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OCR for page 89
A
Glaciogenic and Hygroscopic Seeding:
Previous Research and Current Status
Details of the methods and findings stemming from research employing or
relating to glaciogenic and hydroscopic seeding are discussed below. The glaciogenic
seeding approach is divided into static and dynamic seeding, and separates convective
from layered or stratiform cloud processes.
PREVIOUS RESEARCH
Glaciogenic Seeding Expel iments
Stalic Seeding. Convective Clock
Since convective storms produce a significant percentage of the rainfall occurring
over many parts of the world, these cloud systems have been the subject of numerous
seeding experiments to test the static seeding concepts. The best-known early
experiments on convective clouds were the Arizona Projects (Batten and Kassander,
1967), the Israeli experiments (Gagin and Newn~ann, 1974), and the Whitetop experiment
(Braham, Jr., ] 964~ 1979~. Measurements of physical variables were made on al] three
projects, but they were linked by the crude measurement systems available at the tinge.
These measurements helped in the interpretation of the statistical results and placed the
physical concept on a ironer scientific base (Cotton, 1986~. The exper;~ents used area-
wide seeding with silver iodide dispensed Tom airplanes flying at cloud-base levels
upwind of the target areas. Although statistical significance was not achieved in
Whitetop7 the data indicated a decrease in rainfall following seeding. This result also was
reported in the Arizona experiment (Batten and Kassa~der, 1967; Neyman et al., ] 9723.
Smaller cloud systems were often used as the experimental traits in order to
minimize the complexity of the dynamic framework Many of the experiments used a
combination of physical measurements and statistics to investigate the early links in
seeding-induced changes to the rainfall formation process. Although several of these
experiments showed that it was possible to alter the initial steps of the precipitation
formation process, it was more difficult to prove that these changes translated to
~9
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90
A PPEATDIXA
increased precipitation on the ground. The experimental units, due to their size, were
open not significant contributors to precipitation in the area. Results based on smaller
clouds might not be transferable to more dynamically vigorous cloud complexes.
Some of the initial steps in the chain of events of precipitation formation that
have been demonstrated in field measurements arid laboratory and modeling studies
include increased concentrations of ice crystals and the more rapid production of
precipitation particles in cumulus clouds. In tl~e High Plains Experiment (HIPLEX-1), a
detailed seeding hypothesis (Smith et al, 1984) guided a well-designed field program that
monitored each step in tl~e physical hypothesis. Although the experiment fai led to
demonstrate statistically all the hypothesized steps the problems could be traced to the
physical dataset (Cooper and Lawson, 1984~. This in itself' is a significant result that
shows the ability of physical measurements and studies to provide an understanding of
the underlying processes in each experiment. The results suggested that a snore limited
window of opportunity exists for precipitation enhancement than was thought previously.
Cotton and Pielke (1995) summarized this window of opportunity notion as being limited
to
J (I
· clouds that are relatively cold-based and continental;
clouds having top temperatures in the flange -10 °C to -25°C; and
· a ti~nescale confined to the availability of significant supercooled water before
depletion by entrainment and natural precipitation processes.
It was ~ ecognized in HJPLEX that small clouds would make very little
contribution to rainfall. The study of larger cloud complexes planned as part of HIPLEX
was not completed when the experiments were prematurely halted. However important
microphysical findings did emerge from the study of the smaller clouds.
The Israeli glaciogenic precipitation enhancement experiments, based on tl~e
static seeding concept as applied to winter cold-front and post-frontal cloud bands (with
embedded convection). initially provided strong evidence of increases in precipitation on
the ground (Gagin and Neumann, 19814. These experiments eventually became the
subject of a scientific debate initiated by Rangno and Hobbs (1995~. The validity of the
results was questioned and alternative reasons were presented for the results: a Type I
statistical error (lucky draw) in Israeli I and natural variability in rainfall in Israeli lI. The
likelihood of the Type I error eventually was shown to be equal to the statistical
significance level, and the mixed results of the Israeli II experiment were discussed
(Gabriel and Rosenfe]d 1990) and further explanation using physical-statisticaJ analyses
was given (Rosenfeld and Farbstein, ] 992; Rosenfeld and Nirel, 1996; Levi and
Rosenfeld, 19963. An important lesson to learn Mom this debate is to measure and record
all possible physical variables in the chain of events in precipitations Formation, in order to
support the results of any statistical experiment. Silverman (2001), in his preview of
glaciogenic seeding experiments, highlights some of the shortcomings of the statistical
design and execution of these experiments.
