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 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.

How Are Tornadic Supercell Soundings Significantly Different From Nearby Baseline Environments?

“…Choosing the baseline environments at the same hour as the corresponding event mitigates the impact of the diurnal variation of environmental parameters, as described in A. K. Anderson‐Frey et al. (2016) and Hua and Anderson‐Frey (2022). The ±15‐day increment is chosen so as to maintain a sufficient baseline environment data size for comparison without potentially blending in seasonal variability, as described in R. L. Thompson et al.…”

“…This same grid at the same hour will then be selected from ±15 non-tornadic days centered around the date of the tornado event; these grids constitute the baseline environment. Choosing the baseline environments at the same hour as the corresponding event mitigates the impact of the diurnal variation of environmental parameters, as described in A. K. and Hua and Anderson-Frey (2022). The ±15-day increment is chosen so as to maintain a sufficient baseline environment data size for comparison without potentially blending in seasonal variability, as described in R. L. Thompson et al (2012) andA.…”

Reconstruction of Violent Tornado Environments in Europe: High‐Resolution Dynamical Downscaling of ERA5

“…To derive convective parameters from vertical profiles of pressure, altitude, temperature, humidity, U and V from ERA5 and WRF we used the thundeR R language package (Taszarek et al., 2021). We calculate parameters that are commonly used in the operational forecasting and climatological evaluations of significant tornadoes (Brooks et al., 2003; Grams et al., 2012; Gensini et al., 2021; Hua & Anderson‐Frey, 2021; Ingrosso et al., 2020; R. L. Thompson et al., 2003, 2012, 2013; Taszarek, Allen, Púčik, et al., 2020). These include: convective available potential energy (CAPE), convective inhibition (CIN), 0–500 m mean mixing ratio (MIXR), lifted condensation level (LCL), 0–1 km vertical wind shear (S01), 0–6 km vertical wind shear (S06), 0–500 m storm‐relative helicity (SRH), and the significant tornado parameter (STP; updated formula from Coffer et al.…”

“…To derive convective parameters from vertical profiles of pressure, altitude, temperature, humidity, U and V from ERA5 and WRF we used the thundeR R language package (Taszarek et al, 2021). We calculate parameters that are commonly used in the operational forecasting and climatological evaluations of significant tornadoes (Brooks et al, 2003;Grams et al, 2012;Gensini et al, 2021;Hua & Anderson-Frey, 2021;Ingrosso et al, 2020;R. L. Thompson et al, 2003R.…”

Hybrid Index Based on 3D-Grid TPR-Tree

“…In recent years, research on the indexing structure of mobile objects in 3D space has received extensive research from scholars around the world. In particular, in a real 3D spatial area, the distribution maps of sandstorms [6], locust disasters [7], and tornadoes [8] have been studied, and their historical data has been viewed. The key prevention areas for the next period can be predicted based on the current distribution.…”