• Chou, M.-D., 1990: Parameterization for the absorption of solar radiation by O2 and CO2 with application to climate studies. J. Climate, 3, 209217.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., 1992: A solar radiation model for climate studies. J. Atmos. Sci., 49, 762772.

  • 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. [Available from NASA Center for Aerospace Information, 800 Elkridge Landing Rd., Linthicum Heights, MD 21090-2934.]

  • Colette, A. G., , F. Katopodes 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Dupont, S., , and Y. Brunet, 2009: Coherent structures in canopy edge flow: A large-eddy simulation study. J. Fluid Mech., 630, 93128.

  • Fast, J. D., , and R. C. Easter, 2006: A Lagrangian particle dispersion model compatible with WRF. Extended Abstracts, Seventh Annual WRF User’s Workshop, Boulder, CO, NCAR, P6.2. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2006/WorkshopPapers.htm.]

  • Heilman, W. E., and Coauthors, 2013: Development of modeling tools for predicting smoke dispersion from low-intensity fires. Joint Fire Science Plan Study Final Rep. 09-1-04-1, 64 pp. [Available online at https://www.firescience.gov/projects/09-1-04-1/project/09-1-04-1_final_report.pdf.]

  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., , and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, 165–170.

  • Kanda, M., , and M. Hino, 1994: Organized structures in developing turbulent flow within and above a plant canopy, using a large eddy simulation. Bound.-Layer Meteor., 68, 237257.

    • Search Google Scholar
    • Export Citation
  • Keeley, J. E., 2009: Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland Fire, 18, 116126.

    • Search Google Scholar
    • Export Citation
  • Kiefer, M. T., , S. Zhong, , W. E. Heilman, , J. J. Charney, , and X. Bian, 2013: Evaluation of an ARPS-based canopy flow modeling system for use in future operational smoke prediction efforts. J. Geophys. Res., 118, 61756188, doi:10.1002/jgrd.50491.

    • Search Google Scholar
    • Export Citation
  • Larkin, N. K., and Coauthors, 2009: The BlueSky smoke modeling framework. Int. J. Wildland Fire, 18, 906920.

  • Lathrop, R. G., , and M. B. Kaplan, 2004: New Jersey land use/land cover update to year 2000-2001. New Jersey Department of Environmental Protection, 35 pp. [Available online at http://www.nj.gov/dep/dsr/landuse/landuse00-01.pdf.]

  • Lavdas, L., 1996: Program VSMOKE—User’s manual. USDA Forest Service General Tech. Rep. SRS-6, 156 pp. [Available online at http://www.srs.fs.usda.gov/pubs/1561.]

  • Lin, Y.-L., , R. D. Farley, , and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor., 22, 10651092.

    • Search Google Scholar
    • Export Citation
  • Melvin, M. A., 2012: 2012 national prescribed fire use survey report. Coalition of Prescribed Fire Councils Inc. Tech. Rep. 01-12, 24 pp. [Available online at http://www.stateforesters.org/sites/default/files/publication-documents/2012_National_Prescribed_Fire_Survey.pdf.]

  • Mesinger, F., , Z. I. Janjić, , S. Nicković, , D. Gavrilov, , and D. G. Deaven, 1988: The step-mountain coordinate: Model description and performance for cases of Alpine lee cyclogenesis and for a case of an Appalachian redevelopment. Mon. Wea. Rev., 116, 14931518.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Moeng, C.-H., , and J. C. Wyngaard, 1989: Evaluation of turbulent transport and dissipation closures in second-order modeling. J. Atmos. Sci., 46, 23112330.

    • Search Google Scholar
    • Export Citation
  • National Interagency Fire Center, cited2012: Prescribed fire and acres by agency. [Available online at http://www.nifc.gov/fireInfo/fireInfo_stats_prescribed.html.]

  • Noilhan, J., , and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536549.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Patton, E. G., and Coauthors, 2011: The Canopy Horizontal Array Turbulence Study (CHATS). Bull. Amer. Meteor. Soc., 92, 593611.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Raupach, M., , and A. Thom, 1981: Turbulence in and above plant canopies. Annu. Rev. Fluid Mech., 13, 97129.

  • Riebau, A. R., , D. G. Fox, , M. L. Sestak, , B. Dailey, , and S. F. Archer, 1988: Simple Approach Smoke Estimation Model. Atmos. Environ., 22, 783788.

    • Search Google Scholar
    • Export Citation
  • Seinfeld, J. H., 1975: Air Pollution: Physical and Chemical Fundamentals.McGraw-Hill, 523 pp.

  • Skowronski, N. S., , K. L. Clark, , M. Duveneck, , and J. Hom, 2011: Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sens. Environ., 115, 703714, doi:10.1016/j.rse.2010.10.012.

    • Search Google Scholar
    • Export Citation
  • Stohl, A., , C. Forster, , A. Frank, , P. Seibert, , and G. Wotawa, 2005: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys., 5, 24612474.

