Evaluation of the Ventilation Index in Complex Terrain: A Dispersion Modeling Study

Michael T. Kiefer Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan

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Joseph J. Charney Northern Research Station, U.S. Forest Service, Lansing, Michigan

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Shiyuan Zhong Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan

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Warren E. Heilman Northern Research Station, U.S. Forest Service, Lansing, Michigan

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Xindi Bian Northern Research Station, U.S. Forest Service, Lansing, Michigan

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John L. Hom Northern Research Station, U.S. Forest Service, Newtown Square, Pennsylvania

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Matthew Patterson Northern Research Station, U.S. Forest Service, Newtown Square, Pennsylvania

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Abstract

In this study, the Flexible Particle (FLEXPART)-WRF, a Lagrangian particle dispersion model, is employed to simulate pollutant dispersion in and near the Lehigh Gap, a gap in a prominent ridgeline in eastern Pennsylvania. FLEXPART-WRF is used to evaluate the diagnostic value of the ventilation index (VI), an index that describes the potential for smoke or other pollutants to ventilate away from a source, for indicating dispersion potential in complex terrain. Little is known about the effectiveness of the ventilation index in diagnosing dispersion potential in complex terrain. The modeling approach used in this study is to release a dense cloud of particles across a portion of the model domain and evaluate particle behavior and VI diagnostic value in areas of the domain with differing terrain characteristics. Although both horizontal and vertical dispersion are examined, the study focuses primarily on horizontal dispersion, assessed quantitatively by calculating horizontal residence time (HRT) within a 1-km-radius circle surrounding the particle release point. Analysis of HRT across the domain reveals horizontal dispersion patterns that are influenced by the ridgeline and the Lehigh Gap. Comparison of VI and HRT in different areas of the domain reveals a robust relationship windward of the ridgeline and a weak relationship leeward of the ridgeline and in the vicinity of the Lehigh Gap. The results of this study suggest that VI users should consider whether they are windward or leeward of topographic features, and highlight the need for an alternative metric that better takes into account the influence of the terrain on dispersion.

Northern Research Station, U.S. Forest Service, Morgantown, West Virginia.

© 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: Michael T. Kiefer, mtkiefer@msu.edu

Abstract

In this study, the Flexible Particle (FLEXPART)-WRF, a Lagrangian particle dispersion model, is employed to simulate pollutant dispersion in and near the Lehigh Gap, a gap in a prominent ridgeline in eastern Pennsylvania. FLEXPART-WRF is used to evaluate the diagnostic value of the ventilation index (VI), an index that describes the potential for smoke or other pollutants to ventilate away from a source, for indicating dispersion potential in complex terrain. Little is known about the effectiveness of the ventilation index in diagnosing dispersion potential in complex terrain. The modeling approach used in this study is to release a dense cloud of particles across a portion of the model domain and evaluate particle behavior and VI diagnostic value in areas of the domain with differing terrain characteristics. Although both horizontal and vertical dispersion are examined, the study focuses primarily on horizontal dispersion, assessed quantitatively by calculating horizontal residence time (HRT) within a 1-km-radius circle surrounding the particle release point. Analysis of HRT across the domain reveals horizontal dispersion patterns that are influenced by the ridgeline and the Lehigh Gap. Comparison of VI and HRT in different areas of the domain reveals a robust relationship windward of the ridgeline and a weak relationship leeward of the ridgeline and in the vicinity of the Lehigh Gap. The results of this study suggest that VI users should consider whether they are windward or leeward of topographic features, and highlight the need for an alternative metric that better takes into account the influence of the terrain on dispersion.

Northern Research Station, U.S. Forest Service, Morgantown, West Virginia.

© 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: Michael T. Kiefer, mtkiefer@msu.edu
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  • Achtemeier, G. L., L. P. Naeher, J. Blake, and S. Rathbun, 2010: On the relevance of the ventilation index as a tool for regulating prescribed fire. Proc. 24th Tall Timbers Fire Ecology Conference: The Future of Prescribed Fire: Public Awareness, Health, and Safety, Tallahassee, FL, Tall Timbers Research Station, 79.

  • Brioude, J., and Coauthors, 2013: The Lagrangian particle dispersion model FLEXPART-WRF version 3.1. Geosci. Model Dev., 6, 18891904, https://doi.org/10.5194/gmd-6-1889-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., 1990: Parameterization for the absorption of solar radiation by O2 and CO2 with application to climate studies. J. Climate, 3, 209217, https://doi.org/10.1175/1520-0442(1990)003<0209:PFTAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., 1992: A solar radiation model for climate studies. J. Atmos. Sci., 49, 762772, https://doi.org/10.1175/1520-0469(1992)049<0762:ASRMFU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, 85 pp., https://archive.org/details/nasa_techdoc_19950009331.

