The Spectrum of US Right-Moving Supercell Environments Revealed Through Self-Organizing Maps

Supercells form in environments characterized by strong vertical wind shear and significant conditional instability. However, individual near-storm environments (NSEs) can vary considerably in terms of their temperature, humidity, and wind shear profiles. In previous studies this variability has primarily been analyzed using indices such as bulk wind shear and CAPE, and combinations of these such as the Supercell Composite Parameter. While these metrics have great value to forecasters and researchers alike, they provide only limited information about the vertical structure of the NSE. Skew-T diagrams and hodographs can reveal this structure for individual cases, but are impractical when dealing with a large sample of NSE soundings. In this study we use self-organizing maps (SOMs) to create a set of 12 skew-T diagrams and 12 hodographs which broadly capture the spectrum of right-moving supercell environments in the Contiguous US. The input to the SOMs is a sample of over 12,000 NSE soundings extracted from the Rapid Update Cycle (RUC) and Rapid Refresh (RAP) models between 2008 and 2015. By assigning soundings to their best matching SOM “node” (skew-T diagram and hodograph) we are able to examine the severe weather hazards associated with each node and their spatial and temporal variability. In addition to potential operational value to forecasters, the nodes may be used in numerical simulations to more robustly investigate the sensitivity of supercells to specific characteristics of the NSE.