Environmental Fluid Mechanics

, Volume 8, Issue 4, pp 367–387 | Cite as

Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model

  • Venkata Srinivas Challa
  • Jayakumar Indrcanti
  • Julius M. Baham
  • Chuck Patrick
  • Monika K. Rabarison
  • John H. Young
  • Robert Hughes
  • Shelton J. Swanier
  • Mark G. Hardy
  • Anjaneyulu YerramilliEmail author
Original Article


Mesoscale transport and dispersion of air pollutants from a few major point sources in the Mississippi Gulf coastal region is calculated using a coupled modeling system consisting of the atmospheric dynamical model WRF and the lagrangian particle model HYSPLIT. The sensitivity of the dispersion model results to the meteorological fields is studied by conducting an ensemble of simulations using the WRF model for the same dispersion case. Several parameterization schemes for the physical processes of boundary layer turbulence and land surface temperature/moisture prediction in WRF are used in various combinations to produce different meteorological members which are then used for dispersion simulation. The uncertainty in the simulated concentration probabilities to the meteorological model configurations and the ensemble mean are presented. The parameters used for determining the uncertainties include the wind fields, temperature, area of concentration and the levels of concentration. The results indicate that dispersion model results are influenced by the choices made in respect of the planetary boundary layer and land surface schemes in the mesoscale model to produce the meteorological forecast thereby leading to certain amount of uncertainty in the resultant concentrations. Results show that the specific choices made about the atmospheric model configuration can significantly after the simulated concentrations.


