Characterization of Regional Wind Patterns Using Self-Organizing Maps: Impact on Dallas–Fort Worth Long-Term Ozone Trends

Alexander Kotsakis Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas

Search for other papers by Alexander Kotsakis in
Current site
Google Scholar
PubMed
Close
,
Yunsoo Choi Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas

Search for other papers by Yunsoo Choi in
Current site
Google Scholar
PubMed
Close
,
Amir H. Souri Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas

Search for other papers by Amir H. Souri in
Current site
Google Scholar
PubMed
Close
,
Wonbae Jeon Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas, and Institute of Environmental Studies, Pusan National University, Pusan, South Korea

Search for other papers by Wonbae Jeon in
Current site
Google Scholar
PubMed
Close
, and
James Flynn Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas

Search for other papers by James Flynn in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study analyzes wind patterns in the Dallas–Fort Worth (DFW) area to gain a clearer understanding of meteorological patterns that have historically led to ozone exceedances in this region. Using a clustering algorithm called “self-organizing maps,” we analyzed five notable characteristic regional wind patterns that occurred between April and October in 2000–14. A regional-scale high pressure system, cluster 2, produced weak southeast winds over DFW and accounted for 35.2% of ozone exceedances. Clusters 1 and 5, characterized by southwesterly winds over the DFW area, were together associated with one-third of total ozone exceedances and show quantifiable impacts of the Barnett Shale region on downwind ozone production. Cluster 3, associated with Bermuda-high conditions, had relatively lower ozone in DFW (45.3 ppbv) resulting from transport of lower background ozone from the Gulf of Mexico. For clusters that produce southeasterly or southwesterly winds over Houston, ozone values in DFW were always larger than those in Houston. Further, to determine the potential impact of Houston pollution on DFW ozone, a sensitivity simulation with no Houston emissions and a base simulation were performed. The difference between the simulations revealed ozone enhancements of 1–2 ppbv and coincident enhancements in NOy under south-southeasterly wind conditions. From these results, we conclude that downwind pollution from Houston and the Barnett Shale area exacerbates DFW ozone concentrations, underscoring the impacts of specific wind patterns on air quality in DFW.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yunsoo Choi, ychoi6@uh.edu

Abstract

This study analyzes wind patterns in the Dallas–Fort Worth (DFW) area to gain a clearer understanding of meteorological patterns that have historically led to ozone exceedances in this region. Using a clustering algorithm called “self-organizing maps,” we analyzed five notable characteristic regional wind patterns that occurred between April and October in 2000–14. A regional-scale high pressure system, cluster 2, produced weak southeast winds over DFW and accounted for 35.2% of ozone exceedances. Clusters 1 and 5, characterized by southwesterly winds over the DFW area, were together associated with one-third of total ozone exceedances and show quantifiable impacts of the Barnett Shale region on downwind ozone production. Cluster 3, associated with Bermuda-high conditions, had relatively lower ozone in DFW (45.3 ppbv) resulting from transport of lower background ozone from the Gulf of Mexico. For clusters that produce southeasterly or southwesterly winds over Houston, ozone values in DFW were always larger than those in Houston. Further, to determine the potential impact of Houston pollution on DFW ozone, a sensitivity simulation with no Houston emissions and a base simulation were performed. The difference between the simulations revealed ozone enhancements of 1–2 ppbv and coincident enhancements in NOy under south-southeasterly wind conditions. From these results, we conclude that downwind pollution from Houston and the Barnett Shale area exacerbates DFW ozone concentrations, underscoring the impacts of specific wind patterns on air quality in DFW.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yunsoo Choi, ychoi6@uh.edu
Save
  • Ahmadi, M., and K. John, 2015: Statistical evaluation of the impact of shale gas activities on ozone pollution in north Texas. Sci. Total Environ., 536, 457467, https://doi.org/10.1016/j.scitotenv.2015.06.114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., and Coauthors, 2005: A bad air day in Houston. Bull. Amer. Meteor. Soc., 86, 657669, https://doi.org/10.1175/BAMS-86-5-657.

  • Blanchard, C. L., S. Tanenbaum, and D. R. Lawson, 2008: Differences between weekday and weekend air pollutant levels in Atlanta; Baltimore; Chicago; Dallas–Fort Worth; Denver; Houston; New York; Phoenix; Washington, DC; and surrounding areas. J. Air Waste Manag. Assoc., 58, 15981615, https://doi.org/10.3155/1047-3289.58.12.1598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Byun, D., and K. L. Schere, 2006: Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev., 59, 5177, https://doi.org/10.1115/1.2128636.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Byun, D. W., S. T. Kim, and S. B. Kim, 2007: Evaluation of air quality models for the simulation of a high ozone episode in the Houston metropolitan area. Atmos. Environ., 41, 837853, https://doi.org/10.1016/j.atmosenv.2006.08.038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, Y., and A. H. Souri, 2015: Chemical condition and surface ozone in large cities of Texas during the last decade: Observational evidence from OMI, CAMS, and model analysis. Remote Sens. Environ., 168, 90101, https://doi.org/10.1016/j.rse.2015.06.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooper, O. R., R. S. Gao, D. Tarasick, T. Leblanc, and C. Sweeney, 2012: Long-term ozone trends at rural ozone monitoring sites across the United States, 1990–2010. J. Geophys. Res., 117, D22307, https://doi.org/10.1029/2012JD018261.

