Abstract
Abstract. A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a negative correlation with the MODIS CFs, showing around 1 ppb decrease for 10 % MODIS CF change over the contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 % CF change, implying that spatial and temporal misplacement of the modeled cloud field might have biased modeled surface ozone level. Current NAQFC cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of surface-ozone bias in the current NAQFC system.
Highlights
Ground-level ozone is a secondary pollutant resulting from photochemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) in the presence of solar radiation
While local ozone production is affected by numerous factors, including precursor emissions and meteorological conditions such as temperature and local circulation, ozone photochemistry is photon-limited, and net ozone production shows a direct relationship with changes in UV actinic flux resulting from clouds and aerosols (Dickerson et al, 1997; He and Carmichael, 1999; Jacobson, 1998; Monks et al, 2004)
Pour-Biazar et al (2007) argued that the uncertainties in estimation of cloud transmissivity and errors in the placement of clouds’ location and time could be an important source of uncertainties in simulations of surface ozone, demonstrating during the Texas Air Quality Study campaign that surface-ozone modeling can be improved by adjusting photolysis rates based on the Geostationary Operational Environmental Satellite cloud product
Summary
Ground-level ozone is a secondary pollutant resulting from photochemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) in the presence of solar radiation. Clouds play an important role in regional air quality, impacting both surface ozone and particulate matter by regulating photochemical reaction rates, heterogeneous chemistry, and the evolution and partitioning of particulate matter. Pour-Biazar et al (2007) argued that the uncertainties in estimation of cloud transmissivity and errors in the placement of clouds’ location and time could be an important source of uncertainties in simulations of surface ozone, demonstrating during the Texas Air Quality Study campaign that surface-ozone modeling can be improved by adjusting photolysis rates based on the Geostationary Operational Environmental Satellite cloud product. General performance of the contiguous United States (CONUS)scale air-quality forecast system and possible overestimation of surface-ozone levels due to uncertainty in cloud fractions will be discussed
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