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dc.contributor.authorBunkers, Matthew J.
dc.date.accessioned2019-09-19T10:05:20Z
dc.date.available2019-09-19T10:05:20Z
dc.date.copyright11/22/2017 12:00:00 AM
dc.date.issued2017
dc.identifier.otherwaf-d-17-0133.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261387
dc.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.
dc.publisherAmerican Meteorological Society
dc.titleObservations of Right-Moving Supercell Motion Forecast Errors
dc.typeJournal Paper
dc.journal.volume33
dc.journal.issue1
dc.journal.titleWeather and Forecasting
dc.identifier.doi10.1175/WAF-D-17-0133.1
dc.journal.fristpage145
dc.journal.lastpage159
dc.treeWeather and Forecasting:;2017:;volume 033:;issue 001
dc.contenttypeFulltext


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