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<br />003212 <br /> <br />winter orographic cloud temperatures as warm as 0 oC appears feasible (Reynolds, 1989; <br />Reynolds, 1991), so many winter storms too warm to treat with ground-released Agl may be <br />seedable with propane_ <br /> <br />Greater appreciation now exists of the logistics involved in conducting seeding experiments over <br />mountainous terrain, especially in regard to the need for low level measurements by aircraft <br />and remote sensing systems. Experimental areas must be chosen that permit aircraft <br />observations into the lowest kilometer above the terrain where most SLW is concentrated and <br />where ground-released seeding plumes are fDund_ Evidence that most SLW is located near <br />mountain surfaces includes observations (Hobbs, 1975a; Holroyd and Super, 1984; Hill, 1986; <br />Thompson and Super, 1987; Heggli and Rauber, 1988) and results of numerical model runs <br />(Young, 1974; Blumenstein et al., 1987). Experimental areas should also permit reasonable <br />surface access to the barrier top, where a variety of sensing systems must be operated_ <br /> <br />Recent improvements in instrumentation have made it practical to monitor the key physical <br />processes involved in cloud seeding. For example, microwave radiometers can remotely monitor <br />SLW above mountain barriers. Two-dimensional laser imaging probes can be used on aircraft <br />and on the ground to observe and analyze vast numbers of ice particles. Tracer gas and radar <br />chaff can be released and tracked to monitor positions of seeding plumes and resulting ice <br />crystals. Doppler radars can provide wind fields over mountain barriers. Wind-profiling radars <br />using the RASS (radio acoustic sounding system) technique can continuously monitor vertical <br />profiles of wind and virtual temperature. Automatic weather stations can provide surface <br />measurements of wind, temperature, and moisture over mountain barriers. Chemical analysis <br />methods are sensitive enough to detect silver in snow from Agl seeding. <br /> <br />Reynolds (1988), in a review of winter snDwpack augmentation, showed that a consistent <br />relationship is emerging between physical studies and statistical results. For example, <br />Reclamation and other scientists have provided convincing evidence that the physical seeding <br />hypothesis was correct in a limited number of experiments in recent years_ Super and <br />Heimbach (1988) confirmed microphysical changes in seeded clouds over the Bridger Range <br />that presumably increased snowfall (no surface observations were made in the limited 4-week <br />study)_ Super and BDe (1988b) showed evidence of precipitation changes at aircraft sampling <br />levels and on the surlace during a 2-mD study period over the Grand Mesa, Colorado. Deshler et <br />al_ (1990) demonstrated seeding-induced microphysical changes at aircraft levels in about 35 pct <br />of their experiments, but, as previously noted, following seeding effects to the ground proved <br />difficult in the Sierra Nevada Deshler and Reynolds (1990) presented a case study in which the <br />effects of aerial seeding were followed for over 90 min and 100 km. These direct detection <br />physical experiments have been encouraging. However, too few have been conducted to <br />demonstrate how often storm conditions permit the seeding hypothesis to operate, or how much <br />additional snowfall might result from routine seeding. Moreover, the technology exists to design <br />improved direct detection experiments to better determine appropriate seeding rates and <br />approaches for given cloud conditions. <br /> <br />Improvements in numerical modeling of winter orographic clouds have significantly aided the <br />understanding of airflow and microphysical processes (Young, 1974; Cotton et al., 1986; <br />Bruintjes et al., 1992). Incorporating observations from a particular mountain region into a <br />numerical model adapted for that region can markedly increase understanding of the key <br />processes involved and how seeding influences them. Numerical model simulations can be run <br />with each set of intensive observations to continually improve experimental design, and to test <br /> <br />9 <br />