A Novel Ensemble Design for Probabilistic Predictions of Fine Particulate Matter Over the Contiguous United States (CONUS)

This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near-surface fine particulate matter (PM_2.5) over the contiguous United States (CONUS). Our ensemble design captures three key sources of uncertainties in PM_2.5 modeling including meteorology, emissions, and secondary organic aerosol (SOA) formation. Twenty-four ensemble members were simulated using the Community Multiscale Air Quality (CMAQ) model during January, April, July, and October 2016. The raw ensemble mean performed better than most of the ensemble members but underestimated the observed PM_2.5 over the CONUS with the largest underestimation over the western CONUS owing to negative PM_2.5 bias in nearly all the members. To improve the ensemble performance, we calibrated the raw ensemble using model output statistics (MOS) and variance deficit methods. The calibrated ensemble captured the diurnal and day-to-day variability in observed PM_2.5 very well and exhibited almost zero mean bias. The mean bias in the calibrated ensemble was reduced by 90–100% in the western CONUS and by 40–100% in other parts of the CONUS, compared to the raw ensemble in all months. The corresponding reduction in root-mean-square error (RMSE) was 13–40%. The calibrated ensemble also showed 30% improvement in the RMSE and spread matching compared to the raw ensemble. We have also shown that a nine-member ensemble based on combinations of three meteorological and three anthropogenic emission scenarios can be used as a smaller subset of the full ensemble when sufficient computational resources are not available in the operational setting.