Meso scale Atmospheric dispersion WRF HYSPLIT Sensitivity 


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  1. 1.
    Berg LK, Zhong S (2005) Sensitivity of MM5-simulated boundary layer characteristics to turbulence parameterizations. J Appl Meteorol 44(9): 1467–1483CrossRefGoogle Scholar
  2. 2.
    Chen F, Dudhia J (2001) Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon Weather Rev 129: 569–585 doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2Google Scholar
  3. 3.
    Dabberdt WF, Miller E (2000) Uncertainly, ensembles and air quality dispersion modeling: application and challenges. Atmos Environ 34: 4667–4673CrossRefGoogle Scholar
  4. 4.
    Delle Monache L, Stull RB (2003) An ensemble air-quality forecast over Europe during an ozone episode. Atmos Environ 37: 3469–3474. doi: 10.1016/S1352-2310(03)00475-8 CrossRefGoogle Scholar
  5. 5.
    Draxler RR (2002) Verification of an ensemble dispersion calculation. J Appl Meteorol 42: 308–317 doi:10.1175/1520-0450(2003)042<0308:EOAEDC>2.0.CO;2CrossRefGoogle Scholar
  6. 6.
    Draxler RR (2003) Evaluation of an ensemble dispersion calculation. J Appl Meteorol 42: 308–317 doi:10.1175/1520-0450(2003)042<0308:EOAEDC>2.0.CO;2CrossRefGoogle Scholar
  7. 7.
    Draxler RR, Hess GD (1997) Description of the HYSPLIT_4 modeling system. NOAA Technical Memorandum ERL ARL-224Google Scholar
  8. 8.
    Draxler RR, Hess GD (1998) An overview of the Hysplit_4 modeling system for trajectories, dispersion and deposition. Aust Meteorol Mag 47: 295–308Google Scholar
  9. 9.
    Dudhia J (1989) Numerical study of convection observed during winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46: 3077–3107 doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2CrossRefGoogle Scholar
  10. 10.
    Dudhia J (1996) A multi-layer soil temperature model for MM5. Preprint from the sixth PSU/NCAR mesoscale model users’ workshopGoogle Scholar
  11. 11.
    Galmarini S, Bianconi R, Klug W, Mikkelsen T, Addis R, Astrup P, Astrup P, Baklanov A, Bartniki J, Bartzis JC, Bellasio R, Bompay F, Buckley R, Bouzom M, Champion H, D’Amours R, Davakis E, Eleveld H, Geertsema GT, Glaab H, Kollax M, Ilvonen M, Manning A, Pechinger U, Persson C, Polreich E, Potemski S, Prodanova M, Saltbones J, Slaper H, Sofiev MA, Syrakov D, Sorensen JH, AuweraL Van der, Valkama I, Zelazny R et al (2004) Ensemble dispersion forecasting—part I: concept, approach and indicators. Atmos Environ 38: 4607–4617. doi: 10.1016/j.atmosenv.2004.05.030 CrossRefGoogle Scholar
  12. 12.
    Han Z, Ueda H, An J (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 CrossRefGoogle Scholar
  13. 13.
    Hong SY, Pan HL (1996) Nonlocal boundary layer vertical diffusion in a medium range forecast model. Mon Weather Rev 124: 2322–2339 doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2CrossRefGoogle Scholar
  14. 14.
    Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132: 103–120 doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2CrossRefGoogle Scholar
  15. 15.
    Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with explicit treatment of entrainment processes. Mon Weather Rev 134: 2318–2341. doi: 10.1175/MWR3199.1 CrossRefGoogle Scholar
  16. 16.
    Janzic ZI (1990) The step-mountain coordinate: physical package. Mon Weather Rev 118: 1429–1443 doi:10.1175/1520-0493(1990)118<1429:TSMCPP>2.0.CO;2CrossRefGoogle Scholar
  17. 17.
    Janjic ZI (1996) The surface layer in the NCEP Eta model. In: Eleventh conference on numerical weather prediction, Norfolk, VA, 19–13 August; Amer Met Soc, Boston, MA, pp 354–355Google Scholar
  18. 18.
    Janjic ZI (2002) Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model, NCEP Office Note, No. 437, 61 ppGoogle Scholar
  19. 19.
    Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47: 2784–2802 doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2CrossRefGoogle Scholar
  20. 20.
    Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritcsh scheme In: In Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models. Amer Met Soc, 246 ppGoogle Scholar
  21. 21.
    Krishnamurty TN, Kishtawal CM, LaRow TE, Bachiochi DR, Zhang Z, Willford CE et al (1999) Improved weather and seasonal climate forecast from multimodal superensemble. Science 285: 1548–1550. doi: 10.1126/science.285.5433.1548 CrossRefGoogle Scholar
  22. 22.
    Maryon RH, Best MJ (1995) Estimating the emissions from a nuclear accident using observations of radioactivity with dispersion model products. Atmos Environ 29: 1853–1869. doi: 10.1016/1352-2310(95)00042-W CrossRefGoogle Scholar
  23. 23.
    Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys 20: 851–875CrossRefGoogle Scholar
  24. 24.
    Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14): 16663–16682. doi: 10.1029/97JD00237 CrossRefGoogle Scholar
  25. 25.
    Mosca S, Graziani G, Klug W, Bellasio R, Bianconi R (1998) A statistical methodology for the evaluation of long-range dispersion models: an application to the ETEX exercise. Atmos Environ 32: 4307–4324. doi: 10.1016/S1352-2310(98)00179-4 CrossRefGoogle Scholar
  26. 26.
    Pielke RA, Uliasz M (1998) Use of meteorological models as input to regional and mesoscale air quality models—limitations and strengths. Atmos Environ 32: 1455–1466. doi: 10.1016/S1352-2310(97)00140-4 CrossRefGoogle Scholar
  27. 27.
    Pielke RA, McNider RT, Moran MD, Moon DA, Stocker RA, Walko RL et al (1991) Regional and mesoscale meteorological modeling as applied to air quality studies. In: Van Dop H, Steyn DG (eds) Air pollution modeling and its application, VII. Plenum Press, pp 259–290Google Scholar
  28. 28.
    Seaman NL (2000) Meteorological modeling for air-quality assessments. Atmos Environ 34: 2231–2259. doi: 10.1016/S1352-2310(99)00466-5 CrossRefGoogle Scholar
  29. 29.
    Seaman NL, Stauffer DR, Lario AM (1995) A multiscale four-dimensional data assimilation system applied in the San Joaquin Valley during SARMAP Part I: modeling design and basic performance characteristics. J Appl Meteorol 34: 1739–1761 doi:10.1175/1520-0450(1995)034<1739:AMFDDA>2.0.CO;2CrossRefGoogle Scholar
  30. 30.
    Segal M, Pielke RA, Arritt RW, Moran MD, Yu CH, Henderson D (1986) Southern Florida air pollution climatology study and selected episodic impacts. Report prepared for Air Quality Division, National Park Service, Department of Interior, Denver, COGoogle Scholar
  31. 31.
    Sivillo J, Ahlquist JE, Toth Z (1997) An ensemble forecasting primer. Weather Forecast 12: 809–817 doi:10.1175/1520-0434(1997)012<0809:AEFP>2.0.CO;2CrossRefGoogle Scholar
  32. 32.
    Skamarock WC, Klemp J, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the Advanced Research WRF Version 2. NCAR Technical Note, NCAR/TN-468+STR. Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, Colorado, USAGoogle Scholar
  33. 33.
    Stauffer DR, Seaman NL (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: experiments with synoptic-scale data. Mon Weather Rev 118: 1250–1277 doi:10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2Google Scholar
  34. 34.
    Stauffer DR, Seaman NL, Binkowski F (1991) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II-Effects of data assimilation within the planetary boundary layer. Mon Weather Rev 119: 734–754 doi:10.1175/1520-0493(1991)119<0734:UOFDDA>2.0.CO;2Google Scholar
  35. 35.
    Straume AG (2001) A more extensive investigation of the use of ensemble forecasts for dispersion model evaluation. J Appl Meteorol 40: 425–445 doi:10.1175/1520-0450(2001)040<0425:AMEIOT>2.0.CO;2CrossRefGoogle Scholar
  36. 36.
    Uliasz M (1993) The atmospheric mesoscale dispersion modeling system. J Appl Meteorol 32: 139–149 doi:10.1175/1520-0450(1993)032<0139:TAMDMS>2.0.CO;2CrossRefGoogle Scholar
  37. 37.
    Wandishin MS, Mullen SL, Stensrud DJ, Brooks HE (2001) Evaluation of a short-range multimodel ensemble system. Mon Weather Rev 129: 729–747 doi:10.1175/1520-0493(2001)129<0729:EOASRM>2.0.CO;2CrossRefGoogle Scholar
  38. 38.
    Warner TT, Sheu R, Bowers J, Sykes RI, Dodd GC, Henn DS (2002) Ensemble simulations with coupled atmospheric dynamic and dispersion models: illustrating uncertainties in dosage simulations. J Appl Meteorol 41: 448–504 doi:10.1175/1520-0450(2002)041<0488:ESWCAD>2.0.CO;2CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Venkata Srinivas Challa
    • 1
  • Jayakumar Indrcanti
    • 1
  • Julius M. Baham
    • 1
  • Chuck Patrick
    • 1
  • Monika K. Rabarison
    • 1
  • John H. Young
    • 1
  • Robert Hughes
    • 1
  • Shelton J. Swanier
    • 1
  • Mark G. Hardy
    • 1
  • Anjaneyulu Yerramilli
    • 1
    Email author
  1. 1.Trent Lott Geospatial and Visualization Research CentreJackson State UniversityJacksonUSA

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