Chapter 4.0 A review of uncertainty and sensitivity analyses of atmospheric transport and dispersion models

Developments in Environmental Science

Volumn 6, Issue , 2007, Pages 331-351

  Hanna, Steven R ^a  

^a NONE

Author keywords

[No Author keywords available]

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EID: 44349157069     PISSN: 14748177     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S1474-8177(07)06040-8     Document Type: Review

References (52)

1. Beck M.B. Water quality modeling: A review of the analysis of uncertainty. Water Resour. Res. 23 (1987) 1393-1442
2. Bergin M.S., and Milford J.B. Application of Bayesian Monte Carlo analysis to a Lagrangian photochemical air quality model. Atmos. Environ. 34 (2000) 781-792
3. Carmichael G.R., Sandu A., and Potra F.A. Sensitivity analysis for atmospheric chemistry models via automatic differentiation. Atmos. Environ. 31 (1997) 475-489
4. Cimorelli A.J., Perry S.G., Venkatram A., Weil J.C., Paine R.J., Wilson R.B., Lee R.F., Peters W.D., and Brode R.W. AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. J. Appl. Meteorol. 44 (2005) 682-693
5. Cullen A.C., and Frey H.C. Probabilistic Techniques in Exposure Assessment. A Handbook for Dealing with Variability and Uncertainty in Models and Inputs (1999), Plenum Press, New York 335 pages
6. Dabberdt W.F., and Miller E. Uncertainty, ensembles, and air quality dispersion modeling: Applications and challenges. Atmos. Environ. 34 (2000) 4667-4673
7. Delle Monache L., and Stull R.B. An ensemble air-quality forecast over western Europe during and ozone episode. Atmos. Environ. 37 (2003) 3469-3474
8. DoD (U.S. Department of Defense), 2000. Technical Report-Modeling and Risk Characterization of US Demolition Operations at the Khamisiyah Pit. Special assistant to the Under Secretary of Defense (Personnel and Readiness) for Gulf War Illnesses, Medical Readiness, and Military Deployments, U.S. Department of Defense, p.193, Available from http://www.gulflink.osd.mil/khamisiyah_tech/
9. Downing D.J., Gardner R.H., and Hoffman F.O. Response surface methodologies for uncertainty analysis in assessment models. Technometrics 27 (1985) 151-163
10. Draxler R.R. Evaluation of an ensemble dispersion calculation. J. Appl. Meteorol. 42 (2003) 308-317
11. Du, J., McQueen, J.T., DiMego, G., Black, T., Juang, H., Rogers, E., Ferrier, B., Zhou, B., Toth, Z., Tracton, M.S., 2004. The NOAA/NWS/NCEP Short Range Ensemble Forecast (SREF) system: Evaluation of an initial condition vs. multiple model physics ensemble approach. Proceedings, 16th Conference on Numerical Weather Prediction. American Meteorological Society, Paper 21.3, Seattle, WA, p. 10.
12. EPA, 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion Model (revised): Vol. II-Description of Model Algorithms. EPA-454/b-95-0036, USEPA, RTP, NC 27711.
13. EPA, 2002. Example Application of Modeling Toxic Air Pollutants in Urban Areas. EPA-454/R-02-003, OAQPS/EPA, RTP, NC 27711, p. 91, http://www.epa.gov/scram001/tt25htm#toxics
14. Fisher B. Fuzzy environmental decision-making: Applications to air pollution. Atmos. Environ. 37 (2003) 1865-1877
15. Fox D.G. Uncertainty in air quality modeling. Bull. Am. Meteorol. Soc. 65 (1982) 27-35
16. Freeman D.L., Egami R.T., Robinson N.F., and Watson J.G. A method for propagating measurement uncertainty through dispersion models. J. Air Poll. Control Assoc. 36 (1986) 246-253
17. Galmarini S., Bianconi R., Klug W., Mikkelsen T., Addis R., Andonopoulos S., et al. Ensemble dispersion forecasting - Part I: concept, approach and indicators. Atmos. Environ. 38 (2004) 4607-4617
18. Galmarini S., Bianconi R., Addis R., Andonopoulos S., Astrup P., Bartzis J., et al. Ensemble dispersion forecasting - Part II: application and evaluation. Atmos. Environ. 38 (2004) 4619-4632
19. Hamill T.M., Mullen S.L., Snyder C., Toth Z., and Baumhefner D.P. Ensemble forecasting in the short to medium range: Report from a workshop. Bull. Am. Meteorol. Soc. 81 (2000) 2653-2664
20. Hanna S.R. Concentration fluctuations in a smoke plume. Atmos. Environ. 18 (1984) 1091-1106
21. Hanna S.R. Uncertainties in air quality model predictions. Bound. Lay. Meteorol. 62 (1993) 3-20
22. Hanna S.R., and Chang J.C. Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10000 m. Bound. Lay. Meteorol. 60 (1992) 309-324
23. Hanna S.R., Chang J.C., and Fernau M.E. Monte Carlo estimates of uncertainties in predictions by a photochemical grid model due to uncertainties in input variables. Atmos. Environ. 32 (1998) 3619-3628
