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3
Evaluation Requirements for
Weather Modification
Over the years the overriding critical issue for nearly all weather modification
research and operational activities has been the need for evaluation and validation of the
results. This Committee agrees with flee views stated in many earlier assessments that
objectivity' repeatability, and predictability are primary requirements in weather
modification research, as well as independent confirmation with strong physical and
statistical evidence. In recent years these has beets some improvement in the evaluation
and validation of cloud-seeding activities (e.g., more emphasis on randomization and
double-blind studies), but these evaluation efforts have not been sufficient to make a clear
case for supporting standard methodologies or for achieving predictable results. The
challenge for the scientific community is to develop acceptable evaluation criteria to
ensure float future research and operational programs build a solid scientific foundation
for fi~rthe~ advances. This chapter examines issues related to designing and evaluating
weather modification experiments and commercial seeding operations.
PHYSICAL EVALUATION
The interpretation of observations in the light of established theory and tile
development of new theory based on laboratory experiments and observations in the
atmosphere are sometimes called physical evaluation. A complete physical-dynamical
numerical model of a cloud system (with and without seeding) would be the ideal version
of a physical evaluation If meteorologists had the skill to make perfect forecasts, they
could estimate seeding effects by simply comparing test results with predictions. But
such forecasting skills would requite a complete physical-dynamical model of the
relevant cloud systems, as well as a measurement system capable of establishing initial
conditions for the model. Neither of these exists nor are they likely to exist in the
foreseeable future. In considering the role of weather modification in the field of
atmospheric science, it is important to emphasize that many of the uncertainties limiting
an understanding of the physics and dynamics of seeded clouds are the same as those that
limit quantitative precipitation forecasting in weather forecast models and cloud
pet ameterizations in climate models.
39
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4()
CRITICA L iL8~:Z.JES fair TT'EA TI-lER AIODIFICA TION REtSF-ARCI!
An example of a physical evaluation can be found in early weather modification
experiments that involved dropping dry-ice pellets into stratus clouds and observing the
transition of supercooled drops into masses of ice crystals in the time and locations
predicted by laboratory studies and theory. Because the stratus was unifUrn~ over large
areas and stable over long time periods (relative to the time required far conducting the
experiment)' and because the result could be replicated as often as desired, there was no
need for elaborate statistical studies to establish a cause-and-effect relationship between
tile seeding and the subsequent develo~n~ent of ice crystals.
This example, however, is deceptively simple. Most cloud-seeding ex,veri~nents
have not resulted in responses as clear cut and repeatable as that of dry-ice seeding of
supercooled stratus Often the cloud systems of interest are lily variable in space and
time and this variability is poorly quantified. Convective cloud regions suitable for
seeding }save unknown lifetimes and allay be interspersed svith regions where seeding
would be ineffective. Thus far we are unable to trace the physical effects from the point
of seeding to the end product of rain on the ground. Even our ability to measure the
amount of rain reaching the surface leaves much to be desired, although recent advances
in radar technology (described in Chapter 4) should lead to better measurement of
rainfall. Due to such limitations, cloud scientists have had loo alternative but to turn to
statistical evaluations in their efforts to verify seeding effects.
STATISTICAL EVALUATION
To have reasonable confidence in the results of seeding experiments they must be
carefully designed, conducted, and analyzed with the best techniques available. The goal
is to minimize uncertainties resulting from the large variability in natural weather
systems, from our incomplete knowledge of the physical processes involved, frown our
limited ability to measure the relevant meteorological variables and to target seeding
agents, and from our inability to replicate experiments (in the strictest sense of the word).