  • Colette, A. G., F. K. Chow, and R. L. Street, 2003: A numerical study of inversion-layer breakup and the effects of topographic shading in idealized valleys. J. Appl. Meteor., 42, 12551272, https://doi.org/10.1175/1520-0450(2003)042<1255:ANSOIB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Wekker, S. F. J., and M. Kossman, 2015: Convective boundary layer heights over mountainous terrain—A review of concepts. Front. Earth Sci., 3, 77, https://doi.org/10.3389/feart.2015.00077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dupont, S., and Y. Brunet, 2008: Influence of foliar density profile on canopy flow: A large-eddy simulation study. Agric. For. Meteor., 148, 976990, https://doi.org/10.1016/j.agrformet.2008.01.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferguson, S. A., 2001: Smoke dispersion prediction systems. Smoke Management Guide for Prescribed and Wildland Fire, C. C. Hardy et al., Eds., National Wildfire Coordination Group, 163–178.

  • Goodrick, S. L., G. L. Achtemeier, N. K. Larkin, Y. Liu, and T. M. Strand, 2013: Modelling smoke transport from wildland fires: A review. Int. J. Wildland Fire, 22, 8394, https://doi.org/10.1071/WF11116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gorski, C. J., and A. Farnsworth, 2000: Fire weather and smoke management. Mountain Meteorology: Fundamentals and Applications, Oxford University Press, 237–272.

  • Hanna, S. R., 1982: Applications in air pollution modeling. Atmospheric Turbulence and Air Pollution Modeling, F. T. M. Nieuwstadt and H. D. van Dop, Eds., Reidel, 275–310.

    • Crossref
    • Export Citation
  • Kiefer, M. T., and S. Zhong, 2011: An idealized modeling study of nocturnal cooling processes inside a small enclosed basin. J. Geophys. Res., 116, D20127, https://doi.org/10.1029/2011JD016119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levin, E. J. T., and Coauthors, 2010: Biomass burning smoke aerosol properties measured during Fire Laboratory at Missoula Experiments (FLAME). J. Geophys. Res., 115, D18210, https://doi.org/10.1029/2009JD013601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michioka, T., and F. K. Chow, 2008: High-resolution large-eddy simulations of scalar transport in atmospheric boundary layer flow over complex terrain. J. Appl. Meteor. Climatol., 47, 31503169, https://doi.org/10.1175/2008JAMC1941.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Weather Service, 2018: National blend of models weather element definitions. NOAA Meteorological Development Laboratory, https://www.weather.gov/mdl/nbm_elem_def.

  • National Wildfire Coordinating Group, 2017: Fire behavior field reference guide. PMS 437, 197 pp., https://www.nwcg.gov/sites/default/files/publications/pms437.pdf.

  • Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536549, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2004: Structures and dynamics of quasi-2D mesoscale convective systems. J. Atmos. Sci., 61, 545567, https://doi.org/10.1175/1520-0469(2004)061<0545:SADOQM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., and A. Xiu, 1995: Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. J. Appl. Meteor., 34, 1632, https://doi.org/10.1175/1520-0450-34.1.16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting (WRF) Model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4, http://ams.confex.com/ams/pdfpapers/154114.pdf.

  • Rotach, M. W., and D. Zardi, 2007: On the boundary-layer structure over highly complex terrain: Key findings from MAP. Quart. J. Roy. Meteor. Soc., 133, 937948, https://doi.org/10.1002/qj.71.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C. M., and E. D. Skyllingstad, 2005: Numerical simulation of katabatic flow with changing slope angle. Mon. Wea. Rev., 133, 30653080, https://doi.org/10.1175/MWR2982.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 10351057, https://doi.org/10.1175/MWR-D-13-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stohl, A., C. Forster, A. Frank, P. Seibert, and G. Wotawa, 2005: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys., 5, 24612474, https://doi.org/10.5194/acp-5-2461-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.

    • Crossref
    • Export Citation
  • Sun, W.-Y., and C.-Z. Chang, 1986: Diffusion model for a convective layer. Part I: Numerical simulation of convective boundary layer. J. Climate Appl. Meteor., 25, 14451453, https://doi.org/10.1175/1520-0450(1986)025<1445:DMFACL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whiteman, C. D., 2000: Diurnal mountain winds. Mountain Meteorology: Fundamentals and Applications, Oxford University Press, 171–202.

    • Crossref
    • Export Citation
  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmosphere simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161193, https://doi.org/10.1007/s007030070003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmosphere simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 143165, https://doi.org/10.1007/s007030170027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., D. Wang, J. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139170, https://doi.org/10.1007/s00703-001-0595-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
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