Self-Organizing Maps for the Classification of Spatial and Temporal Variability of Tornado-Favorable Parameters
Self-Organizing Maps for the Classification of Spatial and Temporal Variability of Tornado-Favorable Parameters
复制标题
用于龙卷风有利参数时空变化分类的自组织映射
DOI：
10.1175/mwr-d-21-0168.1
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发表时间：
2021
影响因子：
3.2
通讯作者：
Bryan T. Smith
中科院分区：
文献类型：
--
作者：
Alexandra K. Anderson;Yvette P. Richardson;A. Dean;Richard L. Thompson;Bryan T. Smith
对超细胞龙卷风的空间和临时分布的细微分析以及与这些龙卷风相关的近传感器环境的特征，对于我们对我们对龙卷风的范围的理解至关重要，这些环境可以被视为龙卷风的范围，并且对这两个工作的概率及其相关的环境参数均可及其相关的环境范围。 Probability of deviation above or below the median tornadic near-storm environmental parameter values are estimated by kernel density estimation and classified by self-organizing maps (SOMs). The SOM classification for tornado probability allows for further examination of the deviation of the environmental parameters from the median for each probability cluster. Regions that have similar tornado possibilities but different in the deviation of the environmental parameters (“parameter anomalies”) are also突出显示了不同区域的异常模式，通常沿季节性或不同的尺度演变，但这两者都很少出现，这两者都需要基于近态环境的灵活模型。特定于区域的且可能特定时间的环境基线评估，以提高预测和警告技巧。
A nuanced analysis of the spatial and temporal distribution of supercell tornadoes and the characteristics of the near-storm environments associated with those tornadoes is critical to improving our understanding of the range of environments that can be considered tornado-favorable. This work classifies both supercell tornado probabilities and their associated environmental parameters on hourly and daily time scales based on geographical regions: regional probability of tornado events and the probability of deviation above or below the median tornadic near-storm environmental parameter values are estimated by kernel density estimation and classified by self-organizing maps (SOMs). The SOM classification for tornado probability allows for further examination of the deviation of the environmental parameters from the median for each probability cluster. Regions that have similar tornado probabilities but differ in the deviation of the environmental parameters (“parameter anomalies”) are also highlighted using SOMs. The anomaly patterns for different regions and parameters generally evolve along either seasonal or diurnal scales, but rarely both, highlighting the need for flexible models of tornado potential based on the near-storm environment. The spatial and temporal variability of parameter anomalies add complexity to traditional forecasting approaches that depend upon a fixed set of environmental parameter thresholds. This work highlights the need to develop region-specific and potentially time-specific environmental baseline evaluation to improve forecast and warning skill.