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- Author or Editor: Erin Jones x
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Abstract
A practical approach is recommended for identifying and archiving tornado events, based on the use of definitions that label all vortices as either type I, II, or III tornadoes. This methodology will provide a more meaningful tornado climatology in Storm Data, which separates and classifies all vortices associated in any manner with cumuliform clouds. Tornadoes produced within the mesocyclone of discrete supercell storms, with strong local updrafts (SLUs), will be classified as type I tornadoes. Frequently, these type I tornadoes result from the interaction of the SLU with strong rear-flank downdrafts (RFDs), or with shear vortices in the PBL. Tornadoes produced in association with quasi-linear convective systems (QLCS) will be classified as type II tornadoes (including cold pool, rear-inflow jets, bookend, and mesovortex events along the line). All other vortex types (including landspouts, waterspouts, gustnadoes, cold air vortices, and tornadoes not associated with mesocyclones or QLCS) will be labeled as type III tornadoes. A general discussion is provided that further clarifies the differences and categorization of these three classifications (which encompass 15 tornado species), along with a recommendation that NOAA adopt this taxonomy in operational and data archiving practices. Radar analysis and field observations, combined with storm-scale meteorological expertise, should allow for the official “typing” of tornado reports by NOAA personnel. Establishment of such a climatological database in Storm Data may be of value in assessing the effects (if any) of twenty-first-century global warming on U.S. tornado trends.
Abstract
A practical approach is recommended for identifying and archiving tornado events, based on the use of definitions that label all vortices as either type I, II, or III tornadoes. This methodology will provide a more meaningful tornado climatology in Storm Data, which separates and classifies all vortices associated in any manner with cumuliform clouds. Tornadoes produced within the mesocyclone of discrete supercell storms, with strong local updrafts (SLUs), will be classified as type I tornadoes. Frequently, these type I tornadoes result from the interaction of the SLU with strong rear-flank downdrafts (RFDs), or with shear vortices in the PBL. Tornadoes produced in association with quasi-linear convective systems (QLCS) will be classified as type II tornadoes (including cold pool, rear-inflow jets, bookend, and mesovortex events along the line). All other vortex types (including landspouts, waterspouts, gustnadoes, cold air vortices, and tornadoes not associated with mesocyclones or QLCS) will be labeled as type III tornadoes. A general discussion is provided that further clarifies the differences and categorization of these three classifications (which encompass 15 tornado species), along with a recommendation that NOAA adopt this taxonomy in operational and data archiving practices. Radar analysis and field observations, combined with storm-scale meteorological expertise, should allow for the official “typing” of tornado reports by NOAA personnel. Establishment of such a climatological database in Storm Data may be of value in assessing the effects (if any) of twenty-first-century global warming on U.S. tornado trends.
Abstract
Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.
Abstract
Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.
