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Last modified
7/28/2009 2:27:39 PM
Creation date
10/1/2006 2:12:02 PM
Metadata
Fields
Template:
Weather Modification
Applicant
Steven M. Hunter
Sponsor Name
California Energy Commission
Project Name
Optimizing Cloud Seeding for Water and Energy in California
Title
Optimizing Cloud Seeding for Water and Energy in California
Prepared For
California Energy Commission
Prepared By
Steven M. Hunter
Date
3/31/2006
State
CA
Country
United States
Weather Modification - Doc Type
Report
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<br />d \8'596fH,J-62 AI f h '. . . 1 "" h" <br />egrees' , . ' , . l~' ore measurements 0 t e vanatlons 01 Ice crysta concentrations "dt m <br />seeded plumes arc needed6J:60. Even if such measurements arc lacking, it is prudent to space <br />generators/dispensers close enough across the wind to produce m'erlapping plumes and <br />sutlicient crystal mass for significant snowfall increases (20 per liter minimum requirement, <br />Section C3a). These instrument sitings and configurations are among the most important ways <br />to optimize seeding. Operational projects should assess these issues and make changes as <br />needed, Much more detail on SLW availability, T&D, and generator siting may be found in a <br />recent seeding feasibility studyt>4. <br /> <br />Finally, Agl generators should be able to adjust and measure solution flow rate and name <br />temperature, to ensure that seeding is occurring as planned. Likewise for LP dispensers. propane <br />flow rate and temperature downstream of the expansion nozzle should be monitored, The search <br />for optimum chemistry fonnulations, burners and particle sizes from generators should continue. <br /> <br />c) Evaluation Techniques <br /> <br />Scientilic evaluation of seeding efTects adds cost to operational seeding programs, and therefore <br />has not been commonly pursued as part of those programs. Thcre have been several research <br />projects in which evaluation of efYects was the primary objective. The main goal here should be <br />to optimize seeding methods through applied research. This research should demonstrate that <br />seeding materials arc producing the desired precipitation increases in the target area, The three <br />primary evaluation methods may be catcgori7.cd as physical. modeling, and statistical. <br /> <br />Physical Techniques. The approaches in this category involve either remote or in situ <br />measurements of seeded plumes. their effects on precipitation, or other atmospheric parameters <br />related to cloud seeding. To measure seeded plumes and their effects on precipitation, aircratl <br />sampling and tra~c chemical anal.yses of s~o\\' have bec~ uscd, Exa6Tgl~ ~ncludc single and <br />dual traccr techmques, and combmcd phYSical and chemical methods . -. .6. An examplc of <br />measuring sccding-related paramcters would be microwave radiometer (remote sensing) and <br />aircraft or ground (in situ) measurcments of SLW. Satellite sensors continue to be improvcd and <br />techniques for monitoring eloud hydrometeors, such as the one used to produce Figurc 3, will <br />certainly contributc to weather modification-related knowledge. Satellites have the advantage of <br />a much wider sampling arca than other instruments. and may bc able to measure cloud SI. W in <br />certain cases. Radar has also been used to track seeding plumes68;6'l. There is a consensus among <br />scientilic organizationsI1:Jo;31 that physical mcasurcments are crucial to evaluations, since thcy <br />arc needed to verify and quantify the physical chain of events rcquired for succcssful seeding. <br />Monitoring of natural ambient conditions such as SL Wand temperature in admnce of any <br />seeding is highly desirable, since it would set a baseline for evaluating seeding feasibility and <br />evcntual seeding effccts. <br /> <br />Modeling Techniques. lllis approach has gained popularity in the last decade. fuel cd by <br />increascs in computing pov.'er. Recent examplcs include the Colorado and Nevada WDMP <br />experiments and the Wyoming pilot project (Section C3a), These projects have lIsed <br />sophisticated 3-D numcrical cloud modcls coupled with dispersion models. Thesc models <br />pn:dict sceded plume dispcrsion in mountainous tcrrain. which have been used for targeting <br /> <br />22 <br />
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