How accurate are operational dust models in predicting Particulate Matter (PM) levels in the Eastern Mediterranean Region? Insights from PM Surface Concentrations

Abstract. This study provides the first comprehensive assessment of eleven operational dust forecast models and a multi-model ensemble in predicting ground-level Particulate Matter (PM) concentrations in the Eastern Mediterranean Region (EMR), with a focus on Cyprus, Greece, and Israel. Ground-based observations from regional background stations support model performance assessment across different PM fractions (PM10, PM2.5, and coarse particles), using established statistical metrics (correlation coefficient, R, Mean Bias, MB, and Root Mean Square Error, RMSE). The results reveal substantial variability in accuracy, with R values ranging from −0.24 to 0.91 depending on site and event subset. NASA-GEOS consistently achieves the highest correlation (R = 0.71 at Cyprus), indicating accurate representation of dust transport. In contrast, SILAM and EMA-REG4 perform poorly, with low correlations (R = 0.10 and −0.24, respectively) and significant estimation errors (MB = −90.34 µg/m³ for EMA-REG4). The NOA-WRF model effectively captures extreme dust events, with R = 0.91 during the 95th percentile of PM concentrations in Greece. Most models perform better for coarse PM, with the BOOT methodology indicating reduced scatter and bias during dust storm days. However, no model performs optimally across all sites and conditions, highlighting the need for location-specific tuning and evaluation. The study underscores the importance of refining model configurations and improving parameterizations to enhance forecast accuracy. Future efforts should incorporate localized data and further develop region-specific models to improve the operational use of these systems in early warning protocols for mitigating public health impacts.