ENSO-based probabilistic forecasts of March–May U.S. tornado and hail activity

Chiara Lepore, Michael K. Tippett, John T. Allen

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

Original languageEnglish
Pages (from-to)9093-9101
Number of pages9
JournalGeophysical Research Letters
Volume44
Issue number17
DOIs
StatePublished - Sep 16 2017

Keywords

  • ENSO
  • seasonal probabilistic forecast
  • severe thunderstorms

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