Multivariate Self-Organizing Map Approach to Classifying Supercell Tornado Environments Using Near-Storm, Low-Level Wind and Thermodynamic Profiles

Multivariate Self-Organizing Map Approach to Classifying Supercell Tornado Environments Using... AbstractSelf-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Multivariate Self-Organizing Map Approach to Classifying Supercell Tornado Environments Using Near-Storm, Low-Level Wind and Thermodynamic Profiles

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
eISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0189.1
Publisher site
See Article on Publisher Site

Abstract

AbstractSelf-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.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Jun 22, 2018

References

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