Static Seeding. TFinter O~o~-aphic Clouds
Experiments to seed wintertime orographic clouds for precipitation enhancement
(snowpack and rainfall augmentation) have highlighted the complex interaction between
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A PPEA7DIX A
91
the terrain and the wind-flow structure in determining regions of cloud liquid water
(CLW) and also in targeting and dispersing seeding material. This interaction explains
the difficultly experienced in showing cause and effect through seeding experiments over
flee Sierra Nevada (Desl~ler et al., 19903.
Changes in the concentrations of precipitating ice crystals, ice nuclei, and
precipitation rate have been observed after seeding in topographically forced regions
(Figure A. 1~. In some experiments seeding has produced strong evidence of precipitation
increases, including the Tasmanian experiments when cloud top temperatures were
between-10°C and 12°C in southwesterly airflow (Ryan and King, 1997~. Additionally,
results Tom the Bridger Range experiment showed an order of magnitude increase in ice
particle concentration contingent upon available supercooled liquid water leading to
increased precipitation. In such experiments the biggest challenges again, is to collect
sufficient physical data on the links in the chain of events to support statistical results.
The results frown flee CLIMAX I and CLIMAX II experiments (Grant and Mielke,
1967; Mielke et al., 1981), which were the most compelJi'~g evidence in the United States
for enhancing precipitation in wintertime orographic clouds, were also challenged by
Rangno and Hobbs (]987, 1993~. Although the Rangno and Hobbs reanalyses indicate a
possible increase in precipitation of about 10 percent, which is less than originally
reported, it still is a significant amount. Cotton and Nellie (1995) noted that the design,
implementation, and analysis ofthis experiment were clearly a learning process, not only
for meteorologists but also for statisticians. Many of the cloud systems in orographic
snowpack enhancement programs were not simply "blanket-type" orographic clouds, but
most often they were part of major winter cyclonic storms v`~;th continuously changing
wind-flow regimes and cloud structures, including both temporal- and spatial-changing
CLW regions (Rauber et al., 1986, Rauber and Grant, 1986~. Mesoscale numerical
models (Bruintjes et al., 1994; 1995', sophisticated radars, microwave radiometers, and
tracer studies could help substantially in identifying the spatial and temporal changes in
cloud structures and associated seeding potential (Klimowsky et al., 1998; Reinking et
al., 1999, 2000; Huggins, 19954. These advances are discussed in more detail in Chapter
4.
The chemical and physical properties of aerosols are important in determining ice
formation rates and efficiency. This fact led to the development of new, highly efficient
silver chloto-iodide ice nuclei (DeMott et al., 19831. These nuclei can be generated with a
soluble component to enhance the action of a fast condensation-freezing ice nucleation
mechanism (Fen" and Finnegan, 19893. In addition, new formulations of fast acting,
highly efficient ice nuclei from pyrotechnic devices continued in the 1990s. These new
ice-nucleating agents, effective at temperatures below ARC, represent substantial
improvements over prior ice nuclei generation capabilities arid offer possibilities for
engineering nuclei with specific desirable properties (Figure A.2~.
New methods for detecting small quantities of seeding agents in snowpack and
rainwater also have been recently demonstrated, along with the use of tracer and nuclei
ratio techniques to evaluate seeding effects (Warbu~4on et al., 19959. Warbu~ion also
showed that the dispersion of silver iodide in orographic winter clouds could have
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92
A PPE,\7DIXA
~ ~ .,,,,s. ~ ., . ,~, I,
i.. I.f
·.~.j
FIGURE A. 1 Observed concentrations of precipitating ice crystals, ice nuclei, and precipitation
rate during one leer of Agl seeding between 0945 and 1045, December 15, 1994 in Utal~.
SOURCE: Super arid Halroyd (1997).
s
=O 1.~3
~ 1.~0 .
>_ ~ ~ ~ ~ ~ 09 ' ~ At ~ ~0 (~0 ^} ~
~ · A A. :.. . A. . .~ ~~ (0 hominy By I
A. ~ ~ 1,~
~ ~ 10
~ 4005~15
POst-~0 ~ ~
.~ .~ ~ ~ -
Preps
1.~4 .
