) has developed Model Output Statistics (MOS), which downscale air quality forecasts produced by regional ensemble models and incorporate measurement values from observation sites across Europe.

In our study, we evaluated and compared CAMS MOS forecasts with forecasts from the Slovenian operational CAMx model and raw CAMS ensemble forecasts at Slovenian observational sites. The evaluation was conducted using time series plots, scatter plots, and Taylor diagrams, alongside statistical metrics such as the fraction of forecasts within a factor of two (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized gross error (NMGE), root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (COE), and index of agreement (IOA).

Our findings indicate that CAMS MOS forecasts are almost always more accurate than raw CAMS or CAMx model predictions, particularly for the MOS day-1 forecast. However, for PM₁₀ and PM_2.5, longer-term MOS forecasts (day-2, day-3, and day-4) were less accurate than RAW and/or CAMx predictions based on certain statistical measures. When analysing individual stations, we observed occasional instances where MOS forecasts were less accurate for specific pollutants.

CAMS MOS is a useful tool for improving the national alert system, helping to warn residents about poor air quality conditions and enabling timely protective measures. Consequently, our results suggest that the MOS day-1 forecast can serve as a valuable additional tool for issuing daily PM₁₀ forecasts and, for some stations, even O₃ daily maxima predictions. However, all MOS forecasts should be used with an awareness of their limitations—for example, the time lag in predicting high PM₁₀ episodes and the systematic underestimation of O₃ daily maxima at many Slovenian stations.