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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 -25C; 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-10C and 12C 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.

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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 8C; 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-10C 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.

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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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96 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 -5C to -10C 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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A PPE;N'DIX A 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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98 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 effectsnot 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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loo A PPE,\7DiX A 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

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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. REFERENCES Battan, L. J., and A. R. Kassande~. 1967. Summary of randomized cloud seeding project in Arizona. In Proceedings of the Fifth Berkeley Symposium on 14~Iathematical Statistics and Probability, pp. 29-42. Berkeley: University of California Press. Bigg' E. K. 1995. Tests for persistent effects of cloud seeding in a recent Australian experiment. J. Appl. Mefeorol. ~4~11~:2406-11. Bigg, E. K. 1997. An independent evaluation of a South African hydroscopic cloud seeding experiment, 1 991 - I 995. At~nos. Res. 43:1 1 1 -27.

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