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    Observations of Right-Moving Supercell Motion Forecast Errors

    Source: Weather and Forecasting:;2017:;volume 033:;issue 001::page 145
    Author:
    Bunkers, Matthew J.
    DOI: 10.1175/WAF-D-17-0133.1
    Publisher: American Meteorological Society
    Abstract: AbstractTwo shear-based supercell motion forecast methods are assessed to understand how each method performs under differing environmental conditions for observed right-moving supercells. Accordingly, a 573-case observational dataset is partitioned into small versus large values of environmental and storm-related variables such as bulk wind shear, convective available potential energy, mean wind, storm motion, and storm-relative helicity (SRH). In addition, hodographs are partitioned based on the tornado damage scale, as well as where the storm motion falls among the four quadrants. With respect to the 573-case dataset, the largest supercell motion forecast errors generally occur when the (i) observed midlevel (4?5 km AGL) storm-relative winds are either anomalously weak or strong, (ii) observed 0?3-km AGL SRH is large, (iii) supercell motion is fast, (iv) convective inhibition is strong, or (v) the surface?500-mb (1 mb = 1 hPa) RH is low. Moreover, significantly tornadic supercells are biased 1.2 m s?1 slower and farther right of the hodograph than predicted by the Bunkers forecast method, but show very small bias for the modified Rasmussen?Blanchard method (though errors are slightly larger for this method). Conversely, the smallest errors occur when, relative to the overall sample, the (i) observed upper-level (9?10 km AGL) storm-relative winds are strong, (ii) supercell motion is slow or the mean wind is weak, (iii) surface?500-mb RH is high, or (iv) convective inhibition is weak. Errors also are relatively small when storm motion lies in the bottom-left hodograph quadrant.
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      Observations of Right-Moving Supercell Motion Forecast Errors

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261387
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    contributor authorBunkers, Matthew J.
    date accessioned2019-09-19T10:05:20Z
    date available2019-09-19T10:05:20Z
    date copyright11/22/2017 12:00:00 AM
    date issued2017
    identifier otherwaf-d-17-0133.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261387
    description abstractAbstractTwo shear-based supercell motion forecast methods are assessed to understand how each method performs under differing environmental conditions for observed right-moving supercells. Accordingly, a 573-case observational dataset is partitioned into small versus large values of environmental and storm-related variables such as bulk wind shear, convective available potential energy, mean wind, storm motion, and storm-relative helicity (SRH). In addition, hodographs are partitioned based on the tornado damage scale, as well as where the storm motion falls among the four quadrants. With respect to the 573-case dataset, the largest supercell motion forecast errors generally occur when the (i) observed midlevel (4?5 km AGL) storm-relative winds are either anomalously weak or strong, (ii) observed 0?3-km AGL SRH is large, (iii) supercell motion is fast, (iv) convective inhibition is strong, or (v) the surface?500-mb (1 mb = 1 hPa) RH is low. Moreover, significantly tornadic supercells are biased 1.2 m s?1 slower and farther right of the hodograph than predicted by the Bunkers forecast method, but show very small bias for the modified Rasmussen?Blanchard method (though errors are slightly larger for this method). Conversely, the smallest errors occur when, relative to the overall sample, the (i) observed upper-level (9?10 km AGL) storm-relative winds are strong, (ii) supercell motion is slow or the mean wind is weak, (iii) surface?500-mb RH is high, or (iv) convective inhibition is weak. Errors also are relatively small when storm motion lies in the bottom-left hodograph quadrant.
    publisherAmerican Meteorological Society
    titleObservations of Right-Moving Supercell Motion Forecast Errors
    typeJournal Paper
    journal volume33
    journal issue1
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-17-0133.1
    journal fristpage145
    journal lastpage159
    treeWeather and Forecasting:;2017:;volume 033:;issue 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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