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PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model (vol 108, pg 76, 2015)
  • Published Date:
    2015
  • Source:
    Atmospheric Environment, 119, 430-430.
Filetype[PDF-4.87 MB]


Details:
  • Description:
    A new post-processing method for surface particulate matter (PM2.5) predictions from the National Oceanic and Atmospheric Administration (NOAA) developmental air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model is described. It includes three main components: • A real-time quality control procedure for surface PM2.5 observations; • Model post-processing at each observational site using historical forecast analogs and Kalman filtering; • Spreading the forecast corrections from the observation locations to the entire gridded domain. The methodology is tested using 12 months of CMAQ forecasts of hourly PM2.5, from December 01, 2009 through November 30, 2010. The model domain covers the contiguous USA, and model data are verified against U.S. Environmental Prediction Agency AIRNow PM2.5 observations measured at 716 stations over the CMAQ domain. The model bias is found to have a strong seasonal dependency, with a large positive bias in winter and a small bias in the summer months, and also to have a strong diurnal cycle.
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