    • Search Google Scholar
    • Export Citation
  • Czader, B. H., Y. Choi, X. Li, S. Alvarez, and B. Lefer, 2015: Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas. Atmos. Chem. Phys., 15, 12531263, https://doi.org/10.5194/acp-15-1253-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Darby, L. S., 2005: Cluster analysis of surface winds in Houston, Texas, and the impact of wind patterns on ozone. J. Appl. Meteor., 44, 17881806, https://doi.org/10.1175/JAM2320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, J. M., B. K. Eder, D. Nychka, and Q. Yang, 1998: Modeling the effects of meteorology on ozone in Houston using cluster analysis and generalized additive models. Atmos. Environ., 32, 25052520, https://doi.org/10.1016/S1352-2310(98)00008-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diao, L., A. Roy, B. Czader, S. Pan, W. Jeon, A. H. Souri, and Y. Choi, 2016a: Modeling the effect of relative humidity on nitrous acid formation in the Houston area. Atmos. Environ., 131, 7882, https://doi.org/10.1016/j.atmosenv.2016.01.053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glisan, J. M., W. J. Gutowski Jr., J. J. Cassano, E. N. Cassano, and M. W. Seefeldt, 2016: Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps. J. Geophys. Res. Atmos., 121, 77467761, https://doi.org/10.1002/2016JD024822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houyoux, M. R., J. M. Vukovich, C. J. Coats, N. J. M. Wheeler, and P. S. Kasibhatla, 2000: Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. J. Geophys. Res., 105, 90799090, https://doi.org/10.1029/1999JD900975.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katurji, M., B. Noonan, P. Zawar-Reza, T. Schulmann, and A. Sturman, 2015: Characteristics of the springtime alpine valley atmospheric boundary layer using self-organizing maps. J. Appl. Meteor. Climatol., 54, 20772085, https://doi.org/10.1175/JAMC-D-14-0317.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., D. Parrish, T. Ryerson, U. Nopmongcol, J. Johnson, E. Tai, and G. Yarwood, 2009: Contributions of regional transport and local sources to ozone exceedances in Houston and Dallas: Comparison of results from a photochemical grid model to aircraft and surface measurements. J. Geophys. Res., 114, D00F02, https://doi.org/10.1029/2008JD010248.

    • Search Google Scholar
    • Export Citation
  • Kim, S., D. W. Byun, and D. Cohan, 2009: Contributions of inter- and intra-state emissions to ozone over Dallas–Fort Worth, Texas. Civ. Eng. Environ. Syst., 26, 103116, https://doi.org/10.1080/10286600802005364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S.-W., and Coauthors, 2011: Evaluations of NOx and highly reactive VOC emission inventories in Texas and their implications for ozone plume simulations during the Texas Air Quality Study 2006. Atmos. Chem. Phys., 11, 11 36111 386, https://doi.org/10.5194/acp-11-11361-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kohonen, T., 2001: Self-Organizing Maps. 3rd ed. Springer, 501 pp.

    • Crossref
    • Export Citation
  • Lefer, B., B. Rappengluck, J. Flynn, and C. Haman, 2010: Photochemical and meteorological relationships during the Texas-II Radical and Aerosol Measurement Project (TRAMP). Atmos. Environ., 44, 40054013, https://doi.org/10.1016/j.atmosenv.2010.03.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lefohn, A. S., and J. K. Foley, 1993: Establishing relevant ozone standards to protect vegetation and human health: Exposure dose/response considerations. J. Air Waste Manage. Assoc., 43, 106112, https://doi.org/10.1080/1073161X.1993.10467111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., Y. Choi, B. Czader, A. Roy, H. Kim, B. Lefer, and S. Pan, 2016: The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign. Atmos. Chem. Phys., 16, 31273144, https://doi.org/10.5194/acp-16-3127-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, M., L. W. Horowitz, R. Payton, A. M. Fiore, and G. Tonnesen, 2017: US surface ozone trends and extremes from 1980 to 2014: Quantifying the roles of rising Asian emissions, domestic controls, wildfires, and climate. Atmos. Chem. Phys., 17, 29432970, https://doi.org/10.5194/acp-17-2943-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNider, R. T., K. Doty, W. B. Norris, and A. Biazar, 2005: Conceptual model for extreme ozone concentration events in Dallas and east Texas based on reduced dilution in frontal zones. Texas Commission on Environmental Quality Rep. Project H12.8HRA, 12, 53 pp.

