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<br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br /> <br />4.0 EVALVA TIO:'iS or SEEDI:'iG EFrECTlVE:'iESS <br /> <br />The task of determining the effects of cloud seeding has received considerable attention <br />over the years. Evaluating the results of a cloud seeding program for a single season is rather <br />difficult. The primary reason for the difficulty stems from the large natural variability in the <br />amounts of precipitation that occur in a given area, and between one area and another during a <br />given season. Since cloud seeding is normally feasible only when existing clouds are near to (or <br />already are) producing precipitation. it is not usually obvious if. and how much. the precipitation <br />""'as actually increased by seeding due to this large natural variability. The ability to detect a <br />seeding effect becomes a function of the magnitude of the seeding increase and the number of <br />seeded events. compared with the natural variability in the precipitation pattern. Larger seeding <br />effects can be detected more easily, and ,",'ith a smaller number of seeded cases. than are required <br />to detect small increases_ <br /> <br />Historically. the most significant seeding results ha\.e been observed in wintertime <br />seeding programs in mountainous areas. However. the apparent differences due to seeding are <br />relati\-ely small. being of the order of a 5.20 percent seasonal increase_ In pan. this relatively <br />small percentage increase accounts for the significant number of cases required to establish these <br />results (often live years or more). <br /> <br />Despite the difficulties invoh.ed. some techniques arc available for evaluation of the <br />effects of operational seeding programs. These techniques are not as rigorous or scientifically <br />desirable as is the randomization technique used in research. where roughly half the sample of <br />stornl events is randomly left unseeded. ~10st of!\A WC's clients do not wish to reduce the <br />potential benefits ofa cloud seeding project by half in order to better document the effects of the <br />cloud seeding project. The less rigorous techniques do. however. offer helpful indications of the <br />long-ternl effects of seeding on operational programs. <br /> <br />A commonly employed technique. and the one utilized by !'\A we in this assessment, is <br />the "target" and "control" comparison. This technique is one described by Dr. Arnett Dennis in <br />his book entitled "Weather 7\lodification by Cloud Seeding"(l980). This technique is based on <br />the selection of a variable that would be affected by seeding (such as liquid precipitation or <br /> <br />28 <br />