1.~1
PO:~-~0 ~ ~
(~% AgI)~ ~ ~
~~ ~~ ~~d
- ~ ~ As ~~*~
- -
Pre-~0
~ ~ (~% Ag{)
1.~9
r . ~ . _ , ._ .
~ ... :~ (company A)
·~,-r~ 5'~i
Severe - ding Ale) Supercooling (~)
FIGURE A.2 New Pyrotechnic Developments. Yield per gram of pyrotechnic (left panel) and
yield per gram of silver iodide (right panel) of ice formation by new pyrotechnic glaciogenic
seeding generators prior to (TB- l) and since about 1990. The new type of generators/flares ale
more efficient in producing ice nuclei on a compositional basis, require less silver iodide (as
AglO3 i, arid "react" mucl, faster in a water-saturated cloud. Results are from records of Colorado
State University isothermal cloud chamber facility.
OCR for page 93
A PPEiN:DIX A
93
actively participated in the nucleation of 15 percent to 30 percent of the ice crystals that
formed the snowpack.
Dync'''~ic Seeding
Project Storn~fury was among else earliest dynamic seeding experi~er~ts carried
out on oceanic cumulus clouds. It was pioneering in that it was based on a numerical
model of cumulus dynamics with a complete set of physical and dynamical equations.
The Florida Area Cumulus Experiments (FACE- 1 and FACE-2) also are typical
examples of the early dynamic seeding experiments (Woodley et al., 1982, 1983; Gagin
et al. 19864. The complexity of the chain of physical links leacli~g to precipitation,
together Title difficulties in observing these processes, led to the adoption of a statistical
approach as proof of concept. Initial encouraging results led to several other experiments
designed along similar lines. Experiments in Texas using radar-defined floating targets
showed increases in areas, duration, and rain volume but only slightly in cloud heights.
Although the radar-defined floating target analyses indicated increases in rain volume,
fixed ground-target analyses yielded no significant results To explain the ]ess-than-
expected increases in cloud tops, the dynamic seeding hypothesis was consequently
modified to include more details of mictophysical processes and to emphasize the rapid
conversion of supercooled liquid water (and especially large drops) into graupel in the
seeded plume (Rosenfeld and Woodley, 19933. The nature of the hypothesis is such that it
might be difficult to measure arid verify the different links, especially in the vigorous
cloud systems that are used as experimental units. However, the new cloud physics
instruments and remote-sensing devices that distinguish between water and ice
hydrometeors make documentation more feasible; furthermore, cloud models can be used
to test this conceptual mode].
Since 1980 operational and research glaciogenic seeding experiments for rainfall
enhancement based on the dynamic seeding concept have been conducted in Texas,
Cuba, South Africa, and Thailand. Exploratory analyses of these experiments have
indicated precipitation increases on the scale of individual clouds or cells with varying
levels of statistical support. The evidence for area-wide effects, although suggestive of
precipitation increases is weak and lacking in statistical support. No one has yet run a
definitive area-seeding experiment.
More recently (1994-1998) a randomized convective cloud-seeding experiment
was conducted on mixed-phase clouds ilk Thailand' based on the dynamic seeding
concept. The sample consisted of 62 units, and while the statistical resttlts indicated
increases in rainfall, the results were not statistically significant (Woodley et al., 1999)
The authors stated before commencement of the experiment that 125 units were needed
to provide confidence in the statistical results (i.e.' the sample size needed to be able with
sufficient power to detect a difference that is statistically significant, assuming that
approximately half the sample was treated and the other half was not treated). The
number of experimental units required in a randomized experiment to achieve confidence
in the statistical results is a factor that needs careful consideration by funding agencies, as
several projects have come to an end before this number has been reached (e.g ~ FACE-
2), leaving the results indeterminate.
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94
A PPE,\:DiX A
In recent years the importance of coalescence (and hence aerosols) on cloud
structure, evolution, arid rain production has been emphasized and highlighted in the
dynamic seeding conceptual mode] (RosenfUld and Woodley, 19934. It is known that
clouds In "continental" air masses wills hilly concentrations of cloud droplets (e.g., > 500
cm~3) can sometimes retain regions where water remains supercooled to the point of
hon~ogeneous nucleation (-3 8°C; see Rosenfeld and Woodley, 2000), with freezing
taking place abruptly once the colder temperatures are reached in agreement with
laboratory studies. Continental clouds take twice as long (i.e., they must reach colder
temperatures) to glaciate as maritime clouds having initial cloud droplet concentrations
between lOO cnl~3 and 300 cant, providing a potential "window" for glaciogenic seeding
intervention (Orville, 2001~.
