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<br />seeding materials, to produce desired precipitation augmentation. Assessment of the ability of <br />seeding to meet these critcria can be done through direct observations or possibly through <br />modcling. <br /> <br />As related in the NRC rcport12, computer modeling has secn great advances in the last two to <br />three decadcs, and weather modification should capitalize on its use. Three-dimensional cloud <br />models capable of simulating T&D and cloud microphysics related to seeding effects have <br />already been used in research, Examples include experiments in Utah5.\ the Nevada WDMp51, <br />Colorado WDMp54, and a fcasibility study for a new Wyoming cloud sceding pilot project55. <br />Such modeling can bc used to prcdict seeded plume T &0 for optimum placement of secding <br />generators, as well as verilication of secding cffect (precipitation enhancement). While thcre <br />have becn signiticant advances in modeling, most investigators agree that models are not <br />currently sophisticated cnough to accurately simulate all relevant cloud processes, and thereforc <br />they should not bc the only tools used in prcdiction and veritication. This is where dircct <br />observations can hclp, <br /> <br />Direct physical measurements have been madc in weather modification for many ycars. An <br />important type of such mcasuremcnts has been tr..lcers, Examplcs of tracers are gases such as <br />sulfur hexalluoride (SF6) or chemicals like silver, indium, cesium or rubidium. The chcmicals <br />arc otten released simultaneously with aircraft or ground relcased seeding materials, and can be <br />detected downwind of thosc sources. The silver content from the seeding material itself (Agl) <br />has frequently bccn used as a tracer. This silvcr can cither be scavenged from clouds by natural <br />(not secded) prccipitation, or it can be deposited in thc snowpack as a rcsult of ice nucleation, <br />growth and fallout. Therefore. comparativc use of the other chemicals that can be scavenged, but <br />do not havc ice-nuclcating properties, can provide strong evidence that the sceding plume <br />produced additional precipitation over the target area56. Failure to measure silver in snow at <br />grcater than natural background levels, however. indicates that a silver iodide seeding plume did <br />not interact \...,ith clouds and precipitation in the target arca in such a way to make sceding efTcctive. <br />Othcr physical measuremcnts may bc llsed to distinguish seedcd !ium natural ice particles, For <br />example, thc sizes and habits of those particlcs can be measurcd by clcctronic probes or dift.'Ct <br />capture. Also. the density of various laycrs in the snowpack corresponding to sceded or unseeded <br />periods can be compared for any diOcrences. ^ 1994 SCE research project52 mcasured density <br />incrcases in secded snow layers to estimate a minimum 8% increase in snow water from seeding. <br />Morc such rescarch could cnhance the physical basis of projcct evaluation. <br /> <br />Onc of thc greatest uncertainties in both modcling and physical measuremcnt of seeded plumes is <br />the concentration of icc crystals produced by seeding, It is believed that concentrations <br />exceeding 20 pcr liter of air arc rcquircd to produce significant precipitation rates57;47. <br />Asscssmcnt of crystal concentrations (1/ (I gi\'en location in the targct area depends on a) the <br />plume concentrations at a singlc time and b) the spatial meander of the plume with time. As seen <br />from Figure 5, both a) and b) can be highly variable, ~tore research into ice nuc1eants. both <br />natural and sceded. is needed to address these uncertainties, <br /> <br />20 <br />