  • Ngan, F., and D. Byun, 2011: Classification of weather patterns and associated trajectories of high-ozone episodes in the Houston–Galveston–Brazoria area during the 2005/06 TexAQS-II. J. Appl. Meteor. Climatol., 50, 485499, https://doi.org/10.1175/2010JAMC2483.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, S., Y. Choi, A. Roy, X. Li, W. Jeon, and A. H. Souri, 2015: Modeling the uncertainty of several VOC and its impact on simulated VOC and ozone in Houston, Texas. Atmos. Environ., 120, 404416, https://doi.org/10.1016/j.atmosenv.2015.09.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pearce, J. L., J. Beringer, N. Nicholls, R. J. Hyndman, P. Uotila, and N. J. Tapper, 2011: Investigating the influence of synoptic-scale meteorology on air quality using self-organizing maps and generalized additive modeling. Atmos. Environ., 45, 128136, https://doi.org/10.1016/j.atmosenv.2010.09.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pierce, R. B., and Coauthors, 2009: Impacts of background ozone production on Houston and Dallas, Texas, air quality during the Second Texas Air Quality Study field mission. J. Geophys. Res., 114, D00F09, https://doi.org/10.1029/2008JD011337.

    • Search Google Scholar
    • Export Citation
  • Rutter, A. P., R. J. Griffin, B. K. Cevik, K. M. Shakya, L. Gong, S. Kim, J. H. Flynn, and B. L. Lefer, 2015: Sources of air pollution in a region of oil and gas exploration downwind of a large city. Atmos. Environ., 120, 8999, https://doi.org/10.1016/j.atmosenv.2015.08.073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senff, C. J., R. J. Alvarez, R. M. Hardesty, R. M. Banta, and A. O. Langford, 2010: Airborne lidar measurements of ozone flux downwind of Houston and Dallas. J. Geophys. Res., 115, D20307, https://doi.org/10.1029/2009JD013689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, L., L. J. Mickley, and A. P. K. Tai, 2015: Influence of synoptic patterns on surface ozone variability over the eastern United States from 1980 to 2012. Atmos. Chem. Phys., 15, 10 92510 938, https://doi.org/10.5194/acp-15-10925-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simon, H., A. Reff, B. Wells, J. Xing, and N. Frank, 2015: Ozone trends across the United States over a period of decreasing NOx and VOC emissions. Environ. Sci. Technol., 49, 186195, https://doi.org/10.1021/es504514z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Souri, A. H., Y. Choi, X. Li, A. Kotsakis, and X. Jiang, 2016a: A 15-year climatology of wind pattern impacts on surface ozone in Houston, Texas. Atmos. Res., 174–175, 124134, https://doi.org/10.1016/j.atmosres.2016.02.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Souri, A. H., Y. Choi, W. B. Jeon, X. S. Li, S. Pan, L. J. Diao, and D. A. Westenbarger, 2016b: Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign. Atmos. Environ., 131, 371381, https://doi.org/10.1016/j.atmosenv.2016.02.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stauffer, R. M., A. M. Thompson, and G. S. Young, 2016: Tropospheric ozonesonde profiles at long-term U.S. monitoring sites: 1. A climatology based on self-organizing maps. J. Geophys. Res. Atmos., 121, 13201339, https://doi.org/10.1002/2015JD023641.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan, 2015: NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., 96, 20592077, https://doi.org/10.1175/BAMS-D-14-00110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suciu, L. G., R. J. Griffin, and C. A. Masiello, 2017: Regional background O3 and NOx in the Houston–Galveston–Brazoria (TX) region: A decadal-scale perspective. Atmos. Chem. Phys., 17, 65656581, https://doi.org/10.5194/acp-17-6565-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Travis, K. R., and Coauthors, 2016: Why do models overestimate surface ozone in the southeast United States? Atmos. Chem. Phys., 16, 13 56113 577, https://doi.org/10.5194/acp-16-13561-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vesanto, J., J. Himberg, E. Alhoniemi, and J. Parhankangas, 2000: SOM toolbox for Matlab 5. Helsinki University of Technology, http://www.cis.hut.fi/projects/somtoolbox/.

  • Wang, Y. X., B. X. Jia, S. C. Wang, M. Estes, L. Shen, and Y. Y. Xie, 2016: Influence of the Bermuda high on interannual variability of summertime ozone in the Houston–Galveston–Brazoria region. Atmos. Chem. Phys., 16, 15 26515 276, https://doi.org/10.5194/acp-16-15265-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiedinmyer, C., S. K. Akagi, R. J. Yokelson, L. K. Emmons, J. A. Al-Saadi, J. J. Orlando, and A. J. Soja, 2011: The Fire Inventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev., 4, 625641, https://doi.org/10.5194/gmd-4-625-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., and X. Z. Liang, 2013: Impacts of the Bermuda high on regional climate and ozone over the United States. J. Climate, 26, 10181032, https://doi.org/10.1175/JCLI-D-12-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 667 217 16
PDF Downloads 515 112 16