Improving NOAA NAQFC PM2.5 predictions with a bias correction approach

AMS Citation:
Huang, J., and Coauthors, 2017: Improving NOAA NAQFC PM2.5 predictions with a bias correction approach. Weather and Forecasting, 32, 407-421, doi:10.1175/WAF-D-16-0118.1.
Resource Type:article
Title:Improving NOAA NAQFC PM2.5 predictions with a bias correction approach
Abstract: Particulate matter with an aerodynamic diameter less than or equal to 2.5 mu m (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.
Peer Review:Refereed
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OpenSky citable URL: ark:/85065/d7j9686g
Publisher's Version: 10.1175/WAF-D-16-0118.1
  • Jianping Huang
  • Jeffery McQueen
  • James Wilczak
  • Irina Djalalova
  • Ivanka Stajner
  • Perry Shafran
  • Dave Allured
  • Pius Lee
  • Li Pan
  • Daniel Tong
  • Ho-Chun Huang
  • Geoffrey DiMego
  • Sikchya Upadhayay
  • Luca Delle Monache - NCAR/UCAR
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