Self-Organizing Maps for the Classification of Spatial and Temporal Variability of Tornado-Favorable Parameters

Abstract

A nuanced analysis of the spatial and temporal distribution of supercell tornadoesand 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. The 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. Regions with similar temporal probabilities are then classified by self-organizing maps (SOMs). The SOM classification of tornado probabilities 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.