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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . snowpack). Records of the variable to be tested are acquired for an historical period of several years duration (20 years or more if possible). These records are partitioned into those located within the designated "target" area of the project and those in a nearby "control" area. Ideally the control sites should be selected in an area meteorologically similar area to the target. but onc ......hich would be unaffected by the seeding (or seeding from other adjacent projects). The historical data (e.g.. precipitation) in both the target and control areas are taken from past years that have not been subject to cloud seeding activities. These historical data are evaluated for the same seasonal period of lime as that when the seeding was later conducted. The target and control sets of data for the unseeded seasons are used to develop an equation (typically a linear regression) \\'hich predicts the amount of target area precipitation. based on precipitation observed in the control area. This regression equation is then used during the seeded period. to estimate what the target area precipitation should have been without seeding, based on the control area precipitation. This allows a comparison to be made between the predicted target area precipitation and that which actually occurred during the seeded period. to look for any differences potentially caused by seeding activity. This target and control technique works well where a good historical correlation can be found between target and control area precipitation. Generally, the closer the target and control areas are geographically. and in terms of elevation. the higher the correlation will be. Areas selected too close together. however. can be subject to contanlination of the control area by the seeding activities. This can result in an underestimate of the seeding effect. For precipitation and snowpack assessments. a correlation coefficient (r) of 0.90 or greater would be considered excellent. A correlation coefficient of 0.90 would indicate that oyer SO percent of the variance (r) in the historical data set would be explained by the regression equation used to predict the variable (expected precipitation or snowpack) in the seeded years. An equation indicating perfect correlation would have an r value of 1.0. In this particular case. one potential target site (~tcClure Pass. which measures data appropriate for both the precipitation and snowpack evaluations) \\.'as considered questionable due to its location near the northern boundary of the target area. where lesser seeding effects might be expected to occur due to targeting considerations. Also. there was the question of 29