Assessment of bias-adjusted PMsub2.5/sub air quality forecasts over the continental United States during 2007

摘要

To develop fine particulate matter (PM_2.5) air quality forecasts forthe US, a National Air Quality Forecast Capability (NAQFC) system, whichlinked NOAA's North American Mesoscale (NAM) meteorological model with EPA'sCommunity Multiscale Air Quality (CMAQ) model, was deployed in thedevelopmental mode over the continental United States during 2007. Thisstudy investigates the operational use of a bias-adjustment technique calledthe Kalman Filter Predictor approach for improving the accuracy of thePM_2.5 forecasts at monitoring locations. The Kalman Filter Predictorbias-adjustment technique is a recursive algorithm designed to optimallyestimate bias-adjustment terms using the information extracted from previousmeasurements and forecasts.The bias-adjustment technique is found to improve PM_2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire yearat almost all locations. The NAQFC tends to overestimate PM_2.5 duringthe cool season and underestimate during the warm season in the eastern partof the continental US domain, but the opposite is true for the PacificCoast. In the Rocky Mountain region, the NAQFC system overestimates PM_2.5for the whole year. The bias-adjusted forecasts can quickly (after 2–3days' lag) adjust to reflect the transition from one regime to the other.The modest computational requirements and systematic improvements inforecast outputs across all seasons suggest that this technique can beeasily adapted to perform bias adjustment for real-time PM_2.5 airquality forecasts.