Details:

Personal Author:
Lepore, Chiara ; Tippett, Michael K. ; Allen, John T. ;
Lepore, Chiara ; Tippett, Michael K. ; Allen, John T. ; Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) ;
OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) ; Less -
Description:
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.
Keywords:
[+]
El Niño Current
Forecasting
Probabilistic Number Theory
Seasonal Variations
Severe Storms
Southern Oscillation
Thunderstorms
ENSO
Severe Thunderstorms Seasonal Probabilistic Forecast

Source:
Geophys. Res. Lett., 44, 9093– 9101
DOI:
https://doi.org/10.1002/2017GL074781
Document Type:
Journal Article ;
Funding:
Grant no. NA14OAR4310185 ;
Rights Information:
CC BY-NC-ND
Main Document Checksum:
[+]
urn:sha256:682988c888c8363fe9073c4659e57e14409e4392ca930f2c5bcfddd26f177e45
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