These observations of extreme supercooling (Rosenfeld and Woodley' 2000)
seen to be in contrast with many other measurements which have found the initial ice
formation at temperatures as warm as -10 °C (Koenig 1963; and Bruintjes et al., 19874.
The Rosenfeld and Woodley measurements did not indicate whether ice coexisted with
the supercooled water in these cold regions, and it has been known for some time that
severe thunderstorms can contain supercooled water at cold temperatures. These results
highlight one of the major uncertainties in glaciogenic seeding: What is the origin of ice
in fresh updrafts? At a minimum the height and temperature of freezing depend on the
vigor and isolation of the updrafts and the nature and quantity of the ice-forming nuclei.
The CCN input into the clouds is another major determinant of ice in updrafts. Clouds
with CCN concentrations of ]00 cm~3 to 200 cm~3 readily develop raindrops through
coalescence that freeze at temperatures of-10°C or warmer? even in updraft regions. If
greater concentrations exist, however, coalescence will be suppressed and freezing will
take place at much colder temperatures. This effect has been simulated with an explicit
microphysics cloud model (Khain et al., 90011.
In conclusion' glaciogenic seeding has produced clear proof of microphysical
cleanses to simple cloud systems, Title indications based on statistical results that
precipitation has been increased in some experiments. However, against the background
of more than half a century of experimentation, many questions still remain and progress
has been frustratingly slow due to limitations in understanding of the complex physical
processes involved, insufficient design of some experiments? and at times, political,
scientific, and funding pressures. There are still a number of issues that need to be
addressed, including
. the transferability of results from simple cloud systems to larger more complex
storm systems float contribute significantly to area-wide precipitation;
· tl~e link between the formation of ice in strong updrafts in regions of high
supercooled liquid water and the development of larger graupe] particles that could
deplete the liquid water;
the links between recently observed high concentrations of ice crystals'
additional ice crystals produced by seeding, and their initial growth to more precipitation
.
on the ground;
. the interactions between cloud dynamics and microphysics and how they may
change due to seeding; and
.
the measurement limitations of conventional radar.
OCR for page 95
A PPEiN~DIX A
I-lygroscopic Seeding Experiments
95
Since its inception the term "hydroscopic seeding" has taken on slightly different
meanings depending on flee experimental design, type of seeding material used? and the
type of cloud subject to experimentation. In all instances the ultimate goal has been to
er~hance rainfall by somehow promoting the coalescence process. Tl~e direct introduction
of approp~ lately sized salt particles or droplets that can act as artificial] raindrop embryos,
using either water sprays, diluted saline solutions' or ground salts, are the most common
hydroscopic seeding techniques that have been used (Biswas and Dennis' 1971; Czys and
Bruintjes, 1994; Murty et al., 20001. The primate objective of introducing artificial
raindrop embryos (such as salt particles larger than 10 lam dry diameter) is to short-
circuit the action of the CCN population in determining the initial character of the cloud
droplet population, and thus jump-start the coalescence process. This concept has been
Vised in programs in flee United States and oilier countries (Biswas and Dennis, 1971;
Bowed 1952; Cotton, 1982), and is still widely used in Southeast Asian countries. In
fact, the India and l loci experiments reported statistically significant (oc=0.05) increases
in rain (Murty et al., 2000; Silverman and Sukarujanasat~ 2000~. Despite this wide use the
results ale inconclusive due to the lack of physical understanding of the statistical results.
Observations and modeling results have lent some support that under certain conditions
with an optimal seed-drop (artificial embryos see Rokicki and Young, 1978; Tzivion et
al., 1994) size spectrum, precipitation could be enhanced in some clouds.
A recent development related to mixed-phase convective clouds is the use of
hydroscopic flares. The flares produce small (mean dry diameter 0.5 Him to 1 Am)
hydroscopic particles with a fairly long tail in the distribution toward larger sizes. The
flares are used for seeding in the updraft pleas below the bases of convective storms. Due
to size and chemical characteristics, the hydroscopic particles have an advantages
compared to naturally occurring particles (especially continental CCN), in competing for
available water vapor to activate cloud droplets, broadening the cloud droplet size
distribution, and initiate condensation growths thereby improving the efficiency of the
r ainfall formation process (Masher et al., 1997; WOO, 2000~.
