Distinguishing between Hodographs of Severe Hail and Tornadoes

Cameron J. Nixon, John T. Allen

Research output: Contribution to journalArticlepeer-review

Abstract

Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quanti-ties such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, col-lective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we ex-plore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right-and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

Original languageEnglish
Pages (from-to)1761-1782
Number of pages22
JournalWeather and Forecasting
Volume37
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • Forecasting techniques
  • Mesoscale forecasting
  • Operational forecasting

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