1.
Background
RAP
has continued its work in developing operational mesoscale models
in collaboration with other divisions. For example, the NCAR team
works with the Army Test and Evaluation Command (ATEC) to develop
and install operational, mesogamma scale, general-purpose NWP systems. Systems
have been accepted by the Army for the Dugway Proving Ground, Utah;
the White Sands Missile Range, New Mexico; and the Aberdeen Test Center,
Maryland. An additional system has been installed at the Yuma Proving
Ground, Arizona, and is undergoing evaluation, and another one will
be installed at Fort Greely, Alaska in FY02.
Numerical
weather prediction work in RAP also focuses on the development of
improved modeling techniques and knowledge of atmospheric processes,
with the ultimate goal of improving our ability to predict and understand
various weather phenomena. Such work has ranged from an analysis
of Arabian Desert boundary layer processes, to studies of the land-surface
modulation of thunderstorms using a convection-resolving mesoscale
model, to an analysis of locally forced circulations in the Great
Basin Desert. Much of this work has been reported in previous Annual
Scientific Reports; new developments and initiatives will be discussed
here.
2.
Ensemble simulations with coupled atmospheric dynamic and dispersion
models: Illustrating uncertainties in dosage simulations
Ensemble
simulations made using a coupled atmospheric dynamic model and a probabilistic
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 Al 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 must
be made about the configuration of the atmospheric dynamic model.
In this test, various choices were used for model physics representations
and for the large-scale analyses that were used to construct the model
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. An example
of the dosage simulated by one of the ensemble members is shown in
Figure E1.
The
results demonstrate that the specific choices made about the dynamic-model
configuration and the large-scale analyses can have a large impact
on the simulated dosages. For example, the area near the source that
is exposed to a selected dosage threshold varies by up to a factor
of four among members of the ensemble. The agreement between the explicit
and ensemble dosage probabilities is relatively good for both low
and high dosage levels. Although only one ensemble was considered
in this study, the encouraging results suggest that a probabilistic
dispersion model may be of value in quantifying the effects of uncertainties
in a dynamic model ensemble on dispersion model predictions of atmospheric
transport and dispersion.
(RAP
team: T. Warner, R. Sheu, D. Rife)