Editorial Type:
Article
Article Type:
Research Article

Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Brian P. Reen Battlefield Environment Division, U.S. Army Research Laboratory, Adelphi, Maryland, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Brian P. Reen in
Current site
Google Scholar
PubMed Close
,
Kerrie J. Schmehl Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Kerrie J. Schmehl in
Current site
Google Scholar
PubMed Close
,
George S. Young Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by George S. Young in
Current site
Google Scholar
PubMed Close
,
Jared A. Lee Department of Meteorology and Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania, and Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Jared A. Lee in
Current site
Google Scholar
PubMed Close
,
Sue Ellen Haupt Department of Meteorology and Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania, and Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Sue Ellen Haupt in
Current site
Google Scholar
PubMed Close
, and
David R. Stauffer Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by David R. Stauffer in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Nov 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0262.1
Page(s):
2610–2626
Restricted access

Abstract

The relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members’ ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability among the ensemble members is largest near the ABL top during the daytime, apparently because of uncertainty in ABL depth. This persists to the second day of the simulation for the 4-member diagnosis mini-ensemble, which varies only the ABL depth, but for the 12-member physics ensemble the concentration variability is large throughout the daytime ABL. This suggests that the increased within-ABL concentration variability on the second day is due to larger differences among the ensemble members’ predicted meteorological conditions rather than being solely due to differences in the ABL depth diagnosis methods. This work demonstrates new analysis methods for the relationship between ABL depth and plume depth within an ensemble framework and provides motivation for directly including ABL depth uncertainty from a meteorological model into an ATD model.

Corresponding author address: Brian Reen, Battlefield Environment Division, U.S. Army Research Laboratory, 2800 Powder Mill Rd., Adelphi, MD 20783. E-mail: brian.p.reen.civ@mail.mil

Abstract

The relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members’ ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability among the ensemble members is largest near the ABL top during the daytime, apparently because of uncertainty in ABL depth. This persists to the second day of the simulation for the 4-member diagnosis mini-ensemble, which varies only the ABL depth, but for the 12-member physics ensemble the concentration variability is large throughout the daytime ABL. This suggests that the increased within-ABL concentration variability on the second day is due to larger differences among the ensemble members’ predicted meteorological conditions rather than being solely due to differences in the ABL depth diagnosis methods. This work demonstrates new analysis methods for the relationship between ABL depth and plume depth within an ensemble framework and provides motivation for directly including ABL depth uncertainty from a meteorological model into an ATD model.

Corresponding author address: Brian Reen, Battlefield Environment Division, U.S. Army Research Laboratory, 2800 Powder Mill Rd., Adelphi, MD 20783. E-mail: brian.p.reen.civ@mail.mil
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569–585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Coniglio, M. C., J. Correia Jr., P. T. Marsh, and F. Kong, 2013: Verification of convection-allowing WRF Model forecasts of the planetary boundary layer using sounding observations. Wea. Forecasting, 28, 842–862, doi:10.1175/WAF-D-12-00103.1.

    • Search Google Scholar
    • Export Citation

  • Desai, A. R., K. J. Davis, C. J. Senff, S. Ismail, E. V. Browell, D. R. Stauffer, and B. P. Reen, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, U.S.A. Part I: Simple prognostic model. Bound.-Layer Meteor., 119, 195–238, doi:10.1007/s10546-005-9024-6.

    • Search Google Scholar
    • Export Citation

  • Doran, J. C., and S. Zhong, 1995: Variations in mixed-layer depths arising from inhomogeneous surface conditions. J. Climate, 8, 1965–1973, doi:10.1175/1520-0442(1995)008<1965:VIMLDA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State–NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513, doi:10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Earth Systems Research Laboratory, cited 2013: NCEP verification of operational models. [Available online at http://rtvs.noaa.gov/.]

  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328–350, doi:10.1175/WAF843.1.

    • Search Google Scholar
    • Export Citation

  • Fox, D. G., 1984: Uncertainty in air quality modeling. Bull. Amer. Meteor. Soc., 65, 27–36, doi:10.1175/1520-0477(1984)065<0027:UIAQM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Galmarini, S., and Coauthors, 2004: Ensemble dispersion forecasting—Part I: Concept, approach and indicators. Atmos. Environ., 38, 4607–4617, doi:10.1016/j.atmosenv.2004.05.030.

    • Search Google Scholar
    • Export Citation

  • Gilliam, R. C., J. M. Godowitch, and S. T. Rao, 2012: Improving the horizontal transport in the lower troposphere with four-dimensional data assimilation. Atmos. Environ., 53, 186–201, doi:10.1016/j.atmosenv.2011.10.064.