In both South Africa (Masher et al., 1997) and Mexico (WMO, 20003'
hydroscopic flares were applied to mixed-phase convective cloud systems in physical-
statist~cal experiments (i.e., statistical randomized seeding experiments with concurrent
physical measurements). Aircraft microphysical measurements were made to verify some
of the processes involved. Radar-measured 30 dBZ volumes produced by the convective
complexes were tracked by automated software and various storm and track properties
were calculated. These two sets of experiments produced remarkably similar results in
terms of the difference in radar-estimated rainfall between the seeded and non-seeded
groups (see Figure 2.3~. The South African data have been reevaluated independently by
Bigg (1997) and Silverman (2000), and both concluded that there is statistically
significant evidence of an increase in radat-estimated rainfall from seeded convective
cloud systems. For instance, Silverman's (2000) re-evaluation showed an increase in rain
mass in the 30-60 minutes after seeding, significant at the 96 percent level (oc-0.04) or
higher.
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A PPE,NTDIXA
The individual storms selected for the experiment almost without exceptions,
extended well above the freezing level. In the exploratory analyses done on the South
African data (Masher et al., 1997), marked differences were found in stone properties
above 6 km. Tl~e 6 kale level generally corresponds to flee -5°C to -10°C level and
therefore points to probable ice-phase processes being part of the apparent seeding effect
Some indication of how the microphysical changes of broadening, the droplet spectrum
can be brought about by hydroscopic flare particles as well as supporting measurements
are given by Cooper et al. (19971. Although these effects on the ice processes are not well
understood and need further research, the following constitute continued progress:
~ The natural contrast of high concentrations of ice particles in maritime clouds bill
which collision and coalescence are active) that extend above the freezing level (Cotton,
1972; Koenig, 1963; Koenig arid Murray, 1976; Scott and Hobbs' 1977~? compared to the
relatively low concentrations of ice particles found in continental clouds (in wl~cl~
coalescence is not active);
. The freezing temperature that increases with an increase in droplet size due to the
higher probability that larger droplets will contain or come in contact with ice nuclei and
the associated ripening characteristics (Johnson, 19874; and
. The various ice-multiplication processes, including mechanical fracturing of
fragile ice crystals during melting and evaporation, ice splinter formation during riming
due to the pressure break-up of accreted drops, which are dependent on the presence of
relatively large cloud droplets (> 24 ~m) (Hallet and Mossop, 19741.
~ ~ ~ .
In the South African and Mexican hydroscopic hare experiments on mixed-phase
clouds' the Thailand experiment using larger hydroscopic particles on exclusively warm
clouds (Silverman and Sukarnjanasat, 2000), and the glacio.~enic seeding experiment in
~ O ~
Thailand (Woodley et al.' 2003a~b), a delayed seeding response in radal^-derived storm
properties was observed. The South African and Mexican results were analyzed for the
first hour after seeding, and the seeding effect was evident 20~60 minutes after seeding
based on the statistical results. In the Thai hydroscopic and glaciogenic seeding
experiments the seeding effects were evident only after a few hours. This result has been
explained through some seeding-induced dynamic mechanism (Big", 1997; Mather et al.,
1997; Silverman 2000~. The atmospheric kinematic structure and stability in the three
experimental areas differs substantially, complicating the understanding of these apparent
dynamic responses. Although some possible explanations have been suggested (Big=?
1997), this is an issue that demands further investigation and proof.
As a result of the outcome of the hydroscopic seeding experiments' the WMO
Executive Council in May 1999 convened a Working Group on Physics and Chemistry of
Clouds and Weather Modification to review those activities, and subsequently (in
collaboration with the National Center for Atmospheric Research in the United States and
the State of Durango in Mexico) the council organized a workshop Ott hydroscopic
seeding, held in Mazatlan, Mexico, in December l 999 (WMO, 2000~.
The Mazatlan workshop £ eviewed the three recent randomized precipitation
projects (South Africa, Mexico, and Thailand), which had the following common
elements: (1) seeding with hydroscopic particles; (2) evaluation using a time-resolved
estimate of storm rainfall based on radar measurements ire conjunction with an objective
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97
software package for tracking individual storms; (3) statistical analyses indicating
increases in radar-estimated rainfall; and (4) the necessity to invoke seedir~-induced
dynamic effects to explain the results. While the randomized seeding results were viewed
as exciting, the workshop participants concluded that the chain of physical events is not
well understood. It is generally accepted that this 'second pillar" of scientific
understanding is needed to reinforce the statistical results before such results can be fully
accepted. The workshop participants also recommended that a major cooperative field
experitnent employing modern instrumentation be planned and carried out in flee near
fixture (WMO, 20004.