Assessments of seeding effects most often consist of comparisons of the amount
of precipitation (e.g., rain) measured in a target area with that from a control area. Many
of these Compaq isons, especially in the early days of seeding, did not involve
randomization. The target and control areas often were the same fixed geographical area,
and comparisons were between measurements made during the seeding period and those
from a period without seeding. Alternatively? the control area might be a geographically
fixed area adjacent to (and meteorologically similar to) the target area. In this case,
comparisons are made between measurements from the two areas during the same time
periods. In either ofthese designs the comparisons are usually discounted because there is
no way to allow for biases arising from temporal or spatial trends that may have been
present during the trial period. A more statistically robust design, known as a cross-ove~,
uses two similar fixed areas. During each test case ogle area is selected for treatment
through a random process while the other serves as the control.
it has long been recognized that experimental proof of cause-and-eff~ct
relationships (as opposed to chance occurrence) requires randomization and replication
(Fisher, 1958), especially when the test pool is highly variable as in the case of weather
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EFTA L {JA TlOiV REQUIREI\lE.YTS FOR T7F'F,A TI-lER NIODIFICA TlON, 4 1
systems. A number of randomized seeding experiments have been designed and
conducted with the aim of confirming a particular seeding effect. These experiments have
provided a large fraction of our scientific data on clouds and storms, but most did not
provide evidence sufficient to reject the null hypothesis of no seeding effect. A
conclusion commonly reached in these experiments was that "there were indications of
seeding effects based ore physical measurements' but the data were not sufficient to reach
statistical conclusions." Generally the suggested physical evidence tor seeding effects
was deduced frown after-the-fact examination of the data. Front the many kinds of
measurements obtained certain ones may be selected because they appear to be associated
with a useful seeding effect, perhaps in a particular partition of flee data. The scientist
then postulates a mechanism whereby the supposed effect might be linked to the
treatment. Regrettably those postulates have not been verified by further experimentation.
Statisticians working with meteorologists have developed a range of design and
analysis techniques for assessing seeding experiments. In addition to randomization and
replication, a well-designed weather modification experiment Clay include pre-screening
or blocking to reduce the variance ire the test grOUp7 use of cova~iates, alternating target
and control areas (c~oss-over design), and re-randomization as a means of coping with
internal variance and small sample sizes Classical hypothesis testing often is replaced by
a comprel~er~sive data analysis in which all of flee measured variables are brought to bear
on the question of seeding effects (Gabriel t 979; Flueck, 19711. Another relatively new
statistical method that may }provide even better evaluation capabilities is the Bayesian
technique, which can explicitly account for sources of uncertainty and complicated
spatial and temporal dependencies (Appendix B). This technique could have maj or
impacts on weather modi fication research if uti 1 ized.
1 ' 1 ~
. . . . ~ . . , . .. ~ . . . .
Because of the significant natul al variability in cloud systems, seeding
experiments must acquire large numbers of experimental units if a relatively small
seeding effect is to be distinguished from chance variations. This has meant long and
expensive experiments. Protracted experiments are more vulnerable to secular changes in
environmental factors (e.g., weather, land use, background aerosols), many of which can
be handled by proper randomization (at least in principled. For instance, it would have
. . ~ . . ~ .
. , , . ~ ~ . . ,~, ~ , - '. r.
taken over 50 years to carry out a full statistical evaluation of the effects of seeding on
hurricanes using ] 970s technology (Simpson et al., 2002~.
The ACWC introduced the concept of exploratory arid confirmatory experiments
to differentiate between searching for possible seeding effects and formal testing of a
postulated effect. Statistics can be used in an exploratory mariner to guide Understanding
of the important physical processes in a conceptual model For instance, in recent
hydroscopic seeding experiments (i.e.? the South African, Mexican, and Thailand
experiments described in Appendix A) statistical analyses indicated increases in rainfall,
but they appeared later in time than anticipated and did not conform to the original
hypothesis. Dynamical effects, which were riot included in the original hypotheses, were
invoked to explain the results. The statistical analyses thus led to the development of new
hypotheses to explain the experimental results.
Some may argue that a single test variable is necessary to guard against
multiplicity and to provide an unambiguous proof of concept. However' data from cloud-
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CRITICA L I~SS(JES I.\ THEA TI-IER AIODIFI(.-A TION RE,SEARCl-I
seeding experiments are highly variable, and this reduces the power of a single test to
detect differences. To fully consider and evaluate the myriad of variables in weather
modification experiments, multivariatc statistical process models that exhibit spatial and
temporal dependence are much better suited (Appendix B).