    • Search Google Scholar
    • Export Citation

  • Grell, G. A., and D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi:10.1029/2002GL015311.

    • Search Google Scholar
    • Export Citation

  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1995: A description of the fifth generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 122 pp.

  • Han, Z., H. Ueda, and J. An, 2008: Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models. Atmos. Environ., 42, 233–249, doi:10.1016/j.atmosenv.2007.09.053.

    • Search Google Scholar
    • Export Citation

  • Hanna, S. R., 2007: A review of uncertainty and sensitivity analyses of atmospheric transport and dispersion models. Developments in Environmental Science, Vol. 6, C. Borrego and E. Renner, Eds., Elsevier, 331–351.

  • Hanna, S. R., R. Paine, D. Heinold, E. Kintigh, and D. Baker, 2007: Uncertainties in air toxics calculated by the dispersion models AERMOD and ISCST3 in the Houston Ship Channel area. J. Appl. Meteor. Climatol., 46, 1372–1382, doi:10.1175/JAM2540.1.

    • Search Google Scholar
    • Export Citation

  • Hanna, S. R., and Coauthors, 2010: Comparison of observed, MM5, and WRF-NMM model-simulated, and HPAC-assumed boundary-layer meteorological variables for 3 days during the IHOP field experiment. Bound.-Layer Meteor., 134, 285–306, doi:10.1007/s10546-009-9446-7.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.

  • Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, doi:10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation

  • Hu, X.-M., J. W. Nielsen-Gammon, and F. Zhang, 2010: Evaluation of three planetary boundary layer schemes in the WRF Model. J. Appl. Meteor. Climatol.,49, 1831–1844, doi:10.1175/2010JAMC2432.1.

  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation

  • Irwin, J. S., S. T. Rao, W. B. Petersen, and D. B. Turner, 1987: Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty. Atmos. Environ., 21, 1927–1937, doi:10.1016/0004-6981(87)90153-3.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp.

  • Janjić, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271–285, doi:10.1007/s00703-001-0587-6.

    • Search Google Scholar
    • Export Citation

  • Kolczynski, W. C., D. R. Stauffer, S. E. Haupt, and A. Deng, 2009: Ensemble variance calibration for representing meteorological uncertainty for atmospheric transport and dispersion modeling. J. Appl. Meteor. Climatol., 48, 2001–2021, doi:10.1175/2009JAMC2059.1.

    • Search Google Scholar
    • Export Citation

  • Kolczynski, W. C., D. R. Stauffer, S. E. Haupt, N. S. Altman, and A. Deng, 2011: Investigation of ensemble variance as a measure of true forecast variance. Mon. Wea. Rev., 139, 3954–3963, doi:10.1175/MWR-D-10-05081.1.

    • Search Google Scholar
    • Export Citation

  • Lee, J. A., L. J. Peltier, S. E. Haupt, J. C. Wyngaard, D. R. Stauffer, and A. Deng, 2009: Improving SCIPUFF dispersion forecasts with NWP ensembles. J. Appl. Meteor. Climatol., 48, 2305–2319, doi:10.1175/2009JAMC2171.1.

    • Search Google Scholar
    • Export Citation

  • Lee, J. A., W. C. Kolczynski, T. C. McCandless, and S. E. Haupt, 2012: Objective techniques for configuring and down-selecting an NWP ensemble for low-level wind predictions. Mon. Wea. Rev., 140, 2270–2286, doi:10.1175/MWR-D-11-00065.1.

    • Search Google Scholar
    • Export Citation

  • Lewellen, W. S., and R. I. Sykes, 1989: Meteorological data needs for modeling air quality uncertainties. J. Atmos. Oceanic Technol., 6, 759–768, doi:10.1175/1520-0426(1989)006<0759:MDNFMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Panofsky, H. A., 1969: Air pollution meteorology. Amer. Sci., 57, 269–285.

  • Peltier, L. J., S. E. Haupt, J. C. Wyngaard, D. R. Stauffer, A. Deng, J. A. Lee, K. J. Long, and A. J. Annunzio, 2010: Parameterizing mesoscale wind uncertainty for dispersion modeling. J. Appl. Meteor. Climatol., 49, 1604–1614, doi:10.1175/2010JAMC2396.1.

    • Search Google Scholar
    • Export Citation

  • Pleim, J. E., 2007: A combined local and non-local closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1383–1395, doi:10.1175/JAM2539.1.