The workshop concluded:
The recent l~ygroscopic seeding experiments, if validated, lead beyond the
classical result in cloud physics that links cloud condensation nuclei and droplet
spectra at cloud base to the efficiency of rain (for example, the probability that a
cloud of a given depth will produce rain). Rather, these experiments suggest that
CCN affect the total rainfall from a cloud, and apparently also the longevity of
the cloud. This tesult would have important practical implications not only for
water resource needs but also for quantitative precipitation forecasting and for
global change issues (for instance, interactions among regional temperature
changes, changes in natural CCN concentrations, and precipitation patterns)
(WMO, 2000~.
As discussed in Chapter 2, recent satellite measurements have indicated that
plumes of smoke from biomass burning and other sources inhibit coalescence and rain
formation, and that salt dust (Rudich et al., 2002) and sea spray (Rosenfeld et al. 2002)
enhance coalescence and precipitation in clouds in which the precipitation was otherwise
suppressed due to the air pollution. This information together with the results from the
hydroscopic seeding experiments, suggests an intriguing idea that hydroscopic seeding
could be used to override damaging, inadvertent seeding effects that inhibit rainfall, xvith
revote beneficial, deliberate seeding effects that enhance rainfall. This potential should be
explored further.
CURRENT STATUS
During the last 10 years there has been thorough scrutiny and evaluation of
projects involving glaciogenic seeding experiments. Although there are indications that
seeding can increase precipitation based on the statistical results, a number of recent
studies have posed new questions about these experiments. As a result, skepticism
remains as to whether this method provides a cost-effective means for increasing
precipitation for water resources. Commons weaknesses of nearly all glacioge~ic seeding
experiments are the incomplete documentation of the physical chain of events and cause
and effect relationships, and the incomplete understanding of the physical processes
thought to be operating to increase the rainfall and to explain the oftentimes positive
statistical results. An exception is orographic snowpack enhancement, for which many of
the physical processes and causes and effects are better understood.
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A PPEAiDIXA
Although the dynamic seeding conceptual model is plausible and provides a
logical chain of events to enhance precipitations, it is a very complex model, and many
links in the chain are difficult to measure. Especially elusive has been the effect of
seeding on downdrafts and the role this may play in communicating cloud-scale seeding
effects to an area-wide effect. Focused observational experiments modeling studies, and
modern statistical evaluations (Appendix B) are needed to validate arid support Ellis
hypothesis. Although rainfall increases hom individual clouds on a limited scale have
been documented, significant evidence of effects on areal rainfall patterns leas not. It is
these effects—not the area av-e~ age or point measurements of rainfall that are important.
Over mountainous terrain the timely identification of regions of supercooled
liquid water and else efficient targeting and dispersing of seeding material remain difficult
problems. These clouds are part of major winter cyc]onic storms? which often have
continuously changing wind flow regimes and cloud structures. Major uncertainties
include the identification of the light cloud at the right time, the response time for
delivering seeding material, flee coverage on release, and the potential for volume filling.
Evidence Tom plume tracking and measurement of seeding chemicals in fallen snow
shows that plumes of seeding material often do not fill and catalyze the intended cloud
volume (Reynolds, ]988; Stone and Warburton, 1989) Focused numerical modeling
studies on flee questions raised by targeting supercooled or liquid water in mountainous
terrain can advance the understanding of seeding effects (Orville, 1996~. Simultaneous
use of the most advanced observing tools (described in Chapter 4) and improved
statistical evaluation techniques will improve the success of such studies significantly.
Additional weaknesses in some experiments have been problems with the
seeding devices and poor seeding execution. Grouping of all the days during the analysis
phase and including some classes of clouds that should not be responsive to seeding may
dilute the apparent effect of seeding. In addition the large natural and experimental
variability inherent in seeding convective clouds has made detection of a seeding signal
very difficult. Finally, cuts in funding often have resulted in project termination well
before any definitive result could reasonably have been expected.
