edu.ucar.opensky::articles:23608 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:02:03.967478 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d72j6g44 A novel ensemble design for probabilistic predictions of fine particulate matter over the contiguous United States (CONUS) 2020-08-27T00:00:00Z publication https://n2t.org/ark:/85065/d72j6g44 https://doi.org/10.1029/2020JD032554 Kumar, Rajesh author Alessandrini, Stefano author Hodzic, Alma author Lee, Jared author UCAR/NCAR - Library publisher This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near-surface fine particulate matter (PM2.5) 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2020 American Geophysical Union. None eng geoscientificInformation