Predicting particulate matter ( P M 10 ) levels in Morocco: a 5-day forecast using the analog ensemble method

The accurate prediction of particulate matter $d<10\mu \text{m}$ ( ${\mathrm{PM}}_{10}$ ) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of ${\mathrm{PM}}_{10}$ over Morocco for 5 days. The analog ensemble (AnEn) and the bias correction (AnEnBc) techniques were employed to post-process ${\mathrm{PM}}_{10}$ forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the root mean squared error (RMSE) decreased from 63.83 $\mu g/m^3$ in the original forecasts to 44.73 $\mu g/m^3$ with AnEn and AnEnBc, while the mean absolute error (MAE) reduced from 36.70 to 24.30 $\mu g/m^3$ . Additionally, the coefficient of determination ( $R^2$ ) increased more than twofold from 29.11 to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. The integrating reanalysis data and the utilization of the AnEn substantially improved the accuracy of ${\mathrm{PM}}_{10}$ 5-day forecasting in Morocco. This is the first application of this approach in Morocco and the Middle East and North Africa (MENA) and has the potential for translation into early and more accurate warnings of ${\mathrm{PM}}_{10}$ pollution events. The application of such approaches in environmental policies and public health decision-making can minimize the health impacts of air pollution.