Editorial Type:
Article
Article Type:
Research Article

Improving SCIPUFF Dispersion Forecasts with NWP Ensembles

Jared A. Lee Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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L. Joel Peltier Applied Research Laboratory, The Pennsylvania State University, State College, Pennsylvania

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Sue Ellen Haupt Department of Meteorology, The Pennsylvania State University, University Park, and Applied Research Laboratory, The Pennsylvania State University, State College, Pennsylvania

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John C. Wyngaard Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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David R. Stauffer Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Aijun Deng Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Print Publication:
01 Nov 2009
DOI:
https://doi.org/10.1175/2009JAMC2171.1
Page(s):
2305–2319
Restricted access

Abstract

The relationships between atmospheric transport and dispersion (AT&D) plume uncertainty and uncertainties in the transporting wind fields are investigated using the Second-Order Closure, Integrated Puff (SCIPUFF) AT&D model driven by numerical weather prediction (NWP) meteorological fields. Modeled contaminant concentrations for episode 1 of the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) are compared with recorded ground-level concentrations of the inert tracer gas C7F14. This study evaluates a Taylor-diffusion-based parameterization of dispersion uncertainty for SCIPUFF that uses Eulerian meteorological ensemble velocity statistics and a Lagrangian integral time scale as input. These values are diagnosed from NWP ensemble data. Individual simulations of the tracer release fail to reproduce some of the monitored surface concentrations of the tracer. The plumes that are predicted using the uncertainty model in SCIPUFF are broader, improving the overlap between the predicted and observed results. Augmenting the meteorological input to SCIPUFF with meteorological ensemble-uncertainty parameters therefore provides both a better estimate of the expected plume location and the relative uncertainties in the predicted concentrations than single deterministic forecasts. These results suggest that this new parameterization of NWP wind field uncertainty for dispersion may provide more sophisticated information that may benefit emergency response and decision making.

* Current affiliation: Bechtel Corporation, Frederick, Maryland.

Corresponding author address: Sue Ellen Haupt, Applied Research Laboratory, P.O. Box 30, State College, PA 16804. Email: haupts2@asme.org

Abstract

The relationships between atmospheric transport and dispersion (AT&D) plume uncertainty and uncertainties in the transporting wind fields are investigated using the Second-Order Closure, Integrated Puff (SCIPUFF) AT&D model driven by numerical weather prediction (NWP) meteorological fields. Modeled contaminant concentrations for episode 1 of the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) are compared with recorded ground-level concentrations of the inert tracer gas C7F14. This study evaluates a Taylor-diffusion-based parameterization of dispersion uncertainty for SCIPUFF that uses Eulerian meteorological ensemble velocity statistics and a Lagrangian integral time scale as input. These values are diagnosed from NWP ensemble data. Individual simulations of the tracer release fail to reproduce some of the monitored surface concentrations of the tracer. The plumes that are predicted using the uncertainty model in SCIPUFF are broader, improving the overlap between the predicted and observed results. Augmenting the meteorological input to SCIPUFF with meteorological ensemble-uncertainty parameters therefore provides both a better estimate of the expected plume location and the relative uncertainties in the predicted concentrations than single deterministic forecasts. These results suggest that this new parameterization of NWP wind field uncertainty for dispersion may provide more sophisticated information that may benefit emergency response and decision making.

* Current affiliation: Bechtel Corporation, Frederick, Maryland.

Corresponding author address: Sue Ellen Haupt, Applied Research Laboratory, P.O. Box 30, State College, PA 16804. Email: haupts2@asme.org

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Journal of Applied Meteorology and Climatology

  • Corrsin, S., 1963: Estimates of the relations between Eulerian and Lagrangian scales in large Reynolds number turbulence. J. Atmos. Sci., 20 , 115–119.

    • Search Google Scholar
    • Export Citation

  • Csanady, G. T., 1973: Turbulent Diffusion in the Environment. Reidel, 248 pp.

  • Dabberdt, W. F., and E. Miller, 2000: Uncertainty, ensembles and air quality dispersion modeling: Applications and challenges. Atmos. Environ., 34 , 4667–4673.

    • Search Google Scholar
    • Export Citation

  • Deng, A., and D. R. Stauffer, 2006: On improving 4-km mesoscale model simulations. J. Appl. Meteor. Climatol., 45 , 361–381.

  • Deng, A., N. L. Seaman, G. K. Hunter, and D. R. Stauffer, 2004: Evaluation of interregional transport using the MM5-SCIPUFF system. J. Appl. Meteor., 43 , 1864–1886.

    • Search Google Scholar
    • Export Citation

  • Draxler, R. R., 1987: Sensitivity of a trajectory model to the spatial and temporal resolution of the meteorological data during CAPTEX. J. Climate Appl. Meteor., 26 , 1577–1588.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129 , 2461–2480. Corrigendum, 130, 1661–1663.

    • Search Google Scholar
    • Export Citation

  • Ferber, J. F., J. L. Hefter, R. R. Draxler, R. J. Lagomarsino, F. L. Thomas, R. N. Dietz, and C. M. Benkovitz, 1986: Cross-Appalachian Tracer Experiment (CAPTEX ’83) final report. NOAA Tech. Memo. ERL ARL-142, 60 pp.