    • Search Google Scholar
    • Export Citation
  • Sun, H., , T. L. Clark, , R. B. Stull, , and T. A. Black, 2006a: Two-dimensional simulation of airflow and carbon dioxide transport over a forested mountain. Part I: Interactions between thermally-forced circulations. Agric. For. Meteor., 140, 338351.

    • Search Google Scholar
    • Export Citation
  • Sun, R., , M. A. Jenkins, , S. K. Krueger, , W. Mell, , and J. J. Charney, 2006b: An evaluation of fire-plume properties simulated with the Fire Dynamics Simulator (FDS) and the Clark coupled wildfire model. Can. J. For. Res., 36, 28942908.

    • Search Google Scholar
    • 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.

    • Search Google Scholar
    • Export Citation
  • Sun, X.-M., , Z.-L. Zhu, , X.-F. Wen, , G.-F. Yuan, , and G.-R. Yu, 2006c: The impact of averaging period on eddy fluxes observed at ChinaFLUX sites. Agric. For. Meteor., 137, 188193.

    • Search Google Scholar
    • 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, 463485.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
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Multiscale Simulation of a Prescribed Fire Event in the New Jersey Pine Barrens Using ARPS-CANOPY

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  • 1 * Department of Geography, Michigan State University, East Lansing, Michigan
  • | 2 Northern Research Station, USDA Forest Service, East Lansing, Michigan
  • | 3 Northern Research Station, USDA Forest Service, Morgantown, West Virginia
  • | 4 Northern Research Station, USDA Forest Service, Newtown Square, Pennsylvania
  • | 5 Northern Research Station, USDA Forest Service, New Lisbon, New Jersey
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Abstract

Smoke prediction products are one of the tools used by land management personnel for decision making regarding prescribed fires. This study documents the application to a prescribed fire of a smoke prediction system that employs ARPS-CANOPY, a modified version of the Advanced Regional Prediction System (ARPS) model containing a canopy submodel, as the meteorological driver. In this paper, the performance of ARPS-CANOPY in simulating meteorological fields in the vicinity of a low-intensity fire is assessed using flux-tower data collected prior to and during a low-intensity prescribed fire in the New Jersey Pine Barrens in March 2011. A three-dimensional high-resolution plant area density dataset is utilized to define the characteristics of the canopy, and the fire is represented in ARPS-CANOPY as a heat flux to the atmosphere. The standard ARPS model is compared with reanalysis and upper-air data to establish that the model can simulate the observed synoptic-mesoscale and planetary boundary layer features that are salient to this study. ARPS-CANOPY profiles of mean turbulent kinetic energy, wind speed/direction, and temperature exhibit patterns that appear in the flux-tower observations during both the preburn phase of the experiment and the period of time the flux tower experienced perturbed atmospheric conditions due to the impinging fire. Last, the character and source of turbulence in and around the fire line are examined. These results are encouraging for smoke prediction efforts since transport of smoke from low-intensity fires is highly sensitive to the near-surface meteorological conditions and, in particular, turbulent flows.

Corresponding author address: Michael Kiefer, Michigan State University, Geography Bldg. Rm. 203, 673 Auditorium Rd., East Lansing, MI 48824. E-mail: mtkiefer@msu.edu

Abstract

Smoke prediction products are one of the tools used by land management personnel for decision making regarding prescribed fires. This study documents the application to a prescribed fire of a smoke prediction system that employs ARPS-CANOPY, a modified version of the Advanced Regional Prediction System (ARPS) model containing a canopy submodel, as the meteorological driver. In this paper, the performance of ARPS-CANOPY in simulating meteorological fields in the vicinity of a low-intensity fire is assessed using flux-tower data collected prior to and during a low-intensity prescribed fire in the New Jersey Pine Barrens in March 2011. A three-dimensional high-resolution plant area density dataset is utilized to define the characteristics of the canopy, and the fire is represented in ARPS-CANOPY as a heat flux to the atmosphere. The standard ARPS model is compared with reanalysis and upper-air data to establish that the model can simulate the observed synoptic-mesoscale and planetary boundary layer features that are salient to this study. ARPS-CANOPY profiles of mean turbulent kinetic energy, wind speed/direction, and temperature exhibit patterns that appear in the flux-tower observations during both the preburn phase of the experiment and the period of time the flux tower experienced perturbed atmospheric conditions due to the impinging fire. Last, the character and source of turbulence in and around the fire line are examined. These results are encouraging for smoke prediction efforts since transport of smoke from low-intensity fires is highly sensitive to the near-surface meteorological conditions and, in particular, turbulent flows.

Corresponding author address: Michael Kiefer, Michigan State University, Geography Bldg. Rm. 203, 673 Auditorium Rd., East Lansing, MI 48824. E-mail: mtkiefer@msu.edu
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