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<br />Printed January 30, 1990 <br /> <br />based mainly on statistical analyses of precipitation data collected on randomized seeding <br />experiments. Differences in the responses of different types of clouds to seeding were noted in the <br />analyses, so meteorologists coined the word "seed able" to denote clouds which can be modified to <br />yield additional precipitation. <br /> <br />Randomized tests of hail suppression have been less numerous and less promising than randomized <br />tests of precipitation augmentation, and review panels have been cautious about the feasibility of <br />hail suppression. Here, too, differences in the responses of different types of clouds to seeding have <br />been noted. The AMS statement refers to "the possibility of increasing hail in some circumstances <br />and decreasing it in others." <br /> <br />In view of the economic importance of artificial means of augmenting precipitation and suppressing <br />hail, it would be desirable to eliminate, or at least substantially reduce, uncertainties about the <br />effects of cloud seeding. However, the large variability of natural precipitation and the variations <br />in responses to seeding make it impossible to accomplish this objective by merely repeating past <br />experiments. Tukey et al. (1978) have pointed to the dangers of subjective influences on data <br />collection and analysis, and the multiplicity of statistical tests performed in some experiments as <br />reasons for skepticism. Today, conservative scientists: are unlikely to accept a "statistically <br />significant" result from a cloud seeding experiment as establishing anything more than a suitable <br />hypothesis for a series of confirmatory experiments. <br /> <br />Fortunately, new tools have become available over the past 20 years that permit an engineering <br />approach, rather than a purely statistical approach, to weather modification. An engineering <br />approach involves studies of the generation and delivery of cloud seeding agents, the growth of both <br />natural and artificial precipitation particles in different types of clouds, and changes in cloud <br />dynamics that might be induced by seeding, The new tools include numerical models of cloud <br />systems, which can simulate the activity of cloud seeding agents as well as natural cloud proce:;ses, <br />new sensors for observing atmospheric water in its various phases, and modern computers. The <br />computers make it possible to process the data generated by the new sensors and to run the <br />numerical cloud models, which help in the interpretation of results. Computers also do an <br />important job in aircraft navigation; without on-board computers, it would be impossible for a <br />research aircraft to return repeatedly to the same cloud volume to follow the history of plumes of <br />seeding materials and the growth of precipitation particles. <br /> <br />2. New Sensors for Measuring Atmospheric Water <br /> <br />Weather modification experiments have utilized many types of equipment to monitor atmospheric <br />water vapor as well as clouds and precipitation, and new ones have been added in recent years. <br />Doppler radar sets to map the motions of precipitation particles and radar sets with polarization <br />capability to distinguish liquid precipitation particles from solid ones could be included in the next <br />generation of experiments. Meteorological satellites, with their ability to depict clouds. and Il)ap <br />cloud top temperatures over large areas, are playing a role of increasing importance. Laser-based <br />devices have been used to probe clouds from ground stations in Utah (Sassen, 1985), for exnmple, <br />but their utility in cloud seeding experiments is limited due to the strong absorption of visible light <br />by clouds (Carswell, 1981). <br /> <br />2 <br />