Articles | Volume 8, issue 9
Geosci. Model Dev., 8, 2959–2965, 2015
Geosci. Model Dev., 8, 2959–2965, 2015

Development and technical paper 25 Sep 2015

Development and technical paper | 25 Sep 2015

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

H. C. Kim1,2, P. Lee1, F. Ngan1,2, Y. Tang1,2, H. L. Yoo1,2, and L. Pan1,2 H. C. Kim et al.
  • 1NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
  • 2Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA

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.

Short summary
This study focuses on the evaluation of regional air quality model's performance based on the cloud information from satellites. While cloud information is crucial in photochemistry model, the definitions of cloud fraction from model and satellite are not physically consistent. We demonstrate that improper modeling of cloud fraction is correlated with surface ozone bias, and we also show that current model cloud field might be too bright, causing an overestimation of surface ozone level.