To fully evaluate the utility of glaciogenic cloud-seedi~g agents requires a more
complete understanding of natural ice formation processes. Measurements are needed of
the origin of natural ice nuclei, what their composition is, how they act in clouds, and
how they are distributed in the atmosphere. The impacts of changes and variability in
engineered and natural aerosols on ice formation must also be investigated, so that their
inspects on cloud modification efforts can lee understood and even anticipated.
Pitfalls that have affected experiments in the past include errors in the statistical
design and conceptual model, changes in seeding strategy or seeding material,
inappropriate statistical or evaluation methods, and inadequate tools to conduct the
experiment. Statistical experimental design, including the sample size and length of the
experiment' must be appropriate in order to detect the statistical significance of changes
that occur in response to seeding (Fletcher and Steffens? 1996; Gabriel, 1999, 2000;
Mielke et al., 1984; Ryan and King, 1997; Smith et al., 1984). However, appropriate
experiments are difficult to design according to the "classical,' perspective, because there
can never be a true control; the atmosphere is dynamic and constantly changing. A
~ 1 Cat ~ 1
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A PPEA~DJX A
99
detailed review of the evaluation of weather modification experiments and curt ent
statistical methods is given in Chapter 3 and Appendix B.
While the classical (i.e., large particle salt powders) warns cloud-seeding
technique is still widely used in countries in Southeast Asia statistical experiments have
shown mixed results. Observations and No deli results have lent some support that
under certain conditions with an optimal seed drop-size spectrum (Rokicki and Young,
1978; Tzivion et al., 19944' precipitations could be enhanced in some clouds.
Disadvantages of this approach ale that large quantities of salt are speeded and dispersion
of the salt into flee cloud inflow is difficult to accomplish. In addition, the growth rates of
the particles to raindrops must match the updraft profile or their growth will be inefficient
(KIazura and Todd, 1978; Young, 19963. In a modeling study Farley and Chen il975)
-fluted that salt seeding only produced a few large drops without a significant effect on the
precipitation process unless drop breakup acted to ir~duce a chain reaction that enhanced
the effects of seeding While some positive effects have been reported (Biswas and
Dennis, 1971; Murty et al.' 2000; Silverman and Sukar~janasat, 2000), seeding with
hydroscopic material has usually appeared less attractive than seeding with ice nuclei due
to the lack of physical understanding.
Although promising statistical results have been obtained with hydroscopic
seeding some fundamental questions regarding the physical processes need to be
answered in order to provide a sound scientific basis for this technology. The physical
processes responsible for the apparent successes in South Africa and Mexico using small
hydroscopic particles are not fully understood.
One fundamental impediment is the diffusion and transport of seeding material
throughout the cloud. Well et al. (1993) showed that it takes more than 10 minutes for a
plume released in a cloud to spread over distances of several kilometers and to fill an
updraft region of a single cell. It has been hypothesized that the initial spreading of
seeding effects through a cloud occurs via the formation of drizzle drops A possible
solution to this problem is to seed only the strongest updrafts, which are expected to rise
to near cloud top, where any drizzle-size drops produced might spread and be carried
downward in the descending flow near the cloud edge. According to Blyth et al. ( 1988)
such material would spread throughout the cloud and might affect large regions of the
original turret and perhaps other turrets. Such a circulation is supported by the
observations of Stith et al. ( 1986, 1990' ] 996~.
A modeling study by Cooper et al. (1997) indicated that the concentrations of
drizzle drops produced by seeding can vary by several orders of magnitude, depending on
the size spectra of seed particles. Reisin et al. (1996~' Yin et al (2000a,b), and Cato et al.
(2002) found similar results and suggested that for seeding to have an optimum effect
(producing sufficient concentrations of drizzle-size drops), mean seed particle radii
between 0.5 Em and 6 rim are needed. These modeling studies indicate that the role of
battleground CON and giant CCN is crucial for determining the effectiveness of the
seeded particles, because the seeded nuclei compete with background aerosols for the
available water vapor. The results from these model calculations should be interpreted
with considerable caution, because they oversimplify the precipitation formation process
and the complex dynamics of convective clouds.
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Ire addition, the suggested dynamic effects need to be explored further, and the
modeling studies need to be validated by observations. In summary, while some recent
experiments provided good statistical results, there are nonetheless many uncertainties
with respect to the physical interpretation of the statistical results that remain to be
addressed. Some of the accost critical of fleece uncertainties are summarized in Box 2.2.
