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

Nowotarski Christopher J.; Jones Erin A.

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

机译：使用近风暴，低水平风和热力学型材对超级电池龙卷风环境进行分类的多变量自组织地图方法

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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.

机译：自组织地图（SOM）已被证明是分类气象数据的有用工具。本文在早期的工作中建立了索麦斯，以分类龙卷风和顿涅尔超级雷暴的近风暴环境的模型分析邻近探测。产生一系列多变量SOM，其中改变了输入变量，高度，尺寸和SOM节点的数量。索细数据库包括关于近风暴风轮廓的信息在龙头和顿路风暴之间比限于热力学信息的差异更有效。对于最佳性能的SOM，从匹配近风暴环境匹配到SOM节点的概率预测可以为预测技术提供适度的改进，相对于概率预测的现有方法。

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《Weather and forecasting》 |2018年第3期|共10页
作者
Nowotarski Christopher J.; Jones Erin A.;
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作者单位

Texas A&

M Univ Dept Atmospher Sci College Stn TX 77843 USA;

Millersville Univ Pennsylvania Dept Earth Sci Millersville PA 17551 USA;

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原文格式 PDF
正文语种 eng
中图分类大气科学（气象学）;
关键词
Supercells; Tornadoes; Forecasting; Probability forecasts; models; distribution; Statistical forecasting;

机译：超级胶凝土;龙卷风;预测;概率预测;模型;分布;统计预测;

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1. Multivariate Self-Organizing Map Approach to Classifying Supercell Tornado Environments Using Near-Storm, Low-Level Wind and Thermodynamic Profiles [J] . Nowotarski Christopher J., Jones Erin A. Weather and forecasting . 2018,第3期

机译：使用近风暴，低水平风和热力学型材对超级电池龙卷风环境进行分类的多变量自组织地图方法
2. Self-Organizing Maps for the Investigation of Tornadic Near-Storm Environments [J] . Anderson-Frey Alexandra K., Richardson Yvette P., Dean Andrew R., Weather and forecasting . 2017,第4期

机译：自组织地图，用于调查龙卷风近风暴环境
3. SOUNDING-DERIVED LOW-LEVEL THERMODYNAMIC CHARACTERISTICS ASSOCIATED WITH TORNADICAND NON-TORNADIC SUPERCELL ENVIRONMENTS IN THE SOUTHEAST UNITED STATES [J] . Justyn D. Jackson, Michael E. Brown National Weather Digest . 2009,第1期

机译：美国东南部圆环和非圆环超细胞环境相关的低声级热力学特征
4. USING DATA FROM THE METEOROLOGICAL DATA COLLECTION AND REPORTING SYSTEM TO ASCERTAIN THE NEAR-STORM ENVIRONMENT NEAR MEMPHIS, TENNESSEE DURING THE 5-6 FEBRUARY 2008 TORNADO OUTBREAK [C] . Michael B. Scotten AMS annual meeting . 2011

机译：在2008年2月5日至6日TORNADO突袭期间，使用来自气象数据收集和报告系统的数据来确定田纳西州附近风暴的近暴风雨环境
5. Investigating the Sensitivity of Supercell Maturation to Variations in the Low-Level Vertical Wind Profile [D] . Goldacker, Nicholas Alan. 2020

机译：调查超级细胞成熟对低水平垂直风剖面变化的敏感性
6. Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach [O] . Mike van Diest, Jan Stegenga, Heinrich J. Wörtche, -1

机译：使用多元整体运动数据量化运动过程中的姿势控制：一种自组织映射方法
7. Matlab Application of Kohonen Self-organizing Map to Classify Consumers’ Load Profiles [O] . Dragomir Otilia Elena, Dragomir Florin, Radulescu Marian 2014

机译：Matlab应用Kohonen自组织图对消费者的负荷状况进行分类

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

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