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    Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

    Source: Journal of Applied Meteorology and Climatology:;2013:;volume( 052 ):;issue: 006::page 1458
    Author:
    Xue, Lulin
    ,
    Tessendorf, Sarah A.
    ,
    Nelson, Eric
    ,
    Rasmussen, Roy
    ,
    Breed, Daniel
    ,
    Parkinson, Shaun
    ,
    Holbrook, Pat
    ,
    Blestrud, Derek
    DOI: 10.1175/JAMC-D-12-0149.1
    Publisher: American Meteorological Society
    Abstract: our cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.
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      Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4216968
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    • Journal of Applied Meteorology and Climatology

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    contributor authorXue, Lulin
    contributor authorTessendorf, Sarah A.
    contributor authorNelson, Eric
    contributor authorRasmussen, Roy
    contributor authorBreed, Daniel
    contributor authorParkinson, Shaun
    contributor authorHolbrook, Pat
    contributor authorBlestrud, Derek
    date accessioned2017-06-09T16:49:13Z
    date available2017-06-09T16:49:13Z
    date copyright2013/06/01
    date issued2013
    identifier issn1558-8424
    identifier otherams-74712.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4216968
    description abstractour cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.
    publisherAmerican Meteorological Society
    titleImplementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests
    typeJournal Paper
    journal volume52
    journal issue6
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-12-0149.1
    journal fristpage1458
    journal lastpage1476
    treeJournal of Applied Meteorology and Climatology:;2013:;volume( 052 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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