Abstract
Abstract. To develop fine particulate matter (PM2.5) air quality forecasts for the US, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the Pacific Coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjusted forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematic improvements in forecast outputs across all seasons suggest that this technique can be easily adapted to perform bias adjustment for real-time PM2.5 air quality forecasts.
Highlights
Ozone (O3) and fine particulate matter (PM2.5; particles with aerodynamic diameters less than 2.5 μm) in the atmosphere have been a major concern because of their adverse effects on human and ecosystem health
O3 and PM2.5 are the two pollutants used in the US to compute the Air Quality Index (AQI), a standardized indicator of air quality conditions at a given location; the current AQI standard in the United States is primarily based on daily maximum 8-h O3 and daily mean (24-h average) PM2.5 concentrations
The National Air Quality Forecast Capability (NAQFC) system consists of 3 primary components: (1) the National Weather Service’s North American Mesoscale (NAM) model based on the Weather Research Forecast nonhydrostatic mesoscale model (WRFNMM) which provides the meteorological and atmospheric dynamic conditions for the Air Quality Forecast (AQF); (2) the US Environmental Protection Agency (EPA)’s Community Multiscale Air Quality (CMAQ) (Byun and Schere, 2006) model, which simulates the transport, chemical evolution, and deposition of atmospheric substances; and (3) an interface component (PREMAQ) that processes both the meteorological and emission inputs to conform with the CMAQ grid structure, coordinate system, and input format
Summary
Ozone (O3) and fine particulate matter (PM2.5; particles with aerodynamic diameters less than 2.5 μm) in the atmosphere have been a major concern because of their adverse effects on human and ecosystem health. Real-time O3 forecasts using air quality models have been publicly available in the US for several years over different domains (McHenry et al, 2004; McKeen et al, 2005; Otte et al, 2005; Eder, et al, 2006), while real-time PM2.5 forecasts are mainly in the developmental stage and not available to the general public. The NAQFC (Otte et al, 2005), developed by the National Oceanic and Atmospheric Administration (NOAA) and the US Environmental Protection Agency (EPA) couples NOAA’s operational North American Mesoscale (NAM) weather prediction model (Black, 1994; Rogers et al, 1996; http://www.dtcenter.org/wrf-nmm/users) with EPA’s Community Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006). The developmental mode model predictions are available for the year of 2007 over the continental US domain, providing a consistent
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