Dissemination of Student Publications

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

Student Author(s): JONES, ERIN A.
Faculty Author(s): -
Department: ESCI
Publication: Weather & Forecasting
Year: 2018
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 nearstorm 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 FROM AUTHOR] Copyright of Weather & Forecasting is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Link: Multivariate Self-Organizing Map Approach to Classifying Supercell Tornado Environments Using Near-Storm, Low-Level Wind and Thermodynamic Profiles.