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EEnsemble Simulations with Coupled Atmospheric
Dynamic and Dispersion Models:
Illustrating Uncertainties in Dosage Simulations
Summary of a presentation by Tom Warner, University of Colorado
Ensemble simulations made using a coupled atmospheric dynamic model and a proba-
bilistic Lagrangian puff dispersion model were employed in a forensic analysis of the transport
and dispersion of a toxic gas that may have been released near A1 Muthanna, Iraq, during the Gulf
War. The ensemble study had two objectives, the first of which was to determine the sensitivity of
the calculated dosage fields to the choices that were to be made about the configuration of the
atmospheric dynamic model. In this test, various choices were made for model physics repre-
sentations and for the large-scale analyses that were used to construct the model's initial and
boundary conditions. The second study objective was to examine the dispersion model's ability to
use ensemble inputs to predict dosage probability distributions. Here, the dispersion model was
used with the ensemble mean fields from the individual atmospheric dynamic model runs,
including the variability in the individual wind fields, to generate dosage probabilities. These are
compared with the explicit dosage probabilities derived from the individual runs of the coupled
modeling system.
The atmospheric dynamic model was the Pennlyvania State-National Center for
Atmospheric Research (NCAR) MM5 modeling system (Dudhia, 1993; Grell et al., 1994~. The
triply nested computational grids used grid increments of 3.3, 10, and 30 km, and they are shown
in Figure E. 1. The high-resolution grid was considered necessary because fine-scale desert land-
scape properties can influence the boundary layer depth, and lakes in the area have dynamic
effects that should be resolved. There were two inner grids. One was centered over A1 Muthanna
in central Iraq (grid 3N), where dispersion simulations were required. Another was centered over
Hafar Al-Batin (grid 3S), the area closest to A1 Muthanna with a similarly arid climate and with
surface and radiosonde data available for comparison with the simulations. The nested grids, each
with 35 computational layers in the vertical, were two-way interacting during the simulation.
Simulations proceeded simultaneously on both grids 3S and 3N. Because the lowest model
computational layer was approximately 40 m above ground level, with increasing layer depths
above, it was not possible for the model to resolve the shallow nocturnal planetary boundary layer
well.
80
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APPENDIX E
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FIGURE E.1 Geographic extent of the computational grids. Grid 1 has a grid increment of 30 km, grid 2
has a grid increment of 10 km, and grids 3N and 3S have a grid increment of 3.3 km. The locations of
surface (plus signs) and upper-air (circles) observations are also shown.
To create the ensemble of MM5 simulations, various options were employed for the
physical process parameterizations and for the global-scale analyses that were combined with local
data to generate regional atmospheric analyses. Table E. 1 defines the model configurations for the
various experiments performed. The MM5 model physics options used in the ensemble study
included three PBL parameterizations: (1) the MRF (Medium-Range Forecast) technique used in
the MRF model of the National Centers for Environmental Prediction (NCEP); (2) the turbulent
kinetic energy (TKE) parameterization; and (3) the Burk-Thompson parameterization (BT). Both
simple and relatively complex approaches were used for the surface energy and moisture budgets.
The simpler approach employed the "slab model," in which ground temperature is calculated for a
single soil layer and there is no explicit representation of vegetation effects. The more complex
approach used a fairly complete land-surface model (LSM). The model initial conditions were
defined by analyzing radiosonde and surface data to the model grids using a successive correction,
objective analysis procedure with three different first-guess fields. The three first-guess fields
were the NCEP global analysis, the European Center for Medium-Range Weather Forecasting
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82
APPENDIX E
(ECMWF) global analysis, and the Navy Operational Global Atmospheric Prediction System
(NOGAPS) analysis.
The probabilistic Lagrangian puff dispersion model used in this study was the SCIPUFF
model (Sykes et al.; 1984, 1988, 1993~. The acronym SCIPUFF describes two aspects of the
model. First, the numerical technique employed to solve the dispersion model equations is the
Gaussian-puff method in which a collection of overlapping three-dimensional puffs is used to
represent an arbitrary time-dependent concentration field. The number of puffs is determined
internally by the model, and it depends on such factors as the release characteristics, the size of the
domain, the numerical resolution choices, and the meteorology. Second, the turbulent diffusion
parameterization used in SCIPUFF is based on second-order closure theories, providing a direct
relationship between measurable velocity statistics and the turbulent dispersion rates.