Statistics can be used not only as a tool to test proof of concept, but also as a tool
~~ discovery (a mathematical "magnifying glass?'). The advances in statistical sciences
described in Appendix B leave not yet been fully applied in weather modification
research. Application of these methods' together with the advances in measurement
technology and modeling, promises improvements both ir1 verification and in our
physical understanding of the processes involved. Appendix B provides an in-depth
scientific discussion of the current methods available in statistical science with direct
application to weather modification. In the sections that follow a mole genera] discussion
of evaluation requirements in weather modification is presented.
MEASUREMENT UNCERTAINTIES
Even though the classical methodologies of testing cloud seeding are well
established, several kinds of difficulties are encountered in practice. Tl~e objective of
assessing the results of a cloud-seeding experiment is to establish whether the test variate,
such as the total lain in a target area under treatment, is different than it would have been
with no treatment. Obviously, one must then be able to measure the test variate with
sufficient accuracy to separate the effects of treatment f om natural variability. This has
been a major problem in cloud-seeding experimentation.
For instance, experiments aimed at increasing rainfall typically have used
networks of surface-~ain gauges as their measurement system Rain gauges give a fairly
accurate measurement of rain at the point of the gauge, but rain is highly variable in
space and time, especially in convective weather situations. The frequency distribution of
storm rainfall amounts is highly skewed, with a large number of small events interspersed
with a small number of large events that account for most of the total lain. Title the
density of rain gauges normally attainable, and integration over periods of hours? area-
average rain amounts have large errors, especially in convective situations. Radar is being
used more frequently for measuring rain, with the advantage of much better spatial
coverage and temporal resolution. But this introduces another variable, namely, the
relation between the measured tadar parameter and rainfall at the surface, which depends
on the drop-size distribution, which may be affected by seeding. Other direct and indirect
measures that have been used for assessing seeding trials, such as hail-fall energy and
crop damage estimates, also introduce additional layers of variability that must be
accounted for.
UNCERTAINTIES IN DEFINING AND TRACKING THE TARGET
In many cloud-seeding experiments the experimental units are elusive, hard to
define, and difficult to follow in time. In fact, to see a convective cloud as a single entity
is an illusion Clouds are transitory, always evolving and mixing internally and with their
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E f A L (JA TION REQlfIREtt1ENTS FOR T7FrEA Tl-TER N1ODIFICA TiON
43
environment. These basic properties of clouds make it difficult to keep track of seeded
units and to replicate the treatment in successive trials. At the same time? however, this
inherent mixing within the atmosphere plays an essential role in most seeding
experiments. In the immediate vicinity of the release point from seeding devices the
concentration of seeding materials is much too high Or effective cloud treatment.
Operators depend on atmospheric Nixing to dilute the seeding material before it reaches
the target area. Further mixing then reduces the concentration of seeding materials and
Nay reduce it to the level where it becomes ineffective.
No two clouds are identical, and clouds are riot independent of one another. A
limited number of experiments have found that traces materials released into the sub-
cloud updraft of a developing convective cloud were subsequently found in the rain
coming from neighboring clouds, thus suggesting some degree of interaction. The
unmount of cloud interaction probably decreases with separation in space and tithe. The
degree of dependence between different clouds on the same day, or clouds in the same
area and air mass on different days, is not measurable and thus hard to allow for in
assessing seeding trials. This issue is often simply ignored in many seeding studies.
However, recent advances in this area integrating several observational tools could help
to address these issues (Mueller et al., 2003~.
