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    Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks

    Source: Bulletin of the American Meteorological Society:;2020:;volume( 100 ):;issue: 012::page 2473
    Author:
    Lundquist, Jessica;Hughes, Mimi;Gutmann, Ethan;Kapnick, Sarah
    DOI: 10.1175/BAMS-D-19-0001.1
    Publisher: American Meteorological Society
    Abstract: In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a wide range of communities.
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      Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4264479
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    contributor authorLundquist, Jessica;Hughes, Mimi;Gutmann, Ethan;Kapnick, Sarah
    date accessioned2022-01-30T18:05:37Z
    date available2022-01-30T18:05:37Z
    date copyright1/7/2020 12:00:00 AM
    date issued2020
    identifier issn0003-0007
    identifier otherbams-d-19-0001_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264479
    description abstractIn mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a wide range of communities.
    publisherAmerican Meteorological Society
    titleOur Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks
    typeJournal Paper
    journal volume100
    journal issue12
    journal titleBulletin of the American Meteorological Society
    identifier doi10.1175/BAMS-D-19-0001.1
    journal fristpage2473
    journal lastpage2490
    treeBulletin of the American Meteorological Society:;2020:;volume( 100 ):;issue: 012
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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