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. . . . except during a limited time for one aircraft pass over the TAR site. Later in the experiment, few IN were encountered at the surface on the Plateau. For the present case study, the modeling suggests the valley-released AgI had an initial vertical impetus by the gravity wave mechanism which provided transport above the surface inversion. This was followed by orographic forcing in a more organized westerly flow. The model confirmed the confinement of the plume to a shallow layer and subsequent lifting due again to a gravity wave was further eastward in the vicinity of the TAR site: It may be possible to exploit the influence of gravity waves in surface-based seeding strategies. This was explored by Heimbach and Hall (1994) who applied the model to several source configurations.' Super (1994) suggested that targeting might be enhanced if releases were from a mountain upwind of the Plateau (San Pitch). For the-current case, we suggest that the upwind side of this barrier would be appropriate. Figure 6 also indicates that the W side of the Sanpete Valley could provide some initial upward impetus; however, the subsequent downward portion of the gravity wave could have a detrimental influence on targeting. Releases further upwind of the current valley sites have the disadvantage of greater dispersion which would reduce the concentrations over the target, and greater difficulty in horizontal ~geting. Also, the increased fetch would make targeting more difficult. 8.21. Heimbach, J. A., and A. B. Super, 1996: Simulating the influence of type II error on the outcome of past statistical experiments. J. Applied Meteorology, 35, 1551-1567. ABSTRACT Simulations of randomized winter orographic weather modification experiments were used to explore a possible cause of the many inconclusive results from previous statistical experiments. There is increasing evidence that the response to cloud treatment is highly variable due to differences in the availability of cloud liquid water, seeding agents, targeting effectiveness, and other factors. For this reason the simulations described in this paper focus on the sensitivity of previously applied statistical techniques to different responses to seeding.. Data for the simulations came from two sources: the Bridger Ranger Experiments (BRE). conducted during the two winters from 1970 to 1972, and SNOTEL (snowpack telemetry) data from the Boise. Idaho, area during the winters of 1985-92. The principal focus is on the BRE data from which 6-h experimental units were extracted. This is because previous analyses of these data support the notion of a variable treatment response. Twenty-four-hour experimental units from the BRE and Idaho datasets were also incorporated into the simulations. The simulations indicate a sensitivity to the size of the fraction of seeded units, which had a treatment response with the power of the test being significantly reduced as the fraction of seeded units showing a response decreased. It is suggested that past estimates of experimental duration, based on the simple model that assumed all seeded units have the same response were overly optimistic. The results may partially explain the high frequency of inclusive results from past statistical cloud seeding experiments. The implications of these results is described for past and future statistical weather modification experiments. , .. Monte Carlo techniques were applied in simulations that assumed a randomized target-control experiment. There were five models applied, which involved adding a percentage or constant responses to all or a fraction of the ,seeded units and capping the maximum increment. Experimental units were randomly selected from a pool of nonseeded cases. The selected units were randomly seeded or not seeded, and the seeded units were again randomly selected to have all or a fraction of them show a treatment effect, while keeping the net seasonal response approximately constant: For example, in the case of one out of three 71