    • 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, 16–32, doi:10.1175/1520-0450-34.1.16.

    • Search Google Scholar
    • Export Citation

  • Rao, K. S., 2005: Uncertainty analysis in atmospheric dispersion modeling. Pure Appl. Geophys., 162, 1893–1917, doi:10.1007/s00024-005-2697-4.

    • Search Google Scholar
    • Export Citation

  • Reen, B. P., 2007: Data assimilation strategies and land-surface heterogeneity effects in the planetary boundary layer. Ph.D. dissertation, The Pennsylvania State University, 246 pp. [Available online at http://etda.libraries.psu.edu.]

  • Reen, B. P., D. R. Stauffer, K. J. Davis, and A. R. Desai, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, U.S.A. Part II: Mesoscale modelling. Bound.-Layer Meteor., 120, 275–314, doi:10.1007/s10546-006-9056-6.

    • Search Google Scholar
    • Export Citation

  • Reen, B. P., D. R. Stauffer, and K. J. Davis, 2013: Land-surface heterogeneity effects in the planetary boundary layer. Bound.-Layer Meteor.,150, 1–31, doi:10.1007/s10546-013-9860-8.

  • Seibert, P., F. Beyrich, S.-E. Gryning, S. Joffre, A. Rasmussen, and P. Tercier, 2000: Review and intercomparison of operational methods for the determination of the mixing height. Atmos. Environ., 34, 1001–1027, doi:10.1016/S1352-2310(99)00349-0.

    • Search Google Scholar
    • Export Citation

  • Seidel, D. J., C. O. Ao, and K. Li, 2010: Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis. J. Geophys. Res., 115, D16113, doi:10.1029/2009JD013680.

    • Search Google Scholar
    • Export Citation

  • Shafran, P. C., N. L. Seaman, and G. A. Gayno, 2000: Evaluation of numerical predictions of boundary layer structure during the Lake Michigan Ozone Study (LMOS). J. Appl. Meteor., 39, 412–426, doi:10.1175/1520-0450(2000)039<0412:EONPOB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870–1884, doi:10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Smirnova, T. G., J. M. Brown, S. G. Benjamin, and D. Kim, 2000: Parameterization of cold season processes in the MAPS land-surface scheme. J. Geophys. Res., 105, 4077–4086, doi:10.1029/1999JD901047.

    • Search Google Scholar
    • Export Citation

  • Stauffer, D. R., 2012: Uncertainty in environmental NWP modeling. Handbook of Environmental Fluid Dynamics, Volume Two: Systems, Pollution, Modeling, and Measurements, H. J. S. Fernando, Ed., CRC Press, 411–424.

  • Stauffer, D. R., R. C. Muñoz, and N. L. Seaman, 1999: In-cloud turbulence and explicit microphysics in the MM5. Preprints, Ninth PSU/NCAR MM5 Model Users’ Workshop, Boulder, CO, NCAR, 177–180.

  • Stauffer, D. R., N. L. Seaman, G. K. Hunter, S. M. Leidner, A. Lario-Gibbs, and S. Tanrikulu, 2000: A field-coherence technique for meteorological field-program design for air quality studies. Part I: Description and interpretation. J. Appl. Meteor., 39, 297–316, doi:10.1175/1520-0450(2000)039<0297:AFCTFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Sykes, R. I., S. F. Parker, D. S. Henn, and B. Chowdhury, 2006: SCIPUFF version 2.2, technical documentation. ARAP Tech. Rep. 729, Titan Corporation, Princeton, NJ, 317 pp.

  • Warner, T. T., and R.-S. Sheu, 2000: Multiscale local forcing of the Arabian Desert daytime boundary layer, and implications for the dispersion of surface-released contaminants. J. Appl. Meteor., 39, 686–707, doi:10.1175/1520-0450-39.5.686.

    • Search Google Scholar
    • Export Citation

  • Warner, T. T., R.-S. Sheu, J. F. Bowers, R. I. Sykes, G. C. Dodd, and D. S. Henn, 2002: Ensemble simulations with coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations. J. Appl. Meteor., 41, 488–504, doi:10.1175/1520-0450(2002)041<0488:ESWCAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Xiu, A., and J. E. Pleim, 2001: Development of a land surface model. Part I: Application in a mesoscale meteorology model. J. Appl. Meteor., 40, 192–209, doi:10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhang, D.-L., and W. Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157–169, doi:10.1175/1520-0450(2004)043<0157:DCOSWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 170 67 2
PDF Downloads 87 21 0