Plate 6 displays the SCIPUFF-calculated dosages for the different ensemble members 85
hours after release. (In most cases the gas plume had entirely exited the computational domain by
this time; in the others, gas concentrations remaining on the grid were negligible.) Even though
the gas moved generally to the southeast for all ensemble members, there clearly are significant
differences among the solutions. In some experiments, the plume remained narrow as it traveled
to the southeast. In others, the same initial movement prevailed, but the plume widened rapidly,
especially toward the west. These differences result from the fact that some ensemble members
carry low-level easterlies into southern and central Iraq (thus causing a westward displacement of
the plume boundary), while other ensemble members do not.
One way to quantify the practical implications of the spread in the model solutions is to plot
the time evolution of the area covered by the dosage above some threshold (e.g., the dosage
corresponding to the "first noticeable effects" or the "general population limited. We arbitrarily
chose the lowest dosage plotted in Plate 6 for this purpose. (Note that all dosages scale exactly
with the initial mass of the gas release.) Area-coverage computations were limited to the part of
the grid that is within a 210-lon radius of the release point. Figure E.2 shows that the areas with
dosage above the threshold vary by more than a factor of four within the ensemble. In addition, it
TABLE E. 1 Experimental Conditions for Each of the Ensemble-Member Simulations.
Large Scale Analysis
Ensemble Member Used for First Guess Boundary Layer
Number and Lateral Boundary Parameterizations Surface Physics
Conditions
1 ECMWF MRF Slab
2 NCEP MRF Slab
3 NOGAPS MRF Slab
4 ECMWF MRF LSM
5 NCEP MRF LSM
6 NOGAPS MRF LSM
7 ECMWF TKE Slab
8 NCEP TKE Slab
9 NOGAPS TKE Slab
10 ECMWF BT Slab
11 NCEP BT Slab
12 NOGAPS BT Slab
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APPENDIX E
A.
A:
3~:
200:~0
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Elapsed Time aRer Oas Release (~)
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FIGURE E.2 Time evolution of the area with dosage above the threshold corresponding to the lowest
value plotted in Plate 6. Area computations are limited to the part of the grid within the circle that is
tangent to the sides of grid 3N, with a radius of 210 km (see arc in upper-left panel of Plate 6).
83
also is clear that the area coverage for the threshold dosage continues to increase out to almost 30
hours for some simulations, but for others, the area exposed reaches its maximum in as little as 8
hours.
The ensemble of dosage simulations makes it possible to calculate plots of the probability
of dosages exceeding certain thresholds. Plate 7 is an isoprobability diagram for dosages
exceeding 10- kit s my. ~
i, ~ i, Where most of the ensemble members agree that the dosage at a location
exceeds the threshold, the probability is high. Such probabilistic information is clearly much more
useful to decision-makers than a single dosage simulation of unknown accuracy.
REFERENCES
Dudhia, J. 1993. A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and
the simulation of an Atlantic cyclone and cold front. Mon. Weal Rev. 121: 1493-1513.
Grell, G.A., J. Dudhia, and D.R. Stauffer. 1994. A description of the fifth generation Penn StatelNCAR
mesoscale model (MM5~. NCAR Technical Note, NCAA/TN 398+STR, 138pp. (Available from
NCAR, P.O. Box 3000, Boulder, CO 80307.)
Sykes, R.I., W.S. Lewellen, and S.F. Parker. 1984. A turbulent-transport model for concentration fluctuations
and fluxes. J. Fluid Mech. 139:193-218.
Sykes, R.I., W.S. Lewellen, S.F. Parker, and D.S. Henn. 1988. A hierarchy of dynamic plume models
incorporating uncertainty. Volume 4: Second order Closure Integrated Puff, Electric Power Research
Institute, EPRI EA-6095 Volume 4, Project 1616-28. (Available from R.I. Sykes, ARAP/Titan, 50
Washington Rd., P.O. Box 2229, Princeton, NJ 08543-2229.)
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84
APPENDIX E
Sykes, R.I., S.F. Parker, D.S. Henn, and W.S. Lewellen. 1993. Numerical simulation of ANATEX tracer data
using a turbulent closure model for long-range dispersion. J. Appl. Meteor. 32:929-947.
Warner, T.T., R.S. Sheu, J. Bowers, R.I. Sykes, G.C. Dodd, and D.S. Henn. 2001. Ensemble simulations with
coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations.
J. Appl. Meteor. 41:488-504.
Representative terms from entire chapter:
dispersion model
