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    A Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather

    Source: Weather and Forecasting:;2007:;volume( 022 ):;issue: 004::page 853
    Author:
    Brunner, Jason C.
    ,
    Ackerman, Steven A.
    ,
    Bachmeier, A. Scott
    ,
    Rabin, Robert M.
    DOI: 10.1175/WAF1022.1
    Publisher: American Meteorological Society
    Abstract: Early enhanced-V studies used 8-km ground-sampled distance and 30-min temporal-sampling Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery. In contrast, the ground-sampled distance of current satellite imagery is 1 km for low earth orbit (LEO) satellite IR imagery. This improved spatial resolution is used to detect and investigate quantitative parameters of the enhanced-V feature. One of the goals of this study is to use the 1-km-resolution LEO data to help understand the impact of higher-resolution GOES data (GOES-R) when it becomes available. A second goal is to use the LEO data available now to provide better severe storm information than GOES when it is available. This study investigates the enhanced-V feature observed with 1-km-resolution satellite imagery as an aid for severe weather warning forecasters by comparing with McCann?s enhanced-V study. Therefore, verification statistics such as the probability of detection, false alarm ratio, and critical success index were calculated. Additionally, the importance of upper-level winds to severe weather occurrence will be compared with that of the quantitative parameters of the enhanced-V feature. The main goal is to provide a basis for the development of an automated detection algorithm for enhanced-V features from the results in this study. Another goal is to examine daytime versus nighttime satellite overpass distributions with the enhanced-V feature.
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      A Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4231161
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    contributor authorBrunner, Jason C.
    contributor authorAckerman, Steven A.
    contributor authorBachmeier, A. Scott
    contributor authorRabin, Robert M.
    date accessioned2017-06-09T17:34:48Z
    date available2017-06-09T17:34:48Z
    date copyright2007/08/01
    date issued2007
    identifier issn0882-8156
    identifier otherams-87487.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231161
    description abstractEarly enhanced-V studies used 8-km ground-sampled distance and 30-min temporal-sampling Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery. In contrast, the ground-sampled distance of current satellite imagery is 1 km for low earth orbit (LEO) satellite IR imagery. This improved spatial resolution is used to detect and investigate quantitative parameters of the enhanced-V feature. One of the goals of this study is to use the 1-km-resolution LEO data to help understand the impact of higher-resolution GOES data (GOES-R) when it becomes available. A second goal is to use the LEO data available now to provide better severe storm information than GOES when it is available. This study investigates the enhanced-V feature observed with 1-km-resolution satellite imagery as an aid for severe weather warning forecasters by comparing with McCann?s enhanced-V study. Therefore, verification statistics such as the probability of detection, false alarm ratio, and critical success index were calculated. Additionally, the importance of upper-level winds to severe weather occurrence will be compared with that of the quantitative parameters of the enhanced-V feature. The main goal is to provide a basis for the development of an automated detection algorithm for enhanced-V features from the results in this study. Another goal is to examine daytime versus nighttime satellite overpass distributions with the enhanced-V feature.
    publisherAmerican Meteorological Society
    titleA Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather
    typeJournal Paper
    journal volume22
    journal issue4
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF1022.1
    journal fristpage853
    journal lastpage872
    treeWeather and Forecasting:;2007:;volume( 022 ):;issue: 004
    contenttypeFulltext
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