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 PM2.5modeling 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 PM2.5over the CONUS with the largest underestimation over the western CONUS owing to negative PM2.5bias 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 PM2.5very 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.
The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two‐way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF‐CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF‐CMAQ's performance using data from the National Climatic Data Center and Environmental Protection Agency Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation's meteorological and chemical performance while also resolving key details in areas of high exposure and impact, that is, urban environments. At 1.3 km, we find that most air quality‐relevant meteorological components of WRF‐CMAQ perform at or above community benchmarks. WRF‐CMAQ's chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2and O3are highly correlated with observations (
- Award ID(s):
- 1848683
- NSF-PAR ID:
- 10408204
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 128
- Issue:
- 6
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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