WMOD00438

• Title: HIPLEX: A Cooperative Program on Rain Augmentation in the High Plains
• Date: 6/1/1984
• Weather Modification - Doc Type: Report

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Figure 2.5- The King Air cloud physics aircraft strengthened
the capacity of HIPLEX to generate critical data used in the
experiments.

2.6 Supporting Studies

In addition to the field projects, HIPLEX involved a
number of supporting studies on cloud modeling, the
response of crops and forage to additional rainfall.
possible economic impacts on increased rainfall. and
ecological factors.

2.6.8 Cloud modeling

The variability of the atmosphere makes it difficult
to perform repeatable experiments; the clouds are
never identical at different times or locations. This
variability has led to the use of statistics to indicate
the average changes. if any, that might be caused by
cloud seeding.

Another approach to estimate the effects of cloud
seeding is through numerical modeling. Simplified
approximations to complex atmospheric processes
can be incorporated into a computer program called
a cloud model. A model can sometimes b'e adjusted
to simulate a real cloud development. Every time a
model is run under the same initial conditions. the
results will be the same. The real atmosphere is not
nearly as consistent. Subsequently, conditions in the
model can be changed to simulate c.loud seeding
effects. For example, the creation of extra ice par-
ticles or the release of extra heat can be introduced
to see what changes occur in the results. If the model
is properly designed, the difference in results
between "natural" and "seeded" computer model
runs should resemble differences observed in real
clouds.

A simple one-dimensional model called GPCM (Great
Plains Cloud Model) was run routinely by the fore-
casters at all sites using data of the upper-air temper-
ature and moisture structure as input. The model

indicated the gross features of cloud development-
cloud-base conditions. cloud-top conditions, and
possible top differences due to seeding (Matthews.
1981). Results from this model clearly showed differ-
ences among the three sites. Another model. "ME-
SOCU," examined differences in cloud development
taking into account some of the cloud vs.
environment interactions. Matthews and Silverman,
(1980) used this model to show the importance of
natural mesoscale vertical motion in controlling
natural variability of cloud growth at HIPLEX sites.

The most complex model applied to HIPLEX condi-
tions was a two-dimensional. time-dependent model
developed at the South Dakota School of Mines and
Technology. The model evolved from a simulation of
convection over mountains. The capabilities of the
model have been greatly expanded under HIPLEX
through the addition of simulations of ice develop-
ment, hailstone growth, effects of convergence in
the wind field. Agl cloud seeding, and dry ice seed-
ing (Chen and Orville, 1980; Orville and Kopp,
1 977).

The Agl seeding modification had to simulate the
complexities of interactions between Agl particles
and water droplets, the rates of ice particle nuclea-
tion as a function of temperature. and the dispersal
of the plume of ice particles through the cloud. The
dry ice seeding modification had to simulate the
nearly instantaneous production of ice crystal
embryos within a confined region of the cloud and
their subsequent dispersal. Both microphysics seed-
ing modifications had to simulate the. change in
cloud dynamics caused by the latent heat released
by conversion of liquid droplets and water vapor to
ice particles. In spite of all these improvements. there
are still certain parameters (like the speed of vertical
motions and the total rainfall) in the model that are
not yet in close agreement with observed values, but
these drawbacks are typical of all present numerical
cloud models. The drawbacks are a result of the
assumptions and simplifications necessary to fit a
model of complex atmospheric processes into the
limited space of even a large computer. Neverthe-
less. a major first step in developing cloud models
for cloud seeding applications and in comparing the
results of model experiments with field observations
has been made. Continued improvement of the
modeling techniques and further calibration against
observations should lead to more realistic models
with greater utility. In the interim, use of these
models within their range of validity has made and
will continue to make significant contributions to the
development of a scientifically sound weather modi-
fication technology. Some cloud model results are
presented in later chapters in the discussion and
interpretation of field observations.

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