A variety of tracking methods and software packages are used to evaluate the
results of seeding activities For example, the South African and Mexican hyg~oscopic
seeding experiments (WMO, 2000) used the sto~ms-based Thunderstorm Identification
Tracking Analysis and Nowcasting (TITAN) tracking software to evaluate their radar-
based r esults. the hygt oscopic seeding experiments in Thailand (Silverman and
Sukarnjanaset, 2000) used a va~iable-radius floating target that moved with the mean
radar echo motion. The recent glaciogenic experiments in Texas (Rosenfeld and
Woodley, 1993) arid Thailand (Woodley et al, 2003) used a hierarchy of radat-tracked
cells imbedded in fed-radius floating targets that are moved with the mean-cell motion.
, , ,
UNCERTAINTIES lN REACHING THE TARGET
When ice-forn~ing agents are released directly into the top of a supercooled
stratus cloud, there is little question whether it reaches ~ susceptible region of cloud.
When the seeding agent is released directly into the updraft under a convective cloud it
will become part of the updraft and presumably will be carried to a level where it can be
effective.
In the case of atea-wide sub-cloud seeding and orographic seeding, the agent
usually is released upwind of the target. Whether it reaches the intended target, and if so
in what amounts will depend on the winds and turbulence between the release point and
the target. In some contexts the means for- measuring and forecasting these winds in real-
time is very limited and thus is another source of uncertainty. Some seeding particles
from go ound-based generators could be scavenged by snow and ice and therefore
diminish the effects of seeding (Warbu~ton et al., 19951. For all of these reasons the
targeting and mixing of the seeding material through a cloud remains lushly uncertain.
However, with new high-resolution mesoscale numerical models and remote sensors,
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CR JTI(',4 L [~SSlJES IN [YEA TI -TER A JODIFICA TION REt5~-A RCI-J
new opportunities exist to address these issues, especially in winter orographic situations
Becloud and cloud-top seeding introduces similar uncertainties but could potentially be
addressed with new modeling and observational tools. As discussed in Chapter 4 the use
of cliff fibers or gaseous tracers may be a particularly good strategy for tracking the
dispersal of seeding material and the resulting cloud enacts.
ASSESSING THE AREA AFFECTED
The areas affected by cloud seeding remain an open questions. In after-the-fact
analyses several rain enhancement projects have reported evidence for physical effects
outside the area or timing originally designated as the target, or beyond the time interval
when seeding effects were anticipated. For example, in recent large particle hydroscopic
seeding trials involving war-base convective clouds in Thailand and Texas, increases in
rain were reported 3 to 12 hours after seeding was conducted' well beyond the time at
which direct effects of seeding were expected and possibly outside the target area. In
Project Whitetop the seeding appears to have decreased rain in the area immediately
downwind of the seeding release line. This was followed by apparent rainfall increases
well downwind in space and time. Does this mean that the scientists misjudged where
seeding materials were actually reaching receptive cloud conditions or does it mean that
the primary effects of seeding were fallowed by secondary effects well beyond the
original target? Such secondary effects could occur, for instance, if seeding materials
become entrained in a downdraft and then are carried outward into the updraft of other
clouds. In the case of the hydroscopic seeding experiments the postulated dynamic effects
due to micronhYsical and dynamical interactions in the cloud and sub-cloud region and
_O
— ~ ~
. ~ ~ ,
.,, ,, . , it, ,, . , .. . . . ~ .. . .
with the environment could result In longet-Jived or progeny clouds. Another related
uncertainty in seeding convective systems is whether a positive effect on some individual
clouds (or cloud complexes) will aggregate to result in increased area rainfall.
An associated question addressed in Appendix A and Box l .1 is that of throbbing
Peter to pay Paul." Debates about the effects of seeding beyond the target area point to
the fact that weather modification can be viewed as more than just a means to increase
local precipitation. Rather, it can be viewed as a means to alter natural hydrological
cycles by increasing the number of times that atmospheric water is recycled at the Earth7s
surface. As mole is learned about the globe] water balance and as new tools enable the
cloud scientist to better understand clouds and their response to seeding, the question of
extended area affects likely will become better defined and understood.
Representative terms from entire chapter:
seeding experiments
