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- Author or Editor: Clinton MacDonald x
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Comparison of Satellite, Model, and In Situ Values of Photosynthetically Available Radiation (PAR)Abstract
Photosynthetically available radiation (PAR) incident at the sea surface penetrates into the water column and drives oceanic primary production. Ecosystem models to estimate phytoplankton biomass and primary production require an estimate of sea surface PAR, which is available from satellite ocean color imagery and atmospheric model predictions. Because the PAR values could come from either source, it is important to understand the variability and accuracies of each. We performed spatial and temporal analyses covering multiple years and seasons, and clear/cloudy conditions. We compare values derived from the imagery to those from the models and to in situ measurements in the Gulf of Mexico to validate the imagery and models and to assess PAR variability based on source. Averaged over space or time, the relative errors in PAR between the six sources (two satellite, three model, and in situ) are generally less than 5%–7%, but they can vary up to 11%. However, the errors and biases on a daily or pixel-by-pixel basis are larger, and the averages can mask seasonal trends.
Abstract
Photosynthetically available radiation (PAR) incident at the sea surface penetrates into the water column and drives oceanic primary production. Ecosystem models to estimate phytoplankton biomass and primary production require an estimate of sea surface PAR, which is available from satellite ocean color imagery and atmospheric model predictions. Because the PAR values could come from either source, it is important to understand the variability and accuracies of each. We performed spatial and temporal analyses covering multiple years and seasons, and clear/cloudy conditions. We compare values derived from the imagery to those from the models and to in situ measurements in the Gulf of Mexico to validate the imagery and models and to assess PAR variability based on source. Averaged over space or time, the relative errors in PAR between the six sources (two satellite, three model, and in situ) are generally less than 5%–7%, but they can vary up to 11%. However, the errors and biases on a daily or pixel-by-pixel basis are larger, and the averages can mask seasonal trends.
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.
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.
