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    The Challenge of Forecasting Significant Tornadoes from June to October Using Convective Parameters

    Source: Weather and Forecasting:;2016:;volume( 031 ):;issue: 006::page 2075
    Author:
    Hart, John A.
    ,
    Cohen, Ariel E.
    DOI: 10.1175/WAF-D-16-0005.1
    Publisher: American Meteorological Society
    Abstract: his study is an application of the Statistical Severe Convective Risk Assessment Model (SSCRAM), which objectively assesses conditional severe thunderstorm probabilities based on archived hourly mesoscale data across the United States collected from 2006 to 2014. In the present study, SSCRAM is used to assess the utility of severe thunderstorm parameters commonly employed by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June?October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of the within-year variability of severe storms predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as the effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease during the months of June?October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June?October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.
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      The Challenge of Forecasting Significant Tornadoes from June to October Using Convective Parameters

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4231973
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    contributor authorHart, John A.
    contributor authorCohen, Ariel E.
    date accessioned2017-06-09T17:37:19Z
    date available2017-06-09T17:37:19Z
    date copyright2016/12/01
    date issued2016
    identifier issn0882-8156
    identifier otherams-88217.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231973
    description abstracthis study is an application of the Statistical Severe Convective Risk Assessment Model (SSCRAM), which objectively assesses conditional severe thunderstorm probabilities based on archived hourly mesoscale data across the United States collected from 2006 to 2014. In the present study, SSCRAM is used to assess the utility of severe thunderstorm parameters commonly employed by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June?October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of the within-year variability of severe storms predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as the effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease during the months of June?October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June?October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.
    publisherAmerican Meteorological Society
    titleThe Challenge of Forecasting Significant Tornadoes from June to October Using Convective Parameters
    typeJournal Paper
    journal volume31
    journal issue6
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-16-0005.1
    journal fristpage2075
    journal lastpage2084
    treeWeather and Forecasting:;2016:;volume( 031 ):;issue: 006
    contenttypeFulltext
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