    • Search Google Scholar
    • Export Citation

  • Fox, D. G., 1984: Uncertainty in air quality modeling. Bull. Amer. Meteor. Soc., 65 , 27–36.

  • Galmarini, S., and Coauthors, 2004a: Ensemble dispersion forecasting—Part I: Application and evaluation. Atmos. Environ., 38 , 4607–4617.

    • Search Google Scholar
    • Export Citation

  • Galmarini, S., and Coauthors, 2004b: Ensemble dispersion forecasting—Part II: Concept, approach and indicators. Atmos. Environ., 38 , 4619–4632.

    • Search Google Scholar
    • Export Citation

  • Gifford, F. A., 1982: Horizontal diffusion in the atmosphere: A Lagrangian-dynamical theory. Atmos. Environ., 16 , 505–512.

  • Gifford, F. A., 1987: The time-scale of atmospheric diffusion considered in relation to the universal diffusion function, f1. Atmos. Environ., 21 , 1315–1320.

    • Search Google Scholar
    • Export Citation

  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 138 pp.

    • Search Google Scholar
    • Export Citation

  • Grimit, E. P., and C. F. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17 , 192–205.

    • Search Google Scholar
    • Export Citation

  • Haagenson, P. L., Y-H. Kuo, M. Skumanich, and N. L. Seaman, 1987: Tracer verification of trajectory models. J. Climate Appl. Meteor., 26 , 410–426.

    • Search Google Scholar
    • Export Citation

  • 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.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124 , 1225–1242.

    • Search Google Scholar
    • Export Citation

  • Kolczynski Jr., 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.

    • Search Google Scholar
    • Export Citation

  • Koračin, D., A. Panorska, V. Isakov, J. S. Touma, and J. Swall, 2007: A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA. Atmos. Environ., 41 , 617–628.

    • Search Google Scholar
    • Export Citation

  • Lee, J. A., 2007: Improving predictions of contaminant dispersion with SCIPUFF using meteorological uncertainty. M.S. thesis, The Pennsylvania State University, 129 pp.

  • Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102 , 409–418.

  • Lumley, J. L., 1962: The mathematical nature of the problem of relating Lagrangian and Eulerian statistical functions in turbulence. Méchanique de la Turbulence, CNRS, 17–26.

    • Search Google Scholar
    • Export Citation

  • Mallet, V., and B. Sportisse, 2006: Ensemble-based air quality forecasts: A multimodel approach to ozone. J. Geophys. Res., 111 , D18302. doi:10.1029/2005JD006675.

    • Search Google Scholar
    • Export Citation

  • Rao, K. S., 2005: Uncertainty analysis in atmospheric dispersion modeling. Pure Appl. Geophys., 162 , 1893–1917.

  • Riccio, A., G. Giunta, and S. Galmarini, 2007: Seeking for the rational basis of the Median Model: The optimal combination of multi-model ensemble results. Atmos. Chem. Phys., 7 , 5701–5737.

    • Search Google Scholar
    • Export Citation

  • Schroeder, A. J., D. R. Stauffer, N. L. Seaman, A. Deng, A. M. Gibbs, G. K. Hunter, and G. S. Young, 2006: An automated high-resolution, rapidly relocatable meteorological nowcasting and prediction system. Mon. Wea. Rev., 134 , 1237–1265.

    • Search Google Scholar
    • Export Citation

  • Sørensen, J. H., 1998: Sensitivity of the DERMA long-range Gaussian dispersion model to meteorological input and diffusion parameters. Atmos. Environ., 32 , 4195–4206.

    • Search Google Scholar
    • Export Citation

  • Stein, A. F., and J. C. Wyngaard, 2001: Fluid modeling and the evaluation of inherent uncertainty. J. Appl. Meteor., 40 , 1769–1774.

  • Stensrud, D. J., J-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128 , 2077–2107.

    • Search Google Scholar
    • Export Citation

  • Straume, A. G., 2001: A more extensive investigation of the use of ensemble forecasts for dispersion model evaluation. J. Appl. Meteor., 40 , 425–445.

    • Search Google Scholar
    • Export Citation

  • Straume, A. G., E. N. Koffi, and K. Nodop, 1998: Dispersion modeling using ensemble forecasts compared to ETEX measurements. J. Appl. Meteor., 37 , 1444–1456.

    • Search Google Scholar
    • Export Citation

  • Sykes, R. I., S. F. Parker, and D. S. Henn, 2004: SCIPUFF version 2.0, technical documentation. A.R.A.P. Tech. Rep. 727, Titan Corporation, Princeton, NJ, 284 pp.

    • Search Google Scholar
    • Export Citation

  • Taylor, G. I., 1921: Diffusion by continuous movements. Proc. London Math. Soc. Ser., 2 , 196–211.

  • Venkatram, A., 1979: The expected deviation of observed concentrations from predicted ensemble means. Atmos. Environ., 13 , 1547–1549.

  • 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.

    • Search Google Scholar
    • Export Citation

  • Weil, J. C., R. I. Sykes, and A. Venkatram, 1992: Evaluating air-quality models: Review and outlook. J. Appl. Meteor., 31 , 1121–1145.

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