A novel ensemble design for probabilistic predictions of fine particulate matter 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.