A more detailed understanding of the clean of events of ~~icrophysical and
dynamical processes in clouds and their responses to hydroscopic and glaciogenic
seeding is needed. The initial development of large drops ire a cloud, the origin of ice in
clouds, and liquid- and ice-phase interactions in the development of precipitation are not
well understood.
A coupled cloud-dyr~amical response is apparent in Amy hydroscopic and
glaciogenic cloud-seeding experiments; for instance' many experiments have indicated
increases in rainfall beyond 30 minutes after treatment, which may be indicative of
dynamic responses that were not anticipated in the original conceptual models. These
interactions are not well understood.
There are uncertainties related to the use of radar alone to estimate rainfall. It is
possible that some statistical results using radar-derived precipitation estimates might be
due to seeding-induced drop size changes that affect the radar observations (Yin et al.,
1998) Additional field measurements of r airdrop spectra are needed to address this issue
Due to inherent assumptions of relating reflectivity from conventional weather radar to
meteorological parameters there are limitations on discriminating between the liquid
water arid ice phases arid changes in the concentrations and sizes of precipitation
particles. These are exactly the characteristics that are assumed to change by cloud
seeding in mixed-phase clouds. Furthermore, conventional radars do not provide
information on the complex motions in cloud systems, how these motions impact the
microphysics and vice versa, or how these motion fields may be affected by seeding.
New radar technologies and techniques (discussed in Chapter 4) and new statistical
evaluation techniques (discussed in Appendix B) may help address these issues.
Most reported cloud-seeding results have come from single-cloud (or storm)
experiments, which do not necessarily address the question of flow area-wide
precipitation may be affected by seeding. In addition, the ~ esults f) om a seeding
experiment in one region cannot automatically be transferred to othet geographic areas,
since large-scale weather systems, topography, background aerosols, and the
thermodynamic and wind profiles will affect the feasibility arid impact of seeding in any
particular location. To increase the likelihood of successful transfer ability all
environmental conditions and methodology of seeding must be replicated (Cotton and
Pielke, 19954~ a skill for greater than currently available Such issues must be examined
for all applications of weather modification.
Related uncertainties pertain to the issue of"extra-area" effects, that is, whether
seeding can affect the weather beyond the targeted ten~poral or spatial range. The
persistent effects of cloud seeding claimed by Bigg (I 995) should be carefully assessed,
as should the statistical results from experiments in Thailand (S;lverrman and
OCR for page 101
A PPEIN:DIX A
101
Sukarnjanasat, 2000; Woodley et al., 2003b) and Israel (Brief et al., 1973), which claim
effects beyond a few hours. Some argue that increasing precipitation in one region could
r educe precipitation downwind (by ``stealing" the atmospheric water vapor), or
conversely, could enhance precipitation downwind (by increasing evaporation and
transpiration and thus providing more moisture for clouds). Such claims however,
currently belong to the realm of speculations as no quantitative studies of this issue have
been conducted. This is a challenging issue to address, due to the current limitations of
quantitative precipitation forecasting
The need to predict what would have happened had there been no weather
modification (which is especially important in the context of attempts to modify
hazardous weather) places an enormous burden on prediction. Predictive numerical
models are required to accurately assess what would have occurred in the absence of any
intervention, in order to assess both the magnitude and the potential consequences of the
change. However, model development and physical understanding are interdependent'
thus advances in both are slow and iterative.
The progress in these areas, together with new observational, laboratory, and
modeling tools (discussed in Chapter 4), substantially enhances our capabilities to
address flue issue of weather recodification with renewed vigor. The biggest challenge
facing the community is to bring more modern technology to bear in addressing the
outstanding uncertainties discussed in this chapter.
Given the number of operational programs worldwide there is clearly a perceived
need for deliberate weather modification to enhance precipitation and to mitigate some
forms of sever e weather. At this time scientific knowledge badly lags the perceived need.
Without a systematic research effort organized to address the most pressing scientific
uncertainties, this gap is certain to widen. The water resources and land-use sectors
should be integral parts of such a research effort. Transforming cloud-seeding
information and results into a geographical information system format could, for
example, facilitate cooperation between meteorological, water resources, and land-use
specialists. Viable precipitation enhancement techniques ~ emain an attractive and
economical prospect, and they deserve focused attention and long-term support. The
development of a stable funding environment to develop a new generation of scientists
worldling in this field is needed.
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. .
. . . .
Representative terms from entire chapter:
seeding experiments
