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    An Intercomparison of Mesoscale Forecasts of Aircraft Icing Using SSM/I Retrievals

    Source: Weather and Forecasting:;1996:;volume( 011 ):;issue: 001::page 66
    Author:
    Tremblay, André
    ,
    Cober, Stewart G.
    ,
    Glazer, Anna
    ,
    Isaac, George
    ,
    Mailhot, Jocelyn
    DOI: 10.1175/1520-0434(1996)011<0066:AIOMFO>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: A technique for the detection of supercooled liquid water (SLW) from Special Sensor Microwave/ (SSM/I) data is discussed. For this study, these SLW retrievals depict areas of icing that are used to compare against different aircraft icing forecast algorithms. It is shown that currently used automated algorithms give incorrect distributions of SLW events with temperature and include systematically glaciated clouds in icing forecasts. This problem is eliminated when an explicit SLW scheme is used. With this scheme the Canadian forecast system can detect roughly 75% of the SLW signal within an accuracy of 100 km when compared to SSM/I retrievals. The scheme also detects 99% of nonicing events within the same accuracy.
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      An Intercomparison of Mesoscale Forecasts of Aircraft Icing Using SSM/I Retrievals

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4165445
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    • Weather and Forecasting

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    contributor authorTremblay, André
    contributor authorCober, Stewart G.
    contributor authorGlazer, Anna
    contributor authorIsaac, George
    contributor authorMailhot, Jocelyn
    date accessioned2017-06-09T14:51:32Z
    date available2017-06-09T14:51:32Z
    date copyright1996/03/01
    date issued1996
    identifier issn0882-8156
    identifier otherams-2834.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4165445
    description abstractA technique for the detection of supercooled liquid water (SLW) from Special Sensor Microwave/ (SSM/I) data is discussed. For this study, these SLW retrievals depict areas of icing that are used to compare against different aircraft icing forecast algorithms. It is shown that currently used automated algorithms give incorrect distributions of SLW events with temperature and include systematically glaciated clouds in icing forecasts. This problem is eliminated when an explicit SLW scheme is used. With this scheme the Canadian forecast system can detect roughly 75% of the SLW signal within an accuracy of 100 km when compared to SSM/I retrievals. The scheme also detects 99% of nonicing events within the same accuracy.
    publisherAmerican Meteorological Society
    titleAn Intercomparison of Mesoscale Forecasts of Aircraft Icing Using SSM/I Retrievals
    typeJournal Paper
    journal volume11
    journal issue1
    journal titleWeather and Forecasting
    identifier doi10.1175/1520-0434(1996)011<0066:AIOMFO>2.0.CO;2
    journal fristpage66
    journal lastpage77
    treeWeather and Forecasting:;1996:;volume( 011 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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