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

abstract

• 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.

authors

• Nowotarski, Christopher

published proceedings

• WEATHER AND FORECASTING

altmetric score

• 1.75

author list (cited authors)

• Nowotarski, C. J., & Jones, E. A.

citation count

• 12

complete list of authors

• Nowotarski, Christopher J||Jones, Erin A

publication date

• June 2018

publisher

• American Meteorological Society  Publisher

published in

• Weather and Forecasting  Journal

keywords

• Distribution
• Forecasting
• Models
• Probability Forecasts
• Statistical Forecasting
• Supercells
• Tornadoes

Digital Object Identifier (DOI)

• 10.1175/WAF-D-17-0189.1

start page

• 661

end page

• 670

volume

• 33

issue

• 3

URL

• http://dx.doi.org/10.1175/waf-d-17-0189.1