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.
Recent research suggests that surface elevation variability may influence tornado activity, though separating this effect from reporting biases is difficult to do in observations. Here we employ Bayes’s law to calculate the empirical joint dependence of tornado probability on population density and elevation roughness in the vicinity of Arkansas for the period 1955–2015. This approach is based purely on data, exploits elevation and population information explicitly in the vicinity of each tornado, and enables an explicit test of the dependence of results on elevation roughness length scale. A simple log-link linear regression fit to this empirical distribution yields an 11% decrease in tornado probability per 10-m increase in elevation roughness at fixed population density for large elevation roughness length scales (15–20 km). This effect increases by at least a factor of 2 moving toward smaller length scales down to 1 km. The elevation effect exhibits no time trend, while the population bias effect decreases systematically in time, consistent with the improvement of reporting practices. Results are robust across time periods and the exclusion of EF1 tornadoes and are consistent with recent county-level and gridded analyses. This work highlights the need for a deeper physical understanding of how elevation heterogeneity affects tornadogenesis and also provides the foundation for a general Bayesian tornado probability model that integrates both meteorological and nonmeteorological parameters.
In recent years, with the rise in popularity of machine learning and deep learning, there is a substantial body of work that uses environmental parameters derived from near-storm proximity soundings to classify the shared characteristics of tornadic storms that occur in different regions, seasons, and times of day (Lu et al., 2015;A. K. Anderson-Frey et al., 2017;Warren et al., 2021), in addition to providing probabilistic prediction of tornadoes (Coffer et al., 2020;Shield & Houston, 2022). Most studies assume that tornadic environments are substantially different from the typical environments for a specific location at a certain time of year and time of day in which tornadic weather does not occur (hereafter referred to as baseline environments), but studies rarely quantify the differences between the two. Our goal here is to describe, fundamentally, the differences in environmental parameters between tornadic supercell environments and analogous baseline environments using simple statistical hypothesis testing. This work provides insights from a simpler perspective to add nuance to the evaluation of complex machine learning algorithms and deep learning models attempting to predict tornadoes using environmental parameters.
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