24. Hanna S.R., Lu Z., Frey H.C., Wheeler N., Vukovich J., Arumachalam S., and Fernau M. Uncertainties in predicted ozone concentration due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain. Atmos. Environ. 35 (2001) 891-903
25. Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D., 2007. Uncertainties in air toxics calculated by the dispersion models AERMOD and ISC in the Houston Ship Channel area. J. Appl. Meteorol. (accepted for publication).
26. Hanna, S.R., Wilkinson, J., Russell, A.G., Vukovich, J., Hansen, D.A., 2005. Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in Chemical Transport Model predictions. J. Geophys. Res. 110, D01302, doi:10.1029/2004JD004986.
27. Helton J.C. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J. Stat. Comput. Simul. 57 (1997) 3-76
28. IAEA, 1989. Evaluating the reliability of predictions made using environmental transfer models. IAEA Safety Series No. 100, International Atomic Energy Agency, Vienna, Austria.
29. Iman R.L., and Helton J.C. An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Anal. 8 (1988) 71-90
30. Irwin J.S., and Hanna S.R. Characterizing uncertainty in plume dispersion models. Int. J. Environ. Poll. 25 (2005) 16-24
31. Irwin J.S., Rao S.T., Petersen W.B., and Turner D.B. Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty. Atmos. Environ. 21 (1987) 1927-1937
32. Isukapalli S.S., Roy A., and Georgopoulos P.G. Stochastic response surface methods (SRSMs) for uncertainty characterization and propagation: Application to environmental and biological systems. Risk Anal. 18 (1998) 351-363
33. Lamb R.G., and Hati S.K. The representation of atmospheric motion in models of regional-scale air pollution. J. Appl. Meteorol. 26 (1987) 837-846
34. Leach M.J., and Chin H.-N. Uncertainty in dispersion forecasting using meteorological ensembles. In: Gryning S.-E., and Schiermeier F. (Eds). Air Pollution Modeling and Its Application XIV (2001), Kluwer Academic/Plenum Publishers, New York 659-663
35. Lewellen W.S., and Sykes R.I. Meteorological data needs for modeling air quality uncertainties. J. Atmos. Oceanic Tech. 6 (1989) 759-768
36. Lorenz E.N. The predictability of a flow which possesses many scales of motion. Tellus 21 (1969) 289-307
37. Menut L. Adjoint modeling for atmospheric pollution process sensitivity at regional scale. J. Geophys. Res. 108 D17 (2003) 8562
38. Morgan M.G., and Henrion M. Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis (1990), Cambridge University Press, New York
39. NCRP, 1996. In: Hoffman F.O. (Ed.), A Guide for Uncertainty Analysis in Dose and Risk Assessments Related to Environmental Contamination. NRCP Commentary Number 14, Nat. Council on Radiation Protection and Measurement, 7910 Woodmont Ave., Bethesda, MD.
40. Oreskes N., Shrader-Frechette K., and Beitz K. Verification, validation, and confirmation of numerical models in the earth sciences. Science 263 (1994) 641-646
41. Pielke Sr. R.A. The need to assess uncertainty in air quality evaluations. Atmos. Environ. 32 (1998) 1467-1468
42. Rao K.S. Uncertainty analysis in atmospheric dispersion modeling. Int. J. Pure Appl. Geophys. 162 (2005) 1893-1917
43. Saltelli A., Tarantola S., Campolongo F., and Ratto M. Sensitivity Analysis in Practice. A Guide to Assessing Scientific Models (2004), Wiley, New Jersy
44. Seefeld S., and Stockwell W.R. First-order sensitivity analysis of models with time-dependent parameters: An application to PAN and ozone. Atmos. Environ. 33 (1999) 2941-2953
45. Stein A.F., and Wyngaard J.C. Fluid modeling and the evaluation of inherent uncertainty. J. Appl. Meteorol. 40 (2001) 1769-1774
46. Straume A.G. A more extensive investigation of the use of ensemble forecasts for dispersion model evaluation. J. Appl. Meteorol. 40 (2001) 425-445
47. Straume A.G., Koffi E., and Nodop K. Dispersion modeling using ensemble forecasts compared to ETEX measurements. J. Appl. Meteorol. 37 (1998) 1444-1456
48. Sykes R.I., Lewellen W.S., and Parker S.F. A turbulent transport model for concentration fluctuations and fluxes. J. Fluid Mech. 139 (1984) 193-218
49. Toth Z. Meeting summary: Ensemble forecasting in WRF. Bull. Am. Meteorol. Soc. 82 (2001) 695-697
50. Venkatram A. Inherent uncertainty in air quality modeling. Atmos. Environ. 22 (1988) 1221-1227
51. Warner T.T., Sheu R.-S., Bowers J.F., Sykes R.I., Dodd G.C., and Henn D.S. Ensemble simulations with coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations. J. Appl. Meteorol. 41 (2002) 488-504
52. Weil J.C., Sykes R.I., and Venkatram A. Evaluating air quality models: Review and outlook. J. Appl. Meteorol. 31 (1992) 1121-1145