Improving regional ozone modeling through systematic evaluation of errors using the aircraft observations during the International Consortium for Atmospheric Research on Transport and Transformation

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Read Full Article (file size: 3194612 bytes) Cited by JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D12S19, doi:10.1029/2006JD007762, 2007 Improving regional ozone modeling through systematic evaluation of errors using the aircraft observations during the International Consortium for Atmospheric Research on Transport and Transformation Marcelo Mena-Carrasco Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Youhua Tang Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Gregory R. Carmichael Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Tianfeng Chai Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Narisara Thongbongchoo Department of Chemical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand J. Elliott Campbell Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Sarika Kulkarni Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA Larry Horowitz Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, New Jersey, USA Jeffrey Vukovich Institute for the Environment, University of North Carolina, Chapel Hill, North Carolina, USA Melody Avery NASA Langley Research Center, Hampton, Virginia, USA William Brune Department of Earth Sciences, Pennsylvania State University, University Park, Pennsylvania, USA Jack E. Dibb Institute for the Study of Earth, Ocean, and Space, University of New Hampshire, Durham, New Hampshire, USA Louisa Emmons Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, Colorado, USA Frank Flocke Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, Colorado, USA Glen W. Sachse NASA Langley Research Center, Hampton, Virginia, USA David Tan School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA Rick Shetter Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, Colorado, USA Robert W. Talbot Institute for the Study of Earth, Ocean, and Space, University of New Hampshire, Durham, New Hampshire, USA David G. Streets Argonne National Laboratory, Argonne, Illinois, USA Gregory Frost Chemical Sciences Division, Earth System Research Laboratory, NOAA, Boulder, Colorado, USA Donald Blake Department of Chemistry, University of California, Irvine, California, USA Abstract During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to −0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NO_z, NO_y, and HNO₃ biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NO_x emissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NO_x emissions, followed by VOCs and CO. PAN as a reservoir of NO_x can contribute to a significant amount of surface ozone through thermal decomposition. Received 6 July 2006; accepted 5 April 2007; published 9 June 2007. Keywords: ozone; North America; bias. Index Terms: 0345 Atmospheric Composition and Structure: Pollution: urban and regional (0305, 0478, 4251); 0365 Atmospheric Composition and Structure: Troposphere: composition and chemistry; 3337 Atmospheric Processes: Global climate models (1626, 4928). Read Full Article (file size: 3194612 bytes) Cited by Citation: Mena-Carrasco, M., et al. (2007), Improving regional ozone modeling through systematic evaluation of errors using the aircraft observations during the International Consortium for Atmospheric Research on Transport and Transformation, J. Geophys. Res., 112, D12S19, doi:10.1029/2006JD007762. Copyright 2007 by the American Geophysical Union.

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