WMOD00268

• Applicant: CWCB
• Sponsor Name: USBR
• Project Name: Final Report Weather Damage Mitigation Program
• Title: Numerical Simulations of Snowpack Augmentation for Drought Mitigation Studies in the Colorado Rocky Mountains
• Prepared For: USBR - WDMP
• Prepared By: Curt Hartzell, Dr. William Cotton, Joe Busto
• Date: 9/1/2005
• State: CO
• Weather Modification - Doc Type: Scientific Study

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. The model predicted seedability could be real; however, because of the
model over precipitation prediction bias and low amounts of supercooled
liquid water content, this possibility is doubtful.
. The background CCN and IN concentrations are unknown but instead are
determined by our selected background concentrations. Too Iowa
background CCN concentration would make clouds more efficient in
natural precipitation formation thereby lowering seedability. Too high
background IN concentrations would likely lead to lower seedability.
. There is circumstantial evidence that the model-predicted supercooled
liquid water content is too low, thereby lowering seedability.
. The evaluated over-prediction bias in precipitation may lead to reduced
opportunities for precipitation enhancement in the model.
. Banded patterns of seed - no seed differences on daily totals suggest a
possible dynamic response to seeding. This pattern of differences results
in much of the target area being in regions of reduced precipitation.
. The low-level warm temperature bias in the model results in delayed Agl
nuclei activation and reduced effectiveness of the seeding agent.
However, this effect has overall a small impact on seedability.
. The simulated transport and diffusion of seeding material from the
generator sites is getting into the clouds too far downwind of the generate
sites. However, the particle modeling suggests that seeded material is
delivered to the target area at levels suitable for seeding, which argues
against the notion that seeding material is not getting into the intended
seeding zones.

It is recommended that, because this was only a one-year contract and
research funding was limited, additional modeling studies are warranted. One of
the first things that needs to be done is to determine the cause of the model over-
prediction bias in precipitation. Another is to explore the various hypotheses that
have been put forward to explain the very small differences between seed and
no-seed precipitation amounts. Still another area to explore is the almost non-
existent SLW in the 2-hr vertically integrated maps over the target area;
additional sensitivity tests would be useful. Also, it would also be desirable to
rerun all or at least the 30 selected days with higher resolution to determine if
increased resolution reduced the precipitation bias and/or the seed, no-seed
differences.

In support of future operational cloud seeding projects in which a model is
used as part of the evaluation technique, it is urged that background CCN and IN
concentrations be measured. Preferably this would be airborne but in lieu of that
longer term ground-based measurements, particularly from higher-terrain sites
would be desirable. Other items that would be very useful on such a project
would be a vertically-pointing radiometer near the summit on the target mountain
barrier for SLW detection, and the use of scanning cloud radar for identifying
regions of liquid water in the clouds and to follow precipitation morphology. In
addition the combination of model predictions and new observations such as

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