Improving National Air Quality Forecasts with Satellite Aerosol Observations

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Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.

NASA Langley Research Center, Hampton, Virginia

Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina

SAIC, Hampton, Virginia

Joint Center for Earth Science Technology, University of Maryland, Baltimore County, Baltimore, Maryland

NASA Goddard Space Flight Center, Greenbelt, Maryland

SSEC/CIMSS, University of Wisconsin—Madison, Madison, Wisconsin

NOAA/NESDIS/ORA, University of Wisconsin—Madison, Madison, Wisconsin

Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina

Sonoma Technology, Inc., Petaluma, California

*CURRENT ASSIGNMENT: NASA Langley Research Center, Hampton, Virginia

+CURRENT AFFILIATION: SSEC/CIMSS, University of Wisconsin—Madison, Madison, Wisconsin

CORRESPONDING AUTHOR: Jassim Al-Saadi, MS 401B, NASA Langley Research Center, Hampton, VA 23681, E-mail: j.a.al-saadi@nasa.gov

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.

NASA Langley Research Center, Hampton, Virginia

Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina

SAIC, Hampton, Virginia

Joint Center for Earth Science Technology, University of Maryland, Baltimore County, Baltimore, Maryland

NASA Goddard Space Flight Center, Greenbelt, Maryland

SSEC/CIMSS, University of Wisconsin—Madison, Madison, Wisconsin

NOAA/NESDIS/ORA, University of Wisconsin—Madison, Madison, Wisconsin

Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina

Sonoma Technology, Inc., Petaluma, California

*CURRENT ASSIGNMENT: NASA Langley Research Center, Hampton, Virginia

+CURRENT AFFILIATION: SSEC/CIMSS, University of Wisconsin—Madison, Madison, Wisconsin

CORRESPONDING AUTHOR: Jassim Al-Saadi, MS 401B, NASA Langley Research Center, Hampton, VA 23681, E-mail: j.a.al